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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

50206 - Finance

Projekt

<a href="/cs/project/GA22-22586S" target="_blank" >GA22-22586S: Aspektově orientovaná analýza sentimentu finančních textů pro predikci finanční výkonnosti podniku</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Annals of Operations Research

ISSN

0254-5330

e-ISSN

1572-9338

Svazek periodika

347

Číslo periodika v rámci svazku

Neuveden

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

28

Strana od-do

1031-1058

Kód UT WoS článku

000977216100004

EID výsledku v databázi Scopus

2-s2.0-85153336466