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DGDNN: Decoupled Graph Diffusion Neural Network for Stock Movement Prediction (2401.01846v1)

Published 3 Jan 2024 in cs.LG and cs.NE

Abstract: Forecasting future stock trends remains challenging for academia and industry due to stochastic inter-stock dynamics and hierarchical intra-stock dynamics influencing stock prices. In recent years, graph neural networks have achieved remarkable performance in this problem by formulating multiple stocks as graph-structured data. However, most of these approaches rely on artificially defined factors to construct static stock graphs, which fail to capture the intrinsic interdependencies between stocks that rapidly evolve. In addition, these methods often ignore the hierarchical features of the stocks and lose distinctive information within. In this work, we propose a novel graph learning approach implemented without expert knowledge to address these issues. First, our approach automatically constructs dynamic stock graphs by entropy-driven edge generation from a signal processing perspective. Then, we further learn task-optimal dependencies between stocks via a generalized graph diffusion process on constructed stock graphs. Last, a decoupled representation learning scheme is adopted to capture distinctive hierarchical intra-stock features. Experimental results demonstrate substantial improvements over state-of-the-art baselines on real-world datasets. Moreover, the ablation study and sensitivity study further illustrate the effectiveness of the proposed method in modeling the time-evolving inter-stock and intra-stock dynamics.

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References (60)
  1. Stock market volatility and learning. The Journal of finance, 71(1):33–82.
  2. Deep learning for stock prediction using numerical and textual information. In 2016 IEEE/ACIS 15th International Conference on Computer and Information Science, pages 1–6. IEEE.
  3. Financial influences and scale-free networks. In Computational Science–ICCS 2006: 6th International Conference, Reading, UK, Proceedings, pages 16–23. Springer.
  4. A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PloS one, 12(7):e0180944.
  5. What will make misinformation spread: An xai perspective. In World Conference on Explainable Artificial Intelligence, pages 321–337. Springer.
  6. Measuring and relieving the over-smoothing problem for graph neural networks from the topological view. In Proceedings of the AAAI conference on artificial intelligence, volume 34, pages 3438–3445.
  7. Memory-based message passing: Decoupling the message for propagation from discrimination. In ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing, pages 4033–4037. IEEE.
  8. Herd behavior and aggregate fluctuations in financial markets. Macroeconomic dynamics, 4(2):170–196.
  9. Information theory and statistics: A tutorial. Foundations and Trends® in Communications and Information Theory, 1(4):417–528.
  10. Knowledge-driven stock trend prediction and explanation via temporal convolutional network. In Companion Proceedings of The 2019 World Wide Web Conference, pages 678–685.
  11. Noisy stock prices and corporate investment. The Review of Financial Studies, 32(7):2625–2672.
  12. Hierarchical multi-scale gaussian transformer for stock movement prediction. In Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pages 4640–4646.
  13. Enhancing stock movement prediction with adversarial training. In IJCAI.
  14. Temporal relational ranking for stock prediction. ACM Transactions on Information Systems, 37(2):1–30.
  15. Relation-aware dynamic attributed graph attention network for stocks recommendation. Pattern Recognition, 121:108119.
  16. Time and frequency dynamics of connectedness between renewable energy stocks and crude oil prices. Energy Economics, 76:1–20.
  17. Exploring structure-adaptive graph learning for robust semi-supervised classification. In 2020 ieee international conference on multimedia and expo, pages 1–6. IEEE.
  18. Tackling over-smoothing for general graph convolutional networks. arXiv preprint arXiv:2008.09864.
  19. Efficient integration of multi-order dynamics and internal dynamics in stock movement prediction. In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, pages 850–858.
  20. Jaynes, E. T. (1957). Information theory and statistical mechanics. Physical review, 106(4):620.
  21. Semi-supervised learning with graph learning-convolutional networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11313–11320.
  22. A comparison of volatility and bid–ask spread for nasdaq and nyse after decimalization. Applied Economics, 43(10):1227–1239.
  23. Jiang, W. (2021). Applications of deep learning in stock market prediction: recent progress. Expert Systems with Applications, 184:115537.
  24. Graph structure learning for robust graph neural networks. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, pages 66–74.
  25. Hats: A hierarchical graph attention network for stock movement prediction. arXiv preprint arXiv:1908.07999.
  26. Diffusion improves graph learning. In Proceedings of the 33rd International Conference on Neural Information Processing Systems, pages 13366–13378.
  27. Graph neural networks for decentralized multi-robot path planning. In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 11785–11792. IEEE.
  28. Deeper insights into graph convolutional networks for semi-supervised learning. In Proceedings of the AAAI conference on artificial intelligence, volume 32.
  29. A multimodal event-driven lstm model for stock prediction using online news. IEEE Transactions on Knowledge and Data Engineering, 33(10):3323–3337.
  30. Modeling the stock relation with graph network for overnight stock movement prediction. In Proceedings of the twenty-ninth international conference on international joint conferences on artificial intelligence, pages 4541–4547.
  31. Risk prediction and evaluation of transnational transmission of financial crisis based on complex network. Cluster Computing, 22:4307–4313.
  32. Towards deeper graph neural networks. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, pages 338–348.
  33. Livingston, M. (1977). Industry movements of common stocks. The Journal of Finance, 32(3):861–874.
  34. Malkiel, B. G. (2003). The efficient market hypothesis and its critics. Journal of economic perspectives, 17(1):59–82.
  35. Mantegna, R. N. (1999). Hierarchical structure in financial markets. The European Physical Journal B-Condensed Matter and Complex Systems, 11:193–197.
  36. Evolvegcn: Evolving graph convolutional networks for dynamic graphs. In Proceedings of the AAAI conference on artificial intelligence, volume 34, pages 5363–5370.
  37. A dual-stage attention-based recurrent neural network for time series prediction. In Proceedings of the 26th International Joint Conference on Artificial Intelligence, pages 2627–2633.
  38. Dropedge: Towards deep graph convolutional networks on node classification. arXiv preprint arXiv:1907.10903.
  39. Predicting stock prices using lstm. International Journal of Science and Research, 6(4):1754–1756.
  40. A survey on oversmoothing in graph neural networks. arXiv preprint arXiv:2303.10993.
  41. Stock selection via spatiotemporal hypergraph attention network: A learning to rank approach. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 497–504.
  42. Deep attentive learning for stock movement prediction from social media text and company correlations. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, pages 8415–8426.
  43. Exploring the scale-free nature of stock markets: Hyperbolic graph learning for algorithmic trading. In Proceedings of the Web Conference, pages 11–22.
  44. Schwert, G. W. (2002). Stock volatility in the new millennium: how wacky is nasdaq? Journal of Monetary Economics, 49(1):3–26.
  45. A global network topology of stock markets: Transmitters and receivers of spillover effects. Physica A: Statistical Mechanics and its Applications, 492:2136–2153.
  46. State dependent parallel neural hawkes process for limit order book event stream prediction and simulation. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 1607–1615.
  47. Graph attention networks. In International Conference on Learning Representations.
  48. Traffic flow prediction via spatial temporal graph neural network. In Proceedings of the web conference, pages 1082–1092.
  49. Transformers in time series: A survey. arXiv preprint arXiv:2202.07125.
  50. Graph wavenet for deep spatial-temporal graph modeling. In Proceedings of the 28th International Joint Conference on Artificial Intelligence, pages 1907–1913.
  51. Temporal and heterogeneous graph neural network for financial time series prediction. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management, pages 3584–3593.
  52. Representation learning on graphs with jumping knowledge networks. In International conference on machine learning, pages 5453–5462. PMLR.
  53. Stock movement prediction from tweets and historical prices. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pages 1970–1979.
  54. Multi-graph convolutional network for relationship-driven stock movement prediction. In 2020 25th International Conference on Pattern Recognition, pages 6702–6709. IEEE.
  55. Accurate multivariate stock movement prediction via data-axis transformer with multi-level contexts. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pages 2037–2045.
  56. Information transfer between stock market sectors: A comparison between the usa and china. Entropy, 22(2):194.
  57. Decoupling the depth and scope of graph neural networks. Advances in Neural Information Processing Systems, 34:19665–19679.
  58. Spatio-temporal graph structure learning for traffic forecasting. In Proceedings of the AAAI conference on artificial intelligence, volume 34, pages 1177–1185.
  59. Adaptive diffusion in graph neural networks. Advances in Neural Information Processing Systems, 34:23321–23333.
  60. A survey on graph structure learning: Progress and opportunities. arXiv:2103.03036 https://doi.org/10.48550/arXiv.2103.03036.
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Authors (6)
  1. Zinuo You (4 papers)
  2. Zijian Shi (10 papers)
  3. Hongbo Bo (5 papers)
  4. John Cartlidge (19 papers)
  5. Li Zhang (693 papers)
  6. Yan Ge (11 papers)
Citations (1)
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