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Corporate vulnerability in the US and China during COVID-19: A machine learning approach
The impact of COVID-19 on stock market dynamics and other macroeconomic indicators has been extensively researched. However, the question of how it affects corporate vulnerability has received less attention. This article aims to fill this gap by examining the implications of COVID-19 on corporate v...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier B.V.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10083205/ https://www.ncbi.nlm.nih.gov/pubmed/37089460 http://dx.doi.org/10.1016/j.jeca.2023.e00302 |
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author | Khan, Muhammad Asif Segovia, Juan E.Trinidad Bhatti, M.Ishaq Kabir, Asif |
author_facet | Khan, Muhammad Asif Segovia, Juan E.Trinidad Bhatti, M.Ishaq Kabir, Asif |
author_sort | Khan, Muhammad Asif |
collection | PubMed |
description | The impact of COVID-19 on stock market dynamics and other macroeconomic indicators has been extensively researched. However, the question of how it affects corporate vulnerability has received less attention. This article aims to fill this gap by examining the implications of COVID-19 on corporate vulnerability in the United States (US) and China, using daily data from January 2020 to December 2021. The empirical results of cointegration analysis demonstrate that COVID-19 considerably worsen corporate vulnerabilities in the long-term in the US and in the short-term in China. Additionally, non-linear results demonstrate long-run asymmetries in the US and short-run asymmetries in China, confirming the accuracy of error prediction and suggesting that US corporations are more exposed to COVID-19-induced risks. The channels through which COVID-19 may affect corporate vulnerability include changes in consumer behavior and demand, disruptions in supply chains, financial stress, government policies and regulations, and changes in the competitive landscape. This study sheds light on the effects of the COVID-19 pandemic on corporate vulnerability in the US and China, revealing regulatory implications that may necessitate greater government involvement, managerial implications that emphasize risk management and contingency planning, and social implications that highlight the importance of prioritizing stakeholder welfare and embracing digital transformation. |
format | Online Article Text |
id | pubmed-10083205 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier B.V. |
record_format | MEDLINE/PubMed |
spelling | pubmed-100832052023-04-10 Corporate vulnerability in the US and China during COVID-19: A machine learning approach Khan, Muhammad Asif Segovia, Juan E.Trinidad Bhatti, M.Ishaq Kabir, Asif J Econ Asymmetries Article The impact of COVID-19 on stock market dynamics and other macroeconomic indicators has been extensively researched. However, the question of how it affects corporate vulnerability has received less attention. This article aims to fill this gap by examining the implications of COVID-19 on corporate vulnerability in the United States (US) and China, using daily data from January 2020 to December 2021. The empirical results of cointegration analysis demonstrate that COVID-19 considerably worsen corporate vulnerabilities in the long-term in the US and in the short-term in China. Additionally, non-linear results demonstrate long-run asymmetries in the US and short-run asymmetries in China, confirming the accuracy of error prediction and suggesting that US corporations are more exposed to COVID-19-induced risks. The channels through which COVID-19 may affect corporate vulnerability include changes in consumer behavior and demand, disruptions in supply chains, financial stress, government policies and regulations, and changes in the competitive landscape. This study sheds light on the effects of the COVID-19 pandemic on corporate vulnerability in the US and China, revealing regulatory implications that may necessitate greater government involvement, managerial implications that emphasize risk management and contingency planning, and social implications that highlight the importance of prioritizing stakeholder welfare and embracing digital transformation. Elsevier B.V. 2023-06 2023-04-10 /pmc/articles/PMC10083205/ /pubmed/37089460 http://dx.doi.org/10.1016/j.jeca.2023.e00302 Text en © 2023 Elsevier B.V. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Khan, Muhammad Asif Segovia, Juan E.Trinidad Bhatti, M.Ishaq Kabir, Asif Corporate vulnerability in the US and China during COVID-19: A machine learning approach |
title | Corporate vulnerability in the US and China during COVID-19: A machine learning approach |
title_full | Corporate vulnerability in the US and China during COVID-19: A machine learning approach |
title_fullStr | Corporate vulnerability in the US and China during COVID-19: A machine learning approach |
title_full_unstemmed | Corporate vulnerability in the US and China during COVID-19: A machine learning approach |
title_short | Corporate vulnerability in the US and China during COVID-19: A machine learning approach |
title_sort | corporate vulnerability in the us and china during covid-19: a machine learning approach |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10083205/ https://www.ncbi.nlm.nih.gov/pubmed/37089460 http://dx.doi.org/10.1016/j.jeca.2023.e00302 |
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