IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v347y2025i2d10.1007_s10479-023-05311-8.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-023-05311-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10479-023-05311-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:annopr:v:347:y:2025:i:2:d:10.1007_s10479-023-05311-8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.