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An interval constraint-based trading strategy with social sentiment for the stock market

Author

Listed:
  • Mingchen Li

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Kun Yang

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences
    Chinese Academy of Sciences)

  • Wencan Lin

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Yunjie Wei

    (Chinese Academy of Sciences
    Chinese Academy of Sciences)

  • Shouyang Wang

    (Chinese Academy of Sciences
    Chinese Academy of Sciences)

Abstract

Developing effective strategies to earn excess returns in the stock market is a cutting-edge topic in the field of economics. At the same time, stock price forecasting that supports trading strategies is considered one of the most challenging tasks. Therefore, this study analyzes and extracts news media data, expert comments, social opinion data, and pandemic text data using natural language processing, and then combines the data with a deep learning model to forecast future stock price patterns based on historical stock prices. An interval constraint-based trading strategy is constructed. Using data from several typical stocks in the Chinese stock market during the COVID-19 period, the empirical studies and trading simulations show, first, that the sentiment composite index and the deep learning model can improve the accuracy of stock price forecasting. Second, the interval constraint-based trading strategy based on the proposed approach can effectively enhance returns and thus, can assist investors in decision-making.

Suggested Citation

  • Mingchen Li & Kun Yang & Wencan Lin & Yunjie Wei & Shouyang Wang, 2024. "An interval constraint-based trading strategy with social sentiment for the stock market," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-31, December.
    • Handle: RePEc:spr:fininn:v:10:y:2024:i:1:d:10.1186_s40854-023-00567-2
    DOI: 10.1186/s40854-023-00567-2
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