Does Size Matter? Analysing the Financial Implications of COVID-19 on SMEs and Large Companies Using a Hybrid Methodology
Abstract Financial distress prediction holds significant value for stakeholders as it signals a company’s financial health. This study has three key. Objectives developing accurate hybrid prediction models, empirically testing financial distress during the COVID-19 pandemic, and identifying crucial...
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| Format: | Article |
| Language: | English |
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Springer
2025-02-01
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| Series: | Journal of Statistical Theory and Applications (JSTA) |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44199-025-00107-0 |
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| Summary: | Abstract Financial distress prediction holds significant value for stakeholders as it signals a company’s financial health. This study has three key. Objectives developing accurate hybrid prediction models, empirically testing financial distress during the COVID-19 pandemic, and identifying crucial financial distress predictors for small and large companies. Utilising Artificial Neural Networks and Multiple Discriminant Analysis, hybrid models were created and tested on a dataset featuring 1,342 Australian companies. The results indicate that financial distress only increased among large businesses during the pandemic. To add, the COVID-year variable was deemed unimportant in predicting distress. The study underscores varying predictors’ importance, emphasising quick ratio, debt ratio, and gross margin for small businesses, and net income margin, return on assets, and return on capital for large enterprises. This study contributes to the literature by improving our comprehension of how instances of force majeure affect businesses financially, and highlights differences in predictors between small and large enterprises, and provides valuable insights for policymakers, financial analysts, and business leaders aiming to mitigate financial distress during future crises. |
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| ISSN: | 2214-1766 |