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A hybrid model for stock price prediction based on multi-view heterogeneous data

Author

Listed:
  • Wen Long

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

  • Jing Gao

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

  • Kehan Bai

    (Beijing Jiaotong University)

  • Zhichen Lu

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

Abstract

Literature shows that both market data and financial media impact stock prices; however, using only one kind of data may lead to information bias. Therefore, this study uses market data and news to investigate their joint impact on stock price trends. However, combining these two types of information is difficult because of their completely different characteristics. This study develops a hybrid model called MVL-SVM for stock price trend prediction by integrating multi-view learning with a support vector machine (SVM). It works by simply inputting heterogeneous multi-view data simultaneously, which may reduce information loss. Compared with the ARIMA and classic SVM models based on single- and multi-view data, our hybrid model shows statistically significant advantages. In the robustness test, our model outperforms the others by at least 10% accuracy when the sliding windows of news and market data are set to 1–5 days, which confirms our model’s effectiveness. Finally, trading strategies based on single stock and investment portfolios are constructed separately, and the simulations show that MVL-SVM has better profitability and risk control performance than the benchmarks.

Suggested Citation

  • Wen Long & Jing Gao & Kehan Bai & Zhichen Lu, 2024. "A hybrid model for stock price prediction based on multi-view heterogeneous data," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-50, December.
    • Handle: RePEc:spr:fininn:v:10:y:2024:i:1:d:10.1186_s40854-023-00519-w
    DOI: 10.1186/s40854-023-00519-w
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    References listed on IDEAS

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    2. Jeffrey E. Jarrett & Janne Schilling, 2008. "Daily variation and predicting stock market returns for the frankfurter börse (stock market)," Journal of Business Economics and Management, Taylor & Francis Journals, vol. 9(3), pages 189-198, March.
    3. Fischer, Thomas & Krauss, Christopher, 2018. "Deep learning with long short-term memory networks for financial market predictions," European Journal of Operational Research, Elsevier, vol. 270(2), pages 654-669.
    4. Michelangelo Ceci & Gianvito Pio & Vladimir Kuzmanovski & Sašo Džeroski, 2015. "Semi-Supervised Multi-View Learning for Gene Network Reconstruction," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-27, December.
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