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Research on Stock Index Prediction Based on the Spatiotemporal Attention BiLSTM Model

Author

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  • Shengdong Mu

    (Collaborative Innovation Center of Green Development in the Wuling Shan Region, Yangtze Normal University, Chongqing 408100, China
    Chongqing Vocational College of Transportation, Chongqing 402200, China
    Department of Business Administration, International College, Krirk University, Bangkok 10220, Thailand
    These authors contributed equally to this work.)

  • Boyu Liu

    (School of Innovation and Entrepreneurship, Hubei University of Economics, Wuhan 430000, China
    These authors contributed equally to this work.)

  • Jijian Gu

    (Chongqing Vocational College of Transportation, Chongqing 402200, China)

  • Chaolung Lien

    (Department of Business Administration, International College, Krirk University, Bangkok 10220, Thailand)

  • Nedjah Nadia

    (Department of Electronics Engineering and Telecommunications, State University of Rio de Janeiro, Rio de Janeiro 205513, Brazil)

Abstract

Stock index fluctuations are characterized by high noise and their accurate prediction is extremely challenging. To address this challenge, this study proposes a spatial–temporal–bidirectional long short-term memory (STBL) model, incorporating spatiotemporal attention mechanisms. The model enhances the analysis of temporal dependencies between data by introducing graph attention networks with multi-hop neighbor nodes while incorporating the temporal attention mechanism of long short-term memory (LSTM) to effectively address the potential interdependencies in the data structure. In addition, by assigning different learning weights to different neighbor nodes, the model can better integrate the correlation between node features. To verify the accuracy of the proposed model, this study utilized the closing prices of the Hong Kong Hang Seng Index (HSI) from 31 December 1986 to 31 December 2023 for analysis. By comparing it with nine other forecasting models, the experimental results show that the STBL model achieves more accurate predictions of the closing prices for short-term, medium-term, and long-term forecasts of the stock index.

Suggested Citation

  • Shengdong Mu & Boyu Liu & Jijian Gu & Chaolung Lien & Nedjah Nadia, 2024. "Research on Stock Index Prediction Based on the Spatiotemporal Attention BiLSTM Model," Mathematics, MDPI, vol. 12(18), pages 1-20, September.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:18:p:2812-:d:1475874
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    References listed on IDEAS

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    1. Akhter Mohiuddin Rather & V. N. Sastry & Arun Agarwal, 2017. "Stock market prediction and Portfolio selection models: a survey," OPSEARCH, Springer;Operational Research Society of India, vol. 54(3), pages 558-579, September.
    2. Fischer, Thomas & Krauss, Christopher, 2018. "Deep learning with long short-term memory networks for financial market predictions," European Journal of Operational Research, Elsevier, vol. 270(2), pages 654-669.
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    Cited by:

    1. Shiguo Huang & Linyu Cao & Ruili Sun & Tiefeng Ma & Shuangzhe Liu, 2024. "Enhancing Portfolio Optimization: A Two-Stage Approach with Deep Learning and Portfolio Optimization," Mathematics, MDPI, vol. 12(21), pages 1-21, October.

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