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

Cai Yang, Mohammad Zoynul Abedin (), Hongwei Zhang, Futian Weng () and Petr Hajek
Additional contact information
Cai Yang: Hunan University
Mohammad Zoynul Abedin: Swansea University
Hongwei Zhang: Central South University
Futian Weng: Xiamen University
Petr Hajek: University of Pardubice

Annals of Operations Research, 2025, vol. 347, issue 2, No 10, 1058 pages

Abstract: 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.

Keywords: COVID-19; Government interventions; Stock market; SHapley Additive exPlanations (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10479-023-05311-8 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:annopr:v:347:y:2025:i:2:d:10.1007_s10479-023-05311-8

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10479

DOI: 10.1007/s10479-023-05311-8

Access Statistics for this article

Annals of Operations Research is currently edited by Endre Boros

More articles in Annals of Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().