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Exploring the attention mechanism in LSTM-based Hong Kong stock price movement prediction

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  • Shun Chen
  • Lei Ge

Abstract

State-of-the-art methods using attention mechanism in Recurrent Neural Networks have shown exceptional performance targeting sequential predictions and classifications. We explore the attention mechanism in Long–Short-Term Memory (LSTM) network based stock price movement prediction. Our proposed model significantly enhances the LSTM prediction performance in the Hong Kong stock market. The attention LSTM (AttLSTM) model is compared with the LSTM model in Hong Kong stock movement prediction. Further parameter tuning results also demonstrate the effectiveness of the attention mechanism in LSTM-based prediction method.

Suggested Citation

  • Shun Chen & Lei Ge, 2019. "Exploring the attention mechanism in LSTM-based Hong Kong stock price movement prediction," Quantitative Finance, Taylor & Francis Journals, vol. 19(9), pages 1507-1515, September.
    • Handle: RePEc:taf:quantf:v:19:y:2019:i:9:p:1507-1515
    DOI: 10.1080/14697688.2019.1622287
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    Cited by:

    1. Xiaodong Zhang & Suhui Liu & Xin Zheng, 2021. "Stock Price Movement Prediction Based on a Deep Factorization Machine and the Attention Mechanism," Mathematics, MDPI, vol. 9(8), pages 1-21, April.
    2. Keshab Raj Dahal & Ankrit Gupta & Nawa Raj Pokhrel, 2024. "Predicting the Direction of NEPSE Index Movement with News Headlines Using Machine Learning," Econometrics, MDPI, vol. 12(2), pages 1-26, June.
    3. Niu, Hongli & Xu, Kunliang & Liu, Cheng, 2021. "A decomposition-ensemble model with regrouping method and attention-based gated recurrent unit network for energy price prediction," Energy, Elsevier, vol. 231(C).
    4. Ito, Katsuki & Iima, Hitoshi & Kitamura, Yoshihiro, 2022. "LSTM forecasting foreign exchange rates using limit order book," Finance Research Letters, Elsevier, vol. 47(PA).
    5. Fateme Shahabi Nejad & Mohammad Mehdi Ebadzadeh, 2023. "Stock market forecasting using DRAGAN and feature matching," Papers 2301.05693, arXiv.org.
    6. Evangelos Liaras & Michail Nerantzidis & Antonios Alexandridis, 2024. "Machine learning in accounting and finance research: a literature review," Review of Quantitative Finance and Accounting, Springer, vol. 63(4), pages 1431-1471, November.
    7. Hakan Gunduz, 2021. "An efficient stock market prediction model using hybrid feature reduction method based on variational autoencoders and recursive feature elimination," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-24, December.
    8. Luca Grilli & Domenico Santoro, 2022. "Forecasting financial time series with Boltzmann entropy through neural networks," Computational Management Science, Springer, vol. 19(4), pages 665-681, October.
    9. Yi Fu & Shuai Cao & Tao Pang, 2020. "A Sustainable Quantitative Stock Selection Strategy Based on Dynamic Factor Adjustment," Sustainability, MDPI, vol. 12(10), pages 1-12, May.
    10. Grilli, Luca & Santoro, Domenico, 2020. "How Boltzmann Entropy Improves Prediction with LSTM," MPRA Paper 100578, University Library of Munich, Germany.
    11. Zhang, Yongjie & Chu, Gang & Shen, Dehua, 2021. "The role of investor attention in predicting stock prices: The long short-term memory networks perspective," Finance Research Letters, Elsevier, vol. 38(C).
    12. Qinkai Chen, 2021. "Stock Movement Prediction with Financial News using Contextualized Embedding from BERT," Papers 2107.08721, arXiv.org.
    13. Cheng Zhao & Ping Hu & Xiaohui Liu & Xuefeng Lan & Haiming Zhang, 2023. "Stock Market Analysis Using Time Series Relational Models for Stock Price Prediction," Mathematics, MDPI, vol. 11(5), pages 1-13, February.
    14. Chuting Sun & Qi Wu & Xing Yan, 2023. "Dynamic CVaR Portfolio Construction with Attention-Powered Generative Factor Learning," Papers 2301.07318, arXiv.org, revised Jan 2024.
    15. Yan Guo & Dezhao Tang & Wei Tang & Senqi Yang & Qichao Tang & Yang Feng & Fang Zhang, 2022. "Agricultural Price Prediction Based on Combined Forecasting Model under Spatial-Temporal Influencing Factors," Sustainability, MDPI, vol. 14(17), pages 1-18, August.
    16. Sun, Chuting & Wu, Qi & Yan, Xing, 2024. "Dynamic CVaR portfolio construction with attention-powered generative factor learning," Journal of Economic Dynamics and Control, Elsevier, vol. 160(C).
    17. Yao, Haixiang & Xia, Shenghao & Liu, Hao, 2022. "Six-factor asset pricing and portfolio investment via deep learning: Evidence from Chinese stock market," Pacific-Basin Finance Journal, Elsevier, vol. 76(C).
    18. Wang, Chen & Shen, Dehua & Li, Youwei, 2022. "Aggregate Investor Attention and Bitcoin Return: The Long Short-term Memory Networks Perspective," Finance Research Letters, Elsevier, vol. 49(C).

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