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; Petr Hajek

doi:10.1007/s10479-023-05311-8

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

Ann Oper Res. 2023 Apr 24:1-28. doi: 10.1007/s10479-023-05311-8. Online ahead of print.

Authors

Cai Yang¹, Mohammad Zoynul Abedin^{2

3}, Hongwei Zhang^{4

5}, Futian Weng^{6

7

8}, Petr Hajek⁹

Affiliations

¹ School of Business Administration, Hunan University, Changsha, 410082 China.
² School of Management, Swansea University, Bay Campus, Fabian Way, SA1 8EN Swansea, Wales UK.
³ Department of Finance, Performance and Marketing, Teesside University International Business School, Teesside University, Middlesbrough, TS1 3BX Tees Valley UK.
⁴ School of Mathematics and Statistics, Central South University, Changsha, 410083 Hunan China.
⁵ Institute of Metal Resources Strategy, Central South University, Changsha, 410083 China.
⁶ School of Medicine, Xiamen University, Xiamen, 361005 China.
⁷ National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, 361005 China.
⁸ Data Mining Research Center, Xiamen University, Xiamen, 361005 China.
⁹ Science and Research Centre, Faculty of Economics and Administration, University of Pardubice, Studentska 84, 532 10 Pardubice, Czech Republic.

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; SHapley Additive exPlanations; Stock market.