Bankruptcy prediction using machine learning and an application to the case of the COVID-19 recession

Aditya Narvekar; Debashis Guha; Aditya Narvekar; Debashis Guha

doi:10.3934/DSFE.2021010

Data Science in Finance and Economics

2021, Volume 1, Issue 2: 180-195. doi: 10.3934/DSFE.2021010

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Research article

Bankruptcy prediction using machine learning and an application to the case of the COVID-19 recession

Aditya Narvekar ^{1
,
,},
Debashis Guha ²

1.
Department of Data Science, SP Jain School of Global Management, Sydney, Australia
2.
Department of Data Science, SP Jain School of Global Management, Mumbai, India

Received: 08 August 2021 Accepted: 27 September 2021 Published: 11 October 2021
JEL Codes: O30, E37, E60

Bankruptcy prediction is an important problem in finance, since successful predictions would allow stakeholders to take early actions to limit their economic losses. In recent years many studies have explored the application of machine learning models to bankruptcy prediction with financial ratios as predictors. This study extends this research by applying machine learning techniques to a quarterly data set covering financial ratios for a large sample of public U.S. firms from 1970–2019. We find that tree-based ensemble methods, especially XGBoost, can achieve a high degree of accuracy in out-of-sample bankruptcy prediction. We next apply our best model, using XGBoost, to the problem of predicting the overall bankruptcy rate in USA in the second half of 2020, after the COVID-19 pandemic had necessitated a lockdown, leading to a deep recession. Our model supports the prediction, made by leading economists, that the rate of bankruptcies will rise substantially in 2020, but it also suggests that this elevated level will not be much higher than 2010.
- bankruptcy prediction,
- machine learning,
- multivariate discriminant analysis,
- logit and probit models,
- big data,
- ensemble models,
- XGBoost,
- Random Forests,
- support vector machines
Citation: Aditya Narvekar, Debashis Guha. Bankruptcy prediction using machine learning and an application to the case of the COVID-19 recession[J]. Data Science in Finance and Economics, 2021, 1(2): 180-195. doi: 10.3934/DSFE.2021010

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Abstract

Bankruptcy prediction is an important problem in finance, since successful predictions would allow stakeholders to take early actions to limit their economic losses. In recent years many studies have explored the application of machine learning models to bankruptcy prediction with financial ratios as predictors. This study extends this research by applying machine learning techniques to a quarterly data set covering financial ratios for a large sample of public U.S. firms from 1970–2019. We find that tree-based ensemble methods, especially XGBoost, can achieve a high degree of accuracy in out-of-sample bankruptcy prediction. We next apply our best model, using XGBoost, to the problem of predicting the overall bankruptcy rate in USA in the second half of 2020, after the COVID-19 pandemic had necessitated a lockdown, leading to a deep recession. Our model supports the prediction, made by leading economists, that the rate of bankruptcies will rise substantially in 2020, but it also suggests that this elevated level will not be much higher than 2010.

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