2000 character limit reached
Quantformer: from attention to profit with a quantitative transformer trading strategy (2404.00424v2)
Published 30 Mar 2024 in q-fin.MF, cs.AI, and cs.CE
Abstract: In traditional quantitative trading practice, navigating the complicated and dynamic financial market presents a persistent challenge. Fully capturing various market variables, including long-term information, as well as essential signals that may lead to profit remains a difficult task for learning algorithms. In order to tackle this challenge, this paper introduces quantformer, an enhanced neural network architecture based on transformers, to build investment factors. By transfer learning from sentiment analysis, quantformer not only exploits its original inherent advantages in capturing long-range dependencies and modeling complex data relationships, but is also able to solve tasks with numerical inputs and accurately forecast future returns over a given period. This work collects more than 5,000,000 rolling data of 4,601 stocks in the Chinese capital market from 2010 to 2019. The results of this study demonstrated the model's superior performance in predicting stock trends compared with other 100 factor-based quantitative strategies. Notably, the model's innovative use of transformer-liked model to establish factors, in conjunction with market sentiment information, has been shown to enhance the accuracy of trading signals significantly, thereby offering promising implications for the future of quantitative trading strategies.
- Markowitz, H.: Portfolio selection. The Journal of Finance 7(1), 77–91 (1952) Sharpe [1964] Sharpe, W.F.: Capital asset prices: a theory of market equilibrium under conditions of risk. J. Finance 19, 425–442 (1964) Fama and French [1993] Fama, E.F., French, K.R.: Common risk factors in the returns on stocks and bonds. Journal of financial economics 33(1), 3–56 (1993) Fama and French [2015] Fama, E.F., French, K.R.: A five-factor asset pricing model. Journal of financial economics 116(1), 1–22 (2015) Nayak et al. [2015] Nayak, R.K., Mishra, D., Rath, A.K.: A naïve SVM-KNN based stock market trend reversal analysis for indian benchmark indices. Applied Soft Computing 35, 670–680 (2015) Feng et al. [2019] Feng, F., He, X., Wang, X., Luo, C., Liu, Y., Chua, T.-S.: Temporal relational ranking for stock prediction. ACM Transactions on Information Systems (TOIS) 37(2), 1–30 (2019) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Asness [1995] Asness, C.S.: The power of past stock returns to explain future stock returns. SSRN 2865769 (1995) Chen et al. [2020] Chen, Y., Zhao, H., Li, Z., Lu, J.: A dynamic analysis of the relationship between investor sentiment and stock market realized volatility: Evidence from china. PloS one 15(12), 0243080 (2020) PH and Rishad [2020] PH, H., Rishad, A.: An empirical examination of investor sentiment and stock market volatility: evidence from india. Financial Innovation 6(1), 1–15 (2020) Naseem et al. [2021] Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Sharpe, W.F.: Capital asset prices: a theory of market equilibrium under conditions of risk. J. Finance 19, 425–442 (1964) Fama and French [1993] Fama, E.F., French, K.R.: Common risk factors in the returns on stocks and bonds. Journal of financial economics 33(1), 3–56 (1993) Fama and French [2015] Fama, E.F., French, K.R.: A five-factor asset pricing model. Journal of financial economics 116(1), 1–22 (2015) Nayak et al. [2015] Nayak, R.K., Mishra, D., Rath, A.K.: A naïve SVM-KNN based stock market trend reversal analysis for indian benchmark indices. Applied Soft Computing 35, 670–680 (2015) Feng et al. [2019] Feng, F., He, X., Wang, X., Luo, C., Liu, Y., Chua, T.-S.: Temporal relational ranking for stock prediction. ACM Transactions on Information Systems (TOIS) 37(2), 1–30 (2019) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Asness [1995] Asness, C.S.: The power of past stock returns to explain future stock returns. SSRN 2865769 (1995) Chen et al. [2020] Chen, Y., Zhao, H., Li, Z., Lu, J.: A dynamic analysis of the relationship between investor sentiment and stock market realized volatility: Evidence from china. PloS one 15(12), 0243080 (2020) PH and Rishad [2020] PH, H., Rishad, A.: An empirical examination of investor sentiment and stock market volatility: evidence from india. Financial Innovation 6(1), 1–15 (2020) Naseem et al. [2021] Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Fama, E.F., French, K.R.: Common risk factors in the returns on stocks and bonds. Journal of financial economics 33(1), 3–56 (1993) Fama and French [2015] Fama, E.F., French, K.R.: A five-factor asset pricing model. Journal of financial economics 116(1), 1–22 (2015) Nayak et al. [2015] Nayak, R.K., Mishra, D., Rath, A.K.: A naïve SVM-KNN based stock market trend reversal analysis for indian benchmark indices. Applied Soft Computing 35, 670–680 (2015) Feng et al. [2019] Feng, F., He, X., Wang, X., Luo, C., Liu, Y., Chua, T.-S.: Temporal relational ranking for stock prediction. ACM Transactions on Information Systems (TOIS) 37(2), 1–30 (2019) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Asness [1995] Asness, C.S.: The power of past stock returns to explain future stock returns. SSRN 2865769 (1995) Chen et al. [2020] Chen, Y., Zhao, H., Li, Z., Lu, J.: A dynamic analysis of the relationship between investor sentiment and stock market realized volatility: Evidence from china. PloS one 15(12), 0243080 (2020) PH and Rishad [2020] PH, H., Rishad, A.: An empirical examination of investor sentiment and stock market volatility: evidence from india. Financial Innovation 6(1), 1–15 (2020) Naseem et al. [2021] Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Fama, E.F., French, K.R.: A five-factor asset pricing model. Journal of financial economics 116(1), 1–22 (2015) Nayak et al. [2015] Nayak, R.K., Mishra, D., Rath, A.K.: A naïve SVM-KNN based stock market trend reversal analysis for indian benchmark indices. Applied Soft Computing 35, 670–680 (2015) Feng et al. [2019] Feng, F., He, X., Wang, X., Luo, C., Liu, Y., Chua, T.-S.: Temporal relational ranking for stock prediction. ACM Transactions on Information Systems (TOIS) 37(2), 1–30 (2019) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Asness [1995] Asness, C.S.: The power of past stock returns to explain future stock returns. SSRN 2865769 (1995) Chen et al. [2020] Chen, Y., Zhao, H., Li, Z., Lu, J.: A dynamic analysis of the relationship between investor sentiment and stock market realized volatility: Evidence from china. PloS one 15(12), 0243080 (2020) PH and Rishad [2020] PH, H., Rishad, A.: An empirical examination of investor sentiment and stock market volatility: evidence from india. Financial Innovation 6(1), 1–15 (2020) Naseem et al. [2021] Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Nayak, R.K., Mishra, D., Rath, A.K.: A naïve SVM-KNN based stock market trend reversal analysis for indian benchmark indices. Applied Soft Computing 35, 670–680 (2015) Feng et al. [2019] Feng, F., He, X., Wang, X., Luo, C., Liu, Y., Chua, T.-S.: Temporal relational ranking for stock prediction. ACM Transactions on Information Systems (TOIS) 37(2), 1–30 (2019) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Asness [1995] Asness, C.S.: The power of past stock returns to explain future stock returns. SSRN 2865769 (1995) Chen et al. [2020] Chen, Y., Zhao, H., Li, Z., Lu, J.: A dynamic analysis of the relationship between investor sentiment and stock market realized volatility: Evidence from china. PloS one 15(12), 0243080 (2020) PH and Rishad [2020] PH, H., Rishad, A.: An empirical examination of investor sentiment and stock market volatility: evidence from india. Financial Innovation 6(1), 1–15 (2020) Naseem et al. [2021] Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Feng, F., He, X., Wang, X., Luo, C., Liu, Y., Chua, T.-S.: Temporal relational ranking for stock prediction. ACM Transactions on Information Systems (TOIS) 37(2), 1–30 (2019) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Asness [1995] Asness, C.S.: The power of past stock returns to explain future stock returns. SSRN 2865769 (1995) Chen et al. [2020] Chen, Y., Zhao, H., Li, Z., Lu, J.: A dynamic analysis of the relationship between investor sentiment and stock market realized volatility: Evidence from china. PloS one 15(12), 0243080 (2020) PH and Rishad [2020] PH, H., Rishad, A.: An empirical examination of investor sentiment and stock market volatility: evidence from india. Financial Innovation 6(1), 1–15 (2020) Naseem et al. [2021] Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Asness [1995] Asness, C.S.: The power of past stock returns to explain future stock returns. SSRN 2865769 (1995) Chen et al. [2020] Chen, Y., Zhao, H., Li, Z., Lu, J.: A dynamic analysis of the relationship between investor sentiment and stock market realized volatility: Evidence from china. PloS one 15(12), 0243080 (2020) PH and Rishad [2020] PH, H., Rishad, A.: An empirical examination of investor sentiment and stock market volatility: evidence from india. Financial Innovation 6(1), 1–15 (2020) Naseem et al. [2021] Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Asness, C.S.: The power of past stock returns to explain future stock returns. SSRN 2865769 (1995) Chen et al. [2020] Chen, Y., Zhao, H., Li, Z., Lu, J.: A dynamic analysis of the relationship between investor sentiment and stock market realized volatility: Evidence from china. PloS one 15(12), 0243080 (2020) PH and Rishad [2020] PH, H., Rishad, A.: An empirical examination of investor sentiment and stock market volatility: evidence from india. Financial Innovation 6(1), 1–15 (2020) Naseem et al. [2021] Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Chen, Y., Zhao, H., Li, Z., Lu, J.: A dynamic analysis of the relationship between investor sentiment and stock market realized volatility: Evidence from china. PloS one 15(12), 0243080 (2020) PH and Rishad [2020] PH, H., Rishad, A.: An empirical examination of investor sentiment and stock market volatility: evidence from india. Financial Innovation 6(1), 1–15 (2020) Naseem et al. [2021] Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) PH, H., Rishad, A.: An empirical examination of investor sentiment and stock market volatility: evidence from india. Financial Innovation 6(1), 1–15 (2020) Naseem et al. [2021] Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023)
- Sharpe, W.F.: Capital asset prices: a theory of market equilibrium under conditions of risk. J. Finance 19, 425–442 (1964) Fama and French [1993] Fama, E.F., French, K.R.: Common risk factors in the returns on stocks and bonds. Journal of financial economics 33(1), 3–56 (1993) Fama and French [2015] Fama, E.F., French, K.R.: A five-factor asset pricing model. Journal of financial economics 116(1), 1–22 (2015) Nayak et al. [2015] Nayak, R.K., Mishra, D., Rath, A.K.: A naïve SVM-KNN based stock market trend reversal analysis for indian benchmark indices. Applied Soft Computing 35, 670–680 (2015) Feng et al. [2019] Feng, F., He, X., Wang, X., Luo, C., Liu, Y., Chua, T.-S.: Temporal relational ranking for stock prediction. ACM Transactions on Information Systems (TOIS) 37(2), 1–30 (2019) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Asness [1995] Asness, C.S.: The power of past stock returns to explain future stock returns. SSRN 2865769 (1995) Chen et al. [2020] Chen, Y., Zhao, H., Li, Z., Lu, J.: A dynamic analysis of the relationship between investor sentiment and stock market realized volatility: Evidence from china. PloS one 15(12), 0243080 (2020) PH and Rishad [2020] PH, H., Rishad, A.: An empirical examination of investor sentiment and stock market volatility: evidence from india. Financial Innovation 6(1), 1–15 (2020) Naseem et al. [2021] Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Fama, E.F., French, K.R.: Common risk factors in the returns on stocks and bonds. Journal of financial economics 33(1), 3–56 (1993) Fama and French [2015] Fama, E.F., French, K.R.: A five-factor asset pricing model. Journal of financial economics 116(1), 1–22 (2015) Nayak et al. [2015] Nayak, R.K., Mishra, D., Rath, A.K.: A naïve SVM-KNN based stock market trend reversal analysis for indian benchmark indices. Applied Soft Computing 35, 670–680 (2015) Feng et al. [2019] Feng, F., He, X., Wang, X., Luo, C., Liu, Y., Chua, T.-S.: Temporal relational ranking for stock prediction. ACM Transactions on Information Systems (TOIS) 37(2), 1–30 (2019) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Asness [1995] Asness, C.S.: The power of past stock returns to explain future stock returns. SSRN 2865769 (1995) Chen et al. [2020] Chen, Y., Zhao, H., Li, Z., Lu, J.: A dynamic analysis of the relationship between investor sentiment and stock market realized volatility: Evidence from china. PloS one 15(12), 0243080 (2020) PH and Rishad [2020] PH, H., Rishad, A.: An empirical examination of investor sentiment and stock market volatility: evidence from india. Financial Innovation 6(1), 1–15 (2020) Naseem et al. [2021] Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Fama, E.F., French, K.R.: A five-factor asset pricing model. Journal of financial economics 116(1), 1–22 (2015) Nayak et al. [2015] Nayak, R.K., Mishra, D., Rath, A.K.: A naïve SVM-KNN based stock market trend reversal analysis for indian benchmark indices. Applied Soft Computing 35, 670–680 (2015) Feng et al. [2019] Feng, F., He, X., Wang, X., Luo, C., Liu, Y., Chua, T.-S.: Temporal relational ranking for stock prediction. ACM Transactions on Information Systems (TOIS) 37(2), 1–30 (2019) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Asness [1995] Asness, C.S.: The power of past stock returns to explain future stock returns. SSRN 2865769 (1995) Chen et al. [2020] Chen, Y., Zhao, H., Li, Z., Lu, J.: A dynamic analysis of the relationship between investor sentiment and stock market realized volatility: Evidence from china. PloS one 15(12), 0243080 (2020) PH and Rishad [2020] PH, H., Rishad, A.: An empirical examination of investor sentiment and stock market volatility: evidence from india. Financial Innovation 6(1), 1–15 (2020) Naseem et al. [2021] Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Nayak, R.K., Mishra, D., Rath, A.K.: A naïve SVM-KNN based stock market trend reversal analysis for indian benchmark indices. Applied Soft Computing 35, 670–680 (2015) Feng et al. [2019] Feng, F., He, X., Wang, X., Luo, C., Liu, Y., Chua, T.-S.: Temporal relational ranking for stock prediction. ACM Transactions on Information Systems (TOIS) 37(2), 1–30 (2019) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Asness [1995] Asness, C.S.: The power of past stock returns to explain future stock returns. SSRN 2865769 (1995) Chen et al. [2020] Chen, Y., Zhao, H., Li, Z., Lu, J.: A dynamic analysis of the relationship between investor sentiment and stock market realized volatility: Evidence from china. PloS one 15(12), 0243080 (2020) PH and Rishad [2020] PH, H., Rishad, A.: An empirical examination of investor sentiment and stock market volatility: evidence from india. Financial Innovation 6(1), 1–15 (2020) Naseem et al. [2021] Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Feng, F., He, X., Wang, X., Luo, C., Liu, Y., Chua, T.-S.: Temporal relational ranking for stock prediction. ACM Transactions on Information Systems (TOIS) 37(2), 1–30 (2019) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Asness [1995] Asness, C.S.: The power of past stock returns to explain future stock returns. SSRN 2865769 (1995) Chen et al. [2020] Chen, Y., Zhao, H., Li, Z., Lu, J.: A dynamic analysis of the relationship between investor sentiment and stock market realized volatility: Evidence from china. PloS one 15(12), 0243080 (2020) PH and Rishad [2020] PH, H., Rishad, A.: An empirical examination of investor sentiment and stock market volatility: evidence from india. Financial Innovation 6(1), 1–15 (2020) Naseem et al. [2021] Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Asness [1995] Asness, C.S.: The power of past stock returns to explain future stock returns. SSRN 2865769 (1995) Chen et al. [2020] Chen, Y., Zhao, H., Li, Z., Lu, J.: A dynamic analysis of the relationship between investor sentiment and stock market realized volatility: Evidence from china. PloS one 15(12), 0243080 (2020) PH and Rishad [2020] PH, H., Rishad, A.: An empirical examination of investor sentiment and stock market volatility: evidence from india. Financial Innovation 6(1), 1–15 (2020) Naseem et al. [2021] Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Asness, C.S.: The power of past stock returns to explain future stock returns. SSRN 2865769 (1995) Chen et al. [2020] Chen, Y., Zhao, H., Li, Z., Lu, J.: A dynamic analysis of the relationship between investor sentiment and stock market realized volatility: Evidence from china. PloS one 15(12), 0243080 (2020) PH and Rishad [2020] PH, H., Rishad, A.: An empirical examination of investor sentiment and stock market volatility: evidence from india. Financial Innovation 6(1), 1–15 (2020) Naseem et al. [2021] Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Chen, Y., Zhao, H., Li, Z., Lu, J.: A dynamic analysis of the relationship between investor sentiment and stock market realized volatility: Evidence from china. PloS one 15(12), 0243080 (2020) PH and Rishad [2020] PH, H., Rishad, A.: An empirical examination of investor sentiment and stock market volatility: evidence from india. Financial Innovation 6(1), 1–15 (2020) Naseem et al. [2021] Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) PH, H., Rishad, A.: An empirical examination of investor sentiment and stock market volatility: evidence from india. Financial Innovation 6(1), 1–15 (2020) Naseem et al. [2021] Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023)
- Fama, E.F., French, K.R.: Common risk factors in the returns on stocks and bonds. Journal of financial economics 33(1), 3–56 (1993) Fama and French [2015] Fama, E.F., French, K.R.: A five-factor asset pricing model. Journal of financial economics 116(1), 1–22 (2015) Nayak et al. [2015] Nayak, R.K., Mishra, D., Rath, A.K.: A naïve SVM-KNN based stock market trend reversal analysis for indian benchmark indices. Applied Soft Computing 35, 670–680 (2015) Feng et al. [2019] Feng, F., He, X., Wang, X., Luo, C., Liu, Y., Chua, T.-S.: Temporal relational ranking for stock prediction. ACM Transactions on Information Systems (TOIS) 37(2), 1–30 (2019) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Asness [1995] Asness, C.S.: The power of past stock returns to explain future stock returns. SSRN 2865769 (1995) Chen et al. [2020] Chen, Y., Zhao, H., Li, Z., Lu, J.: A dynamic analysis of the relationship between investor sentiment and stock market realized volatility: Evidence from china. PloS one 15(12), 0243080 (2020) PH and Rishad [2020] PH, H., Rishad, A.: An empirical examination of investor sentiment and stock market volatility: evidence from india. Financial Innovation 6(1), 1–15 (2020) Naseem et al. [2021] Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Fama, E.F., French, K.R.: A five-factor asset pricing model. Journal of financial economics 116(1), 1–22 (2015) Nayak et al. [2015] Nayak, R.K., Mishra, D., Rath, A.K.: A naïve SVM-KNN based stock market trend reversal analysis for indian benchmark indices. Applied Soft Computing 35, 670–680 (2015) Feng et al. [2019] Feng, F., He, X., Wang, X., Luo, C., Liu, Y., Chua, T.-S.: Temporal relational ranking for stock prediction. ACM Transactions on Information Systems (TOIS) 37(2), 1–30 (2019) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Asness [1995] Asness, C.S.: The power of past stock returns to explain future stock returns. SSRN 2865769 (1995) Chen et al. [2020] Chen, Y., Zhao, H., Li, Z., Lu, J.: A dynamic analysis of the relationship between investor sentiment and stock market realized volatility: Evidence from china. PloS one 15(12), 0243080 (2020) PH and Rishad [2020] PH, H., Rishad, A.: An empirical examination of investor sentiment and stock market volatility: evidence from india. Financial Innovation 6(1), 1–15 (2020) Naseem et al. [2021] Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Nayak, R.K., Mishra, D., Rath, A.K.: A naïve SVM-KNN based stock market trend reversal analysis for indian benchmark indices. Applied Soft Computing 35, 670–680 (2015) Feng et al. [2019] Feng, F., He, X., Wang, X., Luo, C., Liu, Y., Chua, T.-S.: Temporal relational ranking for stock prediction. ACM Transactions on Information Systems (TOIS) 37(2), 1–30 (2019) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Asness [1995] Asness, C.S.: The power of past stock returns to explain future stock returns. SSRN 2865769 (1995) Chen et al. [2020] Chen, Y., Zhao, H., Li, Z., Lu, J.: A dynamic analysis of the relationship between investor sentiment and stock market realized volatility: Evidence from china. PloS one 15(12), 0243080 (2020) PH and Rishad [2020] PH, H., Rishad, A.: An empirical examination of investor sentiment and stock market volatility: evidence from india. Financial Innovation 6(1), 1–15 (2020) Naseem et al. [2021] Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Feng, F., He, X., Wang, X., Luo, C., Liu, Y., Chua, T.-S.: Temporal relational ranking for stock prediction. ACM Transactions on Information Systems (TOIS) 37(2), 1–30 (2019) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Asness [1995] Asness, C.S.: The power of past stock returns to explain future stock returns. SSRN 2865769 (1995) Chen et al. [2020] Chen, Y., Zhao, H., Li, Z., Lu, J.: A dynamic analysis of the relationship between investor sentiment and stock market realized volatility: Evidence from china. PloS one 15(12), 0243080 (2020) PH and Rishad [2020] PH, H., Rishad, A.: An empirical examination of investor sentiment and stock market volatility: evidence from india. Financial Innovation 6(1), 1–15 (2020) Naseem et al. [2021] Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Asness [1995] Asness, C.S.: The power of past stock returns to explain future stock returns. SSRN 2865769 (1995) Chen et al. [2020] Chen, Y., Zhao, H., Li, Z., Lu, J.: A dynamic analysis of the relationship between investor sentiment and stock market realized volatility: Evidence from china. PloS one 15(12), 0243080 (2020) PH and Rishad [2020] PH, H., Rishad, A.: An empirical examination of investor sentiment and stock market volatility: evidence from india. Financial Innovation 6(1), 1–15 (2020) Naseem et al. [2021] Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Asness, C.S.: The power of past stock returns to explain future stock returns. SSRN 2865769 (1995) Chen et al. [2020] Chen, Y., Zhao, H., Li, Z., Lu, J.: A dynamic analysis of the relationship between investor sentiment and stock market realized volatility: Evidence from china. PloS one 15(12), 0243080 (2020) PH and Rishad [2020] PH, H., Rishad, A.: An empirical examination of investor sentiment and stock market volatility: evidence from india. Financial Innovation 6(1), 1–15 (2020) Naseem et al. [2021] Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Chen, Y., Zhao, H., Li, Z., Lu, J.: A dynamic analysis of the relationship between investor sentiment and stock market realized volatility: Evidence from china. PloS one 15(12), 0243080 (2020) PH and Rishad [2020] PH, H., Rishad, A.: An empirical examination of investor sentiment and stock market volatility: evidence from india. Financial Innovation 6(1), 1–15 (2020) Naseem et al. [2021] Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) PH, H., Rishad, A.: An empirical examination of investor sentiment and stock market volatility: evidence from india. Financial Innovation 6(1), 1–15 (2020) Naseem et al. [2021] Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023)
- Fama, E.F., French, K.R.: A five-factor asset pricing model. Journal of financial economics 116(1), 1–22 (2015) Nayak et al. [2015] Nayak, R.K., Mishra, D., Rath, A.K.: A naïve SVM-KNN based stock market trend reversal analysis for indian benchmark indices. Applied Soft Computing 35, 670–680 (2015) Feng et al. [2019] Feng, F., He, X., Wang, X., Luo, C., Liu, Y., Chua, T.-S.: Temporal relational ranking for stock prediction. ACM Transactions on Information Systems (TOIS) 37(2), 1–30 (2019) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Asness [1995] Asness, C.S.: The power of past stock returns to explain future stock returns. SSRN 2865769 (1995) Chen et al. [2020] Chen, Y., Zhao, H., Li, Z., Lu, J.: A dynamic analysis of the relationship between investor sentiment and stock market realized volatility: Evidence from china. PloS one 15(12), 0243080 (2020) PH and Rishad [2020] PH, H., Rishad, A.: An empirical examination of investor sentiment and stock market volatility: evidence from india. Financial Innovation 6(1), 1–15 (2020) Naseem et al. [2021] Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Nayak, R.K., Mishra, D., Rath, A.K.: A naïve SVM-KNN based stock market trend reversal analysis for indian benchmark indices. Applied Soft Computing 35, 670–680 (2015) Feng et al. [2019] Feng, F., He, X., Wang, X., Luo, C., Liu, Y., Chua, T.-S.: Temporal relational ranking for stock prediction. ACM Transactions on Information Systems (TOIS) 37(2), 1–30 (2019) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Asness [1995] Asness, C.S.: The power of past stock returns to explain future stock returns. SSRN 2865769 (1995) Chen et al. [2020] Chen, Y., Zhao, H., Li, Z., Lu, J.: A dynamic analysis of the relationship between investor sentiment and stock market realized volatility: Evidence from china. PloS one 15(12), 0243080 (2020) PH and Rishad [2020] PH, H., Rishad, A.: An empirical examination of investor sentiment and stock market volatility: evidence from india. Financial Innovation 6(1), 1–15 (2020) Naseem et al. [2021] Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Feng, F., He, X., Wang, X., Luo, C., Liu, Y., Chua, T.-S.: Temporal relational ranking for stock prediction. ACM Transactions on Information Systems (TOIS) 37(2), 1–30 (2019) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Asness [1995] Asness, C.S.: The power of past stock returns to explain future stock returns. SSRN 2865769 (1995) Chen et al. [2020] Chen, Y., Zhao, H., Li, Z., Lu, J.: A dynamic analysis of the relationship between investor sentiment and stock market realized volatility: Evidence from china. PloS one 15(12), 0243080 (2020) PH and Rishad [2020] PH, H., Rishad, A.: An empirical examination of investor sentiment and stock market volatility: evidence from india. Financial Innovation 6(1), 1–15 (2020) Naseem et al. [2021] Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Asness [1995] Asness, C.S.: The power of past stock returns to explain future stock returns. SSRN 2865769 (1995) Chen et al. [2020] Chen, Y., Zhao, H., Li, Z., Lu, J.: A dynamic analysis of the relationship between investor sentiment and stock market realized volatility: Evidence from china. PloS one 15(12), 0243080 (2020) PH and Rishad [2020] PH, H., Rishad, A.: An empirical examination of investor sentiment and stock market volatility: evidence from india. Financial Innovation 6(1), 1–15 (2020) Naseem et al. [2021] Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Asness, C.S.: The power of past stock returns to explain future stock returns. SSRN 2865769 (1995) Chen et al. [2020] Chen, Y., Zhao, H., Li, Z., Lu, J.: A dynamic analysis of the relationship between investor sentiment and stock market realized volatility: Evidence from china. PloS one 15(12), 0243080 (2020) PH and Rishad [2020] PH, H., Rishad, A.: An empirical examination of investor sentiment and stock market volatility: evidence from india. Financial Innovation 6(1), 1–15 (2020) Naseem et al. [2021] Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Chen, Y., Zhao, H., Li, Z., Lu, J.: A dynamic analysis of the relationship between investor sentiment and stock market realized volatility: Evidence from china. PloS one 15(12), 0243080 (2020) PH and Rishad [2020] PH, H., Rishad, A.: An empirical examination of investor sentiment and stock market volatility: evidence from india. Financial Innovation 6(1), 1–15 (2020) Naseem et al. [2021] Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) PH, H., Rishad, A.: An empirical examination of investor sentiment and stock market volatility: evidence from india. Financial Innovation 6(1), 1–15 (2020) Naseem et al. [2021] Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023)
- Nayak, R.K., Mishra, D., Rath, A.K.: A naïve SVM-KNN based stock market trend reversal analysis for indian benchmark indices. Applied Soft Computing 35, 670–680 (2015) Feng et al. [2019] Feng, F., He, X., Wang, X., Luo, C., Liu, Y., Chua, T.-S.: Temporal relational ranking for stock prediction. ACM Transactions on Information Systems (TOIS) 37(2), 1–30 (2019) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Asness [1995] Asness, C.S.: The power of past stock returns to explain future stock returns. SSRN 2865769 (1995) Chen et al. [2020] Chen, Y., Zhao, H., Li, Z., Lu, J.: A dynamic analysis of the relationship between investor sentiment and stock market realized volatility: Evidence from china. PloS one 15(12), 0243080 (2020) PH and Rishad [2020] PH, H., Rishad, A.: An empirical examination of investor sentiment and stock market volatility: evidence from india. Financial Innovation 6(1), 1–15 (2020) Naseem et al. [2021] Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Feng, F., He, X., Wang, X., Luo, C., Liu, Y., Chua, T.-S.: Temporal relational ranking for stock prediction. ACM Transactions on Information Systems (TOIS) 37(2), 1–30 (2019) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Asness [1995] Asness, C.S.: The power of past stock returns to explain future stock returns. SSRN 2865769 (1995) Chen et al. [2020] Chen, Y., Zhao, H., Li, Z., Lu, J.: A dynamic analysis of the relationship between investor sentiment and stock market realized volatility: Evidence from china. PloS one 15(12), 0243080 (2020) PH and Rishad [2020] PH, H., Rishad, A.: An empirical examination of investor sentiment and stock market volatility: evidence from india. Financial Innovation 6(1), 1–15 (2020) Naseem et al. [2021] Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Asness [1995] Asness, C.S.: The power of past stock returns to explain future stock returns. SSRN 2865769 (1995) Chen et al. [2020] Chen, Y., Zhao, H., Li, Z., Lu, J.: A dynamic analysis of the relationship between investor sentiment and stock market realized volatility: Evidence from china. PloS one 15(12), 0243080 (2020) PH and Rishad [2020] PH, H., Rishad, A.: An empirical examination of investor sentiment and stock market volatility: evidence from india. Financial Innovation 6(1), 1–15 (2020) Naseem et al. [2021] Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Asness, C.S.: The power of past stock returns to explain future stock returns. SSRN 2865769 (1995) Chen et al. [2020] Chen, Y., Zhao, H., Li, Z., Lu, J.: A dynamic analysis of the relationship between investor sentiment and stock market realized volatility: Evidence from china. PloS one 15(12), 0243080 (2020) PH and Rishad [2020] PH, H., Rishad, A.: An empirical examination of investor sentiment and stock market volatility: evidence from india. Financial Innovation 6(1), 1–15 (2020) Naseem et al. [2021] Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Chen, Y., Zhao, H., Li, Z., Lu, J.: A dynamic analysis of the relationship between investor sentiment and stock market realized volatility: Evidence from china. PloS one 15(12), 0243080 (2020) PH and Rishad [2020] PH, H., Rishad, A.: An empirical examination of investor sentiment and stock market volatility: evidence from india. Financial Innovation 6(1), 1–15 (2020) Naseem et al. [2021] Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) PH, H., Rishad, A.: An empirical examination of investor sentiment and stock market volatility: evidence from india. Financial Innovation 6(1), 1–15 (2020) Naseem et al. [2021] Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023)
- Feng, F., He, X., Wang, X., Luo, C., Liu, Y., Chua, T.-S.: Temporal relational ranking for stock prediction. ACM Transactions on Information Systems (TOIS) 37(2), 1–30 (2019) Gorishniy et al. [2022] Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Asness [1995] Asness, C.S.: The power of past stock returns to explain future stock returns. SSRN 2865769 (1995) Chen et al. [2020] Chen, Y., Zhao, H., Li, Z., Lu, J.: A dynamic analysis of the relationship between investor sentiment and stock market realized volatility: Evidence from china. PloS one 15(12), 0243080 (2020) PH and Rishad [2020] PH, H., Rishad, A.: An empirical examination of investor sentiment and stock market volatility: evidence from india. Financial Innovation 6(1), 1–15 (2020) Naseem et al. [2021] Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Asness [1995] Asness, C.S.: The power of past stock returns to explain future stock returns. SSRN 2865769 (1995) Chen et al. [2020] Chen, Y., Zhao, H., Li, Z., Lu, J.: A dynamic analysis of the relationship between investor sentiment and stock market realized volatility: Evidence from china. PloS one 15(12), 0243080 (2020) PH and Rishad [2020] PH, H., Rishad, A.: An empirical examination of investor sentiment and stock market volatility: evidence from india. Financial Innovation 6(1), 1–15 (2020) Naseem et al. [2021] Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Asness, C.S.: The power of past stock returns to explain future stock returns. SSRN 2865769 (1995) Chen et al. [2020] Chen, Y., Zhao, H., Li, Z., Lu, J.: A dynamic analysis of the relationship between investor sentiment and stock market realized volatility: Evidence from china. PloS one 15(12), 0243080 (2020) PH and Rishad [2020] PH, H., Rishad, A.: An empirical examination of investor sentiment and stock market volatility: evidence from india. Financial Innovation 6(1), 1–15 (2020) Naseem et al. [2021] Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Chen, Y., Zhao, H., Li, Z., Lu, J.: A dynamic analysis of the relationship between investor sentiment and stock market realized volatility: Evidence from china. PloS one 15(12), 0243080 (2020) PH and Rishad [2020] PH, H., Rishad, A.: An empirical examination of investor sentiment and stock market volatility: evidence from india. Financial Innovation 6(1), 1–15 (2020) Naseem et al. [2021] Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) PH, H., Rishad, A.: An empirical examination of investor sentiment and stock market volatility: evidence from india. Financial Innovation 6(1), 1–15 (2020) Naseem et al. [2021] Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023)
- Gorishniy, Y., Rubachev, I., Babenko, A.: On embeddings for numerical features in tabular deep learning. Advances in Neural Information Processing Systems 35, 24991–25004 (2022) Asness [1995] Asness, C.S.: The power of past stock returns to explain future stock returns. SSRN 2865769 (1995) Chen et al. [2020] Chen, Y., Zhao, H., Li, Z., Lu, J.: A dynamic analysis of the relationship between investor sentiment and stock market realized volatility: Evidence from china. PloS one 15(12), 0243080 (2020) PH and Rishad [2020] PH, H., Rishad, A.: An empirical examination of investor sentiment and stock market volatility: evidence from india. Financial Innovation 6(1), 1–15 (2020) Naseem et al. [2021] Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Asness, C.S.: The power of past stock returns to explain future stock returns. SSRN 2865769 (1995) Chen et al. [2020] Chen, Y., Zhao, H., Li, Z., Lu, J.: A dynamic analysis of the relationship between investor sentiment and stock market realized volatility: Evidence from china. PloS one 15(12), 0243080 (2020) PH and Rishad [2020] PH, H., Rishad, A.: An empirical examination of investor sentiment and stock market volatility: evidence from india. Financial Innovation 6(1), 1–15 (2020) Naseem et al. [2021] Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Chen, Y., Zhao, H., Li, Z., Lu, J.: A dynamic analysis of the relationship between investor sentiment and stock market realized volatility: Evidence from china. PloS one 15(12), 0243080 (2020) PH and Rishad [2020] PH, H., Rishad, A.: An empirical examination of investor sentiment and stock market volatility: evidence from india. Financial Innovation 6(1), 1–15 (2020) Naseem et al. [2021] Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) PH, H., Rishad, A.: An empirical examination of investor sentiment and stock market volatility: evidence from india. Financial Innovation 6(1), 1–15 (2020) Naseem et al. [2021] Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023)
- Asness, C.S.: The power of past stock returns to explain future stock returns. SSRN 2865769 (1995) Chen et al. [2020] Chen, Y., Zhao, H., Li, Z., Lu, J.: A dynamic analysis of the relationship between investor sentiment and stock market realized volatility: Evidence from china. PloS one 15(12), 0243080 (2020) PH and Rishad [2020] PH, H., Rishad, A.: An empirical examination of investor sentiment and stock market volatility: evidence from india. Financial Innovation 6(1), 1–15 (2020) Naseem et al. [2021] Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Chen, Y., Zhao, H., Li, Z., Lu, J.: A dynamic analysis of the relationship between investor sentiment and stock market realized volatility: Evidence from china. PloS one 15(12), 0243080 (2020) PH and Rishad [2020] PH, H., Rishad, A.: An empirical examination of investor sentiment and stock market volatility: evidence from india. Financial Innovation 6(1), 1–15 (2020) Naseem et al. [2021] Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) PH, H., Rishad, A.: An empirical examination of investor sentiment and stock market volatility: evidence from india. Financial Innovation 6(1), 1–15 (2020) Naseem et al. [2021] Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023)
- Chen, Y., Zhao, H., Li, Z., Lu, J.: A dynamic analysis of the relationship between investor sentiment and stock market realized volatility: Evidence from china. PloS one 15(12), 0243080 (2020) PH and Rishad [2020] PH, H., Rishad, A.: An empirical examination of investor sentiment and stock market volatility: evidence from india. Financial Innovation 6(1), 1–15 (2020) Naseem et al. [2021] Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) PH, H., Rishad, A.: An empirical examination of investor sentiment and stock market volatility: evidence from india. Financial Innovation 6(1), 1–15 (2020) Naseem et al. [2021] Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023)
- PH, H., Rishad, A.: An empirical examination of investor sentiment and stock market volatility: evidence from india. Financial Innovation 6(1), 1–15 (2020) Naseem et al. [2021] Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023)
- Naseem, S., Mohsin, M., Hui, W., Liyan, G., Penglai, K.: The investor psychology and stock market behavior during the initial era of covid-19: a study of china, japan, and the united states. Frontiers in Psychology 12, 626934 (2021) Kim [2003] Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023)
- Kim, K.-j.: Financial time series forecasting using support vector machines. Neurocomputing 55(1-2), 307–319 (2003) Huang et al. [2005] Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023)
- Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & operations research 32(10), 2513–2522 (2005) Cavalcante et al. [2016] Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023)
- Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications 55, 194–211 (2016) Huck [2009] Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023)
- Huck, N.: Pairs selection and outranking: An application to the s&p 100 index. European Journal of Operational Research 196(2), 819–825 (2009) Kercheval and Zhang [2015] Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023)
- Kercheval, A.N., Zhang, Y.: Modelling high-frequency limit order book dynamics with support vector machines. Quantitative Finance 15(8), 1315–1329 (2015) Hochreiter and Schmidhuber [1997] Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023)
- Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735–1780 (1997) Bao et al. [2017] Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023)
- Bao, W., Yue, J., Rao, Y.: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one 12(7), 0180944 (2017) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023)
- Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018) Sezer and Ozbayoglu [2018] Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023)
- Sezer, O.B., Ozbayoglu, A.M.: Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing 70, 525–538 (2018) Zhang et al. [2017] Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023)
- Zhang, L., Aggarwal, C., Qi, G.-J.: Stock price prediction via discovering multi-frequency trading patterns. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2141–2149 (2017) Chung et al. [2014] Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023)
- Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning (2014) Fischer and Krauss [2018] Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023)
- Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European journal of operational research 270(2), 654–669 (2018) Pak et al. [2018] Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023)
- Pak, U., Kim, C., Ryu, U., Sok, K., Pak, S.: A hybrid model based on convolutional neural networks and long short-term memory for ozone concentration prediction. Air Quality, Atmosphere & Health 11, 883–895 (2018) O’Shea and Nash [2015] O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023)
- O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv:1511.08458 (2015) Vaswani et al. [2017] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023)
- Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp. 6000–6010 (2017) Radford et al. [2019] Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023)
- Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. figshare https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Assessed: 2019-02-07 (2019) Devlin et al. [2018] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023)
- Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018) Brown et al. [2020] Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023)
- Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: language models are few-shot learners. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS’20 (2020) Araci [2019] Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023)
- Araci, D.: FinBERT: financial sentiment analysis with pre-trained aanguage models. arXiv:1908.10063 (2019) Yang et al. [2020] Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023)
- Yang, Y., Uy, M., Huang, A.: FinBERT: a pretrained language model for financial communications. arXiv:2006.08097 (2020) Wu et al. [2023] Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023)
- Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., Mann, G.: BloombergGPT: a large language model for finance. arXiv:2303.17564 (2023) Ding et al. [2020] Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023)
- Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020) Zhou et al. [2021] Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023)
- Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., Zhang, W.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021) Zeng et al. [2023] Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023)
- Zeng, Z., Kaur, R., Siddagangappa, S., Rahimi, S., Balch, T., Veloso, M.: Financial time series forecasting using CNN and Transformer. arXiv:2304.04912 (2023) Xu et al. [2021] Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023)
- Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4647–4653 (2021) Wu et al. [2020] Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023)
- Wu, C., Wu, F., Huang, Y.: Da-transformer: distance-aware transformer. arXiv:2010.06925 (2020) Klambauer et al. [2017] Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023)
- Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Advances in neural information processing systems 30 (2017) Diamond [1971] Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023)
- Diamond, P.A.: A model of price adjustment. Journal of economic theory 3(2), 156–168 (1971) Wei et al. [2022] Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023)
- Wei, J., Xu, Q., He, C.: Deep learning of predicting closing price through historical adjustment closing price. Procedia Computer Science 202, 379–384 (2022) Paszke et al. [2019] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023)
- Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019) Bergstra and Bengio [2012] Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023)
- Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13(2) (2012) Kingma and Ba [2015] Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023)
- Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015) OpenAI [2023] OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023)
- OpenAI: GPT-4 Technical Report. arXiv:2303.08774 (2023) Touvron et al. [2023] Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023) Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023)
- Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al.: Llama 2: Open foundation and fine-tuned chat models (2023)