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An interpretable system for predicting the impact of COVID-19 government interventions on stock market sectors

Author

Listed:
  • Cai Yang

    (Hunan University)

  • Mohammad Zoynul Abedin

    (Swansea University
    Teesside University)

  • Hongwei Zhang

    (Central South University
    Central South University)

  • Futian Weng

    (Xiamen University
    Xiamen University
    Xiamen University)

  • Petr Hajek

    (University of Pardubice)

Abstract

Evaluating and understanding the financial impacts of COVID-19 has emerged as an urgent research agenda. Nevertheless, the impacts of government interventions on stock markets remain poorly understood. This study explores, for the first time, the impact of COVID-19 related government intervention policies on different stock market sectors using explainable machine learning-based prediction models. The empirical findings suggest that the LightGBM model provides excellent prediction accuracy while preserving computationally efficient and easy explainability of the model. We also find that COVID-19 government interventions are better predictors of stock market volatility than stock market returns. We further show that the observed effects of government intervention on the volatility and returns of ten stock market sectors are heterogeneous and asymmetrical. Our findings have important implications for policymakers and investors in terms of promoting balance and sustaining prosperity across industry sectors through government interventions.

Suggested Citation

  • Cai Yang & Mohammad Zoynul Abedin & Hongwei Zhang & Futian Weng & Petr Hajek, 2025. "An interpretable system for predicting the impact of COVID-19 government interventions on stock market sectors," Annals of Operations Research, Springer, vol. 347(2), pages 1031-1058, April.
    • Handle: RePEc:spr:annopr:v:347:y:2025:i:2:d:10.1007_s10479-023-05311-8
    DOI: 10.1007/s10479-023-05311-8
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