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Deep learning in the Chinese stock market: The role of technical indicators

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  • Ma, Chenyao
  • Yan, Sheng

Abstract

A convolutional neural network (CNN) is applied to forecast stock price changes in the Chinese stock market. We use 27 technical indicators and 5 original price series as benchmark model setting and further explore the model forecasting performance with social media sentiment. Our results show that our model could obtain 70% forecasting accuracy on average. Moreover, social media sentiment could increase the forecasting performance for both indexes and individual stocks.

Suggested Citation

  • Ma, Chenyao & Yan, Sheng, 2022. "Deep learning in the Chinese stock market: The role of technical indicators," Finance Research Letters, Elsevier, vol. 49(C).
    • Handle: RePEc:eee:finlet:v:49:y:2022:i:c:s1544612322002653
    DOI: 10.1016/j.frl.2022.103025
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    References listed on IDEAS

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    Cited by:

    1. Sheng, Yankai & Qu, Yuanyu & Ma, Ding, 2024. "Stock price crash prediction based on multimodal data machine learning models," Finance Research Letters, Elsevier, vol. 62(PA).
    2. Yan, Wan-Lin, 2023. "Stock index futures price prediction using feature selection and deep learning," The North American Journal of Economics and Finance, Elsevier, vol. 64(C).

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