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Stock trend prediction based on industry relationships driven hypergraph attention networks

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Abstract

In financial research, accurately predicting the movement trends of stock prices has been a focus for many researchers. The interrelationships among stocks are important factors that influence stock prices. However, recent research has revealed several limitations of traditional deep learning models in capturing these interrelationships, including the inability to learn higher-order relationships among stocks, the inability to dynamically update the relationship graph, and the failure to model the impact of industry relationships on individual stocks. To address these limitations, we propose an industry relationship-driven hypergraph attention network (IRD-HGAT) for predicting stock price movement trends. A key aspect of our work is the use of a hypergraph structure to represent the higher-order relationships among stock industries. The hypergraph attention mechanism is used to dynamically update the relationships between stocks, and the properties of industry hyperedges are aggregated to analyze the impact of industry relationships on stock prices. By comparing the current state-of-the-art algorithms, IRD-HGAT achieves excellent predictive performance and profitability on both S &P500 and CSi500 datasets, with AUC and Sharpe ratios of 0.87 and 1.12, respectively. Ablation experiments and parameter sensitivity analyses also further validate the validity and predictive stability of the model components.

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Availability of data and materials

In the experiments, the CSI 500 data is obtained through the open-source platform baostock (https://www.baostock.com), and the S &P 500 data is obtained through the open-source platform yfinance (https://github.com/ranaroussi/yfinance).

Notes

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Acknowledgements

This work was supported by the Natural Science Foundation of Guangdong Province (No. 2020A1515011208) and the Science and Technology Program Foundation of Guangzhou (No. 202102080353).

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Authors and Affiliations

  1. School of Science, Wuhan University of Technology, Hongshan District, 430000, Wuhan, Hubei, China

    Haodong Han, Liang Xie & Shengshuang Chen

  2. School of Computer Science, GuangDong University of Education, Guangzhou, 510000, Guangdong, China

    Haijiao Xu

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  1. Haodong Han
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Correspondence to Liang Xie.

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Han, H., Xie, L., Chen, S. et al. Stock trend prediction based on industry relationships driven hypergraph attention networks. Appl Intell 53, 29448–29464 (2023). https://doi.org/10.1007/s10489-023-05035-z

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