Natural Language Processing and Multimodal Stock Price Prediction (2401.01487v1)
Abstract: In the realm of financial decision-making, predicting stock prices is pivotal. Artificial intelligence techniques such as long short-term memory networks (LSTMs), support-vector machines (SVMs), and NLP models are commonly employed to predict said prices. This paper utilizes stock percentage change as training data, in contrast to the traditional use of raw currency values, with a focus on analyzing publicly released news articles. The choice of percentage change aims to provide models with context regarding the significance of price fluctuations and overall price change impact on a given stock. The study employs specialized BERT natural language processing models to predict stock price trends, with a particular emphasis on various data modalities. The results showcase the capabilities of such strategies with a small natural language processing model to accurately predict overall stock trends, and highlight the effectiveness of certain data features and sector-specific data.
- M. A. Istiake Sunny, M. M. S. Maswood, and A. G. Alharbi, “Deep learning-based stock price prediction using lstm and bi-directional lstm model,” in 2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES), 2020, pp. 87–92.
- H. Li, Y. Shen, and Y. Zhu, “Stock price prediction using attention-based multi-input lstm,” in Proceedings of The 10th Asian Conference on Machine Learning, ser. Proceedings of Machine Learning Research, J. Zhu and I. Takeuchi, Eds., vol. 95. PMLR, 14–16 Nov 2018, pp. 454–469. [Online]. Available: https://proceedings.mlr.press/v95/li18c.html
- K. J, H. E, M. S. Jacob, and D. R, “Stock price prediction based on lstm deep learning model,” in 2021 International Conference on System, Computation, Automation and Networking (ICSCAN), 2021, pp. 1–4.
- K. Chen, Y. Zhou, and F. Dai, “A lstm-based method for stock returns prediction: A case study of china stock market,” in 2015 IEEE International Conference on Big Data (Big Data), 2015, pp. 2823–2824.
- H. Ince and T. B. Trafalis, “Short term forecasting with support vector machines and application to stock price prediction,” International Journal of General Systems, vol. 37, no. 6, pp. 677–687, 2008. [Online]. Available: https://doi.org/10.1080/03081070601068595
- Y. Lin, H. Guo, and J. Hu, “An svm-based approach for stock market trend prediction,” in The 2013 International Joint Conference on Neural Networks (IJCNN), 2013, pp. 1–7.
- Z. Hu, J. Zhu, and K. Tse, “Stocks market prediction using support vector machine,” in 2013 6th International Conference on Information Management, Innovation Management and Industrial Engineering, vol. 2, 2013, pp. 115–118.
- J. Kim, J. Seo, M. Lee, and J. Seok, “Stock price prediction through the sentimental analysis of news articles,” in 2019 Eleventh International Conference on Ubiquitous and Future Networks (ICUFN), 2019, pp. 700–702.
- F. Colasanto, L. Grilli, D. Santoro, and G. Villani, “Albertino for stock price prediction: a gibbs sampling approach,” Information Sciences, vol. 597, pp. 341–357, 2022. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S002002552200264X
- S. Mohan, S. Mullapudi, S. Sammeta, P. Vijayvergia, and D. C. Anastasiu, “Stock price prediction using news sentiment analysis,” in 2019 IEEE Fifth International Conference on Big Data Computing Service and Applications (BigDataService), 2019, pp. 205–208.
- H. Huang and T. Zhao, “Stock market prediction by daily news via natural language processing and machine learning,” in 2021 International Conference on Computer, Blockchain and Financial Development (CBFD), 2021, pp. 190–196.
- L. Sayavong, Z. Wu, and S. Chalita, “Research on stock price prediction method based on convolutional neural network,” in 2019 International Conference on Virtual Reality and Intelligent Systems (ICVRIS), 2019, pp. 173–176.
- P. Bhargava, A. Drozd, and A. Rogers, “Generalization in nli: Ways (not) to go beyond simple heuristics,” 2021.
- I. Turc, M. Chang, K. Lee, and K. Toutanova, “Well-read students learn better: The impact of student initialization on knowledge distillation,” CoRR, vol. abs/1908.08962, 2019. [Online]. Available: http://arxiv.org/abs/1908.08962
- Y. Guo, “Stock price prediction based on lstm neural network: the effectiveness of news sentiment analysis,” in 2020 2nd International Conference on Economic Management and Model Engineering (ICEMME), 2020, pp. 1018–1024.
- Kevin Taylor (1 paper)
- Jerry Ng (5 papers)