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

Cai Yang, Abedin Abedin, Hongwei Zhang, Futian Weng, Petr Hajek

Annals of Operations Research

Swansea University Author: Abedin Abedin

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DOI (Published version): 10.1007/s10479-023-05311-8

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 po...

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Published in: Annals of Operations Research
ISSN: 0254-5330 1572-9338
Published: Springer Science and Business Media LLC
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URI: https://cronfa.swan.ac.uk/Record/cronfa64222
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first_indexed 2023-09-20T15:13:12Z
last_indexed 2023-09-20T15:13:12Z
id cronfa64222
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spelling v2 64222 2023-08-31 An interpretable system for predicting the impact of COVID-19 government interventions on stock market sectors 4ed8c020eae0c9bec4f5d9495d86d415 Abedin Abedin Abedin Abedin true false 2023-08-31 BAF 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. Journal Article Annals of Operations Research Springer Science and Business Media LLC 0254-5330 1572-9338 COVID-19, Government interventions, Stock market, SHapley Additive exPlanations 0 0 0 0001-01-01 10.1007/s10479-023-05311-8 http://dx.doi.org/10.1007/s10479-023-05311-8 COLLEGE NANME Accounting and Finance COLLEGE CODE BAF Swansea University 2023-09-22T15:16:54.1400420 2023-08-31T17:21:35.1800111 Faculty of Humanities and Social Sciences School of Management - Accounting and Finance Cai Yang 1 Abedin Abedin 2 Hongwei Zhang 3 Futian Weng 4 Petr Hajek 5
title An interpretable system for predicting the impact of COVID-19 government interventions on stock market sectors
spellingShingle An interpretable system for predicting the impact of COVID-19 government interventions on stock market sectors
Abedin Abedin
title_short An interpretable system for predicting the impact of COVID-19 government interventions on stock market sectors
title_full An interpretable system for predicting the impact of COVID-19 government interventions on stock market sectors
title_fullStr An interpretable system for predicting the impact of COVID-19 government interventions on stock market sectors
title_full_unstemmed An interpretable system for predicting the impact of COVID-19 government interventions on stock market sectors
title_sort An interpretable system for predicting the impact of COVID-19 government interventions on stock market sectors
author_id_str_mv 4ed8c020eae0c9bec4f5d9495d86d415
author_id_fullname_str_mv 4ed8c020eae0c9bec4f5d9495d86d415_***_Abedin Abedin
author Abedin Abedin
author2 Cai Yang
Abedin Abedin
Hongwei Zhang
Futian Weng
Petr Hajek
format Journal article
container_title Annals of Operations Research
institution Swansea University
issn 0254-5330
1572-9338
doi_str_mv 10.1007/s10479-023-05311-8
publisher Springer Science and Business Media LLC
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Accounting and Finance{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Accounting and Finance
url http://dx.doi.org/10.1007/s10479-023-05311-8
document_store_str 0
active_str 0
description 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.
published_date 0001-01-01T15:16:53Z
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score 11.016235

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