Article: Corporate vulnerability in the US and China during COVID-19: A machine learning approach.
Journal of economic asymmetries
2023 Volume 27, Page(s) e00302
Abstract: 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 ... ...
| Abstract | 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. |
|---|---|
| Language | English |
| Publishing date | 2023-04-10 |
| Publishing country | Canada |
| Document type | Journal Article |
| ZDB-ID | 2571389-9 |
| ISSN | 1703-4949 |
| ISSN | 1703-4949 |
| DOI | 10.1016/j.jeca.2023.e00302 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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