Abstract
The stock market is a highly complex and dynamic system, where relationships between stocks play a critical role in predicting price movements. To capture these relationships, we propose a novel approach called the hierarchical predictive representation (HPR). The pairwise attention network is first employed to identify effective relationships between stocks. Then, the hierarchical node matching identifies the most predictive relationship subset at various hierarchical levels. By concatenating representations from various levels, our method achieves a comprehensive representation that reflects local to global information. We further introduce a representation ensemble mechanism to leverage multiple relationships, enhancing the model’s predictive performance. Extensive experiments on various datasets demonstrate the superiority of HPR compared to existing state-of-the-art methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Brockwell, P.J., Davis, R.A.: Introduction to Time Series and Forecasting. STS, Springer, Cham (2016). https://doi.org/10.1007/978-3-319-29854-2
Chen, D., Lin, Y., Li, W., Li, P., Zhou, J., Sun, X.: Measuring and relieving the over-smoothing problem for graph neural networks from the topological view. In: Proceedings of the AAAI Conference On Artificial Intelligence, vol. 34, pp. 3438–3445 (2020)
Dai, W., An, Y., Long, W.: Price change prediction of ultra high frequency financial data based on temporal convolutional network. Proc. Comput. Sci. 199, 1177–1183 (2022)
Deng, S., Zhang, N., Zhang, W., Chen, J., Pan, J.Z., Chen, H.: Knowledge-driven stock trend prediction and explanation via temporal convolutional network. In: Companion Proceedings of The 2019 World Wide Web Conference, pp. 678–685 (2019)
Ding, Q., Wu, S., Sun, H., Guo, J., Guo, J.: Hierarchical multi-scale gaussian transformer for stock movement prediction. In: IJCAI, pp. 4640–4646 (2020)
Edmonds, J.: Paths, trees, and flowers. Can. J. Math. 17, 449–467 (1965)
Feng, F., He, X., Wang, X., Luo, C., Liu, Y., Chua, T.S.: Temporal relational ranking for stock prediction. ACM Trans. Inform. Syst. (TOIS) 37(2), 1–30 (2019)
Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: Advances in Neural Information Processing Systems 30 (2017)
Hsu, Y.L., Tsai, Y.C., Li, C.T.: Fingat: Financial graph attention networks for recommending top-\( k \) k profitable stocks. IEEE Trans. Knowl. Data Eng. 35(1), 469–481 (2021)
Kim, R., So, C.H., Jeong, M., Lee, S., Kim, J., Kang, J.: Hats: A hierarchical graph attention network for stock movement prediction. arXiv preprint arXiv:1908.07999 (2019)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=SJU4ayYgl
Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)
Nelson, B.K.: Time series analysis using autoregressive integrated moving average (arima) models. Acad. Emerg. Med. 5(7), 739–744 (1998)
Qin, Y., Song, D., Chen, H., Cheng, W., Jiang, G., Cottrell, G.: A dual-stage attention-based recurrent neural network for time series prediction. arXiv preprint arXiv:1704.02971 (2017)
Sims, C.A.: Macroeconomics and reality. Econometrica: J. Economet. Soc., 1–48 (1980)
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., Bengio, Y.: Graph attention networks. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=rJXMpikCZ
Xu, Y., Cohen, S.B.: Stock movement prediction from tweets and historical prices. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, vol. 1: Long Papers, pp. 1970–1979 (2018)
Yoo, J., Soun, Y., Park, Y.c., Kang, U.: 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, pp. 2037–2045 (2021)
Yue, Z., Tan, Y.: Non-local graph aggregation for diversified stock recommendation. In: Data Mining and Big Data: 7th International Conference, DMBD 2022, Beijing, China, 21–24 November 2022, Proceedings, Part II, pp. 147–159. Springer (2023). https://doi.org/10.1007/978-981-19-8991-9_12
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)
Zhu, J., Yan, Y., Zhao, L., Heimann, M., Akoglu, L., Koutra, D.: Beyond homophily in graph neural networks: Current limitations and effective designs. Adv. Neural. Inf. Process. Syst. 33, 7793–7804 (2020)
Acknowledgements
This work is supported by Science and Technology Innovation 2030 - ‘New Generation Artificial Intelligence’ Major Project (Grant Nos.: 2018AAA0100302) and partially supported by the National Natural Science Foundation of China (Grant No. 62250037, No. 62076010 and No. 62276008).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Yue, Z., Tan, Y. (2023). Hierarchical Node Representation Learning for Stock Prediction. In: Tan, Y., Shi, Y., Luo, W. (eds) Advances in Swarm Intelligence. ICSI 2023. Lecture Notes in Computer Science, vol 13969. Springer, Cham. https://doi.org/10.1007/978-3-031-36625-3_37
Download citation
DOI: https://doi.org/10.1007/978-3-031-36625-3_37
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-36624-6
Online ISBN: 978-3-031-36625-3
eBook Packages: Computer ScienceComputer Science (R0)