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

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F25%3A39923435" target="_blank" >RIV/00216275:25410/25:39923435 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/article/10.1007/s10479-023-05311-8" target="_blank" >https://link.springer.com/article/10.1007/s10479-023-05311-8</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s10479-023-05311-8" target="_blank" >10.1007/s10479-023-05311-8</a>

Result language
angličtina
Original language name
An interpretable system for predicting the impact of COVID-19 government interventions on stock market sectors
Original language 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.
Czech name
—
Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
50206 - Finance

Project
<a href="/en/project/GA22-22586S" target="_blank" >GA22-22586S: Aspect-based sentiment analysis of financial texts for predicting corporate financial performance</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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