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Stock market prediction with deep learning: The case of China

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  • Liu, Qingfu
  • Tao, Zhenyi
  • Tse, Yiuman
  • Wang, Chuanjie

Abstract

We consider stock price charts as images and use deep learning neural networks (DLNNs) for image modeling. DLNNs can imitate the work of a technical analyst to predict stock price movements in the short term with price charts and stock fundamentals (e.g., price-to-earnings ratio). We find that a deep learning model performs better than a single-layer model in the prediction of the Chinese stock market. DLNNs provide customizable statistical tools for analyzing price charts effectively. More importantly, price trends established by different periods of past daily closing prices dominate stock fundamentals in predicting future price movements.

Suggested Citation

  • Liu, Qingfu & Tao, Zhenyi & Tse, Yiuman & Wang, Chuanjie, 2022. "Stock market prediction with deep learning: The case of China," Finance Research Letters, Elsevier, vol. 46(PA).
    • Handle: RePEc:eee:finlet:v:46:y:2022:i:pa:s1544612321002762
    DOI: 10.1016/j.frl.2021.102209
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    References listed on IDEAS

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    1. Daniele Bianchi & Matthias Büchner & Tobias Hoogteijling & Andrea Tamoni, 2021. "Corrigendum: Bond Risk Premiums with Machine Learning [Bond risk premiums with machine learning]," The Review of Financial Studies, Society for Financial Studies, vol. 34(2), pages 1090-1103.
    2. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," Review of Finance, European Finance Association, vol. 33(5), pages 2223-2273.
    3. Daniele Bianchi & Matthias Büchner & Andrea Tamoni, 2021. "Bond Risk Premiums with Machine Learning [Quadratic term structure models: Theory and evidence]," The Review of Financial Studies, Society for Financial Studies, vol. 34(2), pages 1046-1089.
    4. Liu, Mingxi & Li, Guowen & Li, Jianping & Zhu, Xiaoqian & Yao, Yinhong, 2021. "Forecasting the price of Bitcoin using deep learning," Finance Research Letters, Elsevier, vol. 40(C).
    5. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
    6. Fama, Eugene F. & French, Kenneth R., 2015. "A five-factor asset pricing model," Journal of Financial Economics, Elsevier, vol. 116(1), pages 1-22.
    7. ANGHEL, Dan-Gabriel, 2021. "A reality check on trading rule performance in the cryptocurrency market: Machine learning vs. technical analysis," Finance Research Letters, Elsevier, vol. 39(C).
    8. 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).
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    Cited by:

    1. Christian Fieberg & Daniel Metko & Thorsten Poddig & Thomas Loy, 2023. "Machine learning techniques for cross-sectional equity returns’ prediction," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 45(1), pages 289-323, March.
    2. Naseh Majidi & Mahdi Shamsi & Farokh Marvasti, 2022. "Algorithmic Trading Using Continuous Action Space Deep Reinforcement Learning," Papers 2210.03469, arXiv.org.
    3. Ma, Yilin & Wang, Yudong & Wang, Weizhong & Zhang, Chong, 2023. "Portfolios with return and volatility prediction for the energy stock market," Energy, Elsevier, vol. 270(C).
    4. Guo, Wei & Liu, Qingfu & Luo, Zhidan & Tse, Yiuman, 2022. "Forecasts for international financial series with VMD algorithms," Journal of Asian Economics, Elsevier, vol. 80(C).

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