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Predicting stock price crash risk in China: A modified graph WaveNet model

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Listed:
  • Jing, Zhongbo
  • Li, Qin
  • Zhao, Hongyi
  • Zhao, Yang

Abstract

The stock price of a firm is dynamically influenced by its own factors as well as those of its peers. In this study, we introduce a Graph Attention Network (GAT) integrated with WaveNet architecture—termed the GAT-WaveNet model—to capture both time-series and spatial dependencies for forecasting the stock price crash risk of Chinese listed firms from 2012 to 2023. Utilizing node-rolling techniques to prevent overfitting, our results show that the GAT-WaveNet model significantly outperforms traditional machine learning models in prediction accuracy. Moreover, investment portfolios leveraging the GAT-WaveNet model substantially exceed the cumulative returns of those based on other models.

Suggested Citation

  • Jing, Zhongbo & Li, Qin & Zhao, Hongyi & Zhao, Yang, 2024. "Predicting stock price crash risk in China: A modified graph WaveNet model," Finance Research Letters, Elsevier, vol. 64(C).
    • Handle: RePEc:eee:finlet:v:64:y:2024:i:c:s1544612324004987
    DOI: 10.1016/j.frl.2024.105468
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    References listed on IDEAS

    as
    1. 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.
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    3. Liyun Zhou & Jialiang Huang, 2019. "Investor trading behaviour and stock price crash risk," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 24(1), pages 227-240, January.
    4. 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.
    5. Dichtl, Hubert & Drobetz, Wolfgang & Otto, Tizian, 2023. "Forecasting Stock Market Crashes via Machine Learning," Journal of Financial Stability, Elsevier, vol. 65(C).
    6. Sevim, Cuneyt & Oztekin, Asil & Bali, Ozkan & Gumus, Serkan & Guresen, Erkam, 2014. "Developing an early warning system to predict currency crises," European Journal of Operational Research, Elsevier, vol. 237(3), pages 1095-1104.
    7. Steve Bond & Michael Devereux, 1988. "Financial volatility, the stock market crash and corporate investment," Fiscal Studies, Institute for Fiscal Studies, vol. 9(2), pages 72-80, May.
    8. Chang, Xin & Chen, Yangyang & Zolotoy, Leon, 2017. "Stock Liquidity and Stock Price Crash Risk," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 52(4), pages 1605-1637, August.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Stock price crash risk; Graph neural networks; Graph attention networks; Machine learning;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • M41 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Accounting

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