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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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    Cited by:

    1. 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).
    2. 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.
    3. Naseh Majidi & Mahdi Shamsi & Farokh Marvasti, 2022. "Algorithmic Trading Using Continuous Action Space Deep Reinforcement Learning," Papers 2210.03469, arXiv.org.
    4. 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).

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