Predicting distress: a post Insolvency and Bankruptcy Code 2016 analysis

Paras Arora () and Suman Saurabh ()
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Paras Arora: McKinsey and Company
Suman Saurabh: Indian Institute of Technology Kanpur

Journal of Economics and Finance, 2022, vol. 46, issue 3, No 10, 604-622

Abstract: Abstract In 2016, India's Insolvency and Bankruptcy Board laid out the Insolvency and Bankruptcy Code for Indian companies struggling financially and seeking solvency or resolution. Since then, around three hundred firms have filed for bankruptcy resolution in India as per IBC 2016. This research studies the financial distress in Indian companies listed on the Bombay Stock Exchange (BSE) by taking a balanced sample of companies. The extant research has employed various methodologies. We apply state-of-the-art machine learning techniques for the task of prediction, such as logistic regression, lasso regression, decision tree, bagging, boosting, and support vector machine. We selected eighteen firm-level variables as explanatory variables, among which the ratio of Market capitalization/Debt came out to be the most critical variable in all the models. This variable is suggested as a measure of leverage in Altman's z-score model. The finding on the significance of the ratio of Market capitalization/Debt is in line with the existing literature. Debt is expected to be higher for financially distressed firms than the financially healthy ones. Further, as the investors might not be interested in investing in a distressed firm, it is likely to lead to a further decrease in market capitalization in financially distressed firms. The random forest bagging model achieved the highest accuracy, recall, and area under the curve (AUC) for the receiver operating characteristic (ROC) curve on the performance of the models. The boosting model achieved the highest precision.

Keywords: Financial distress; Machine learning; Bankruptcy; Insolvency; G32; G33 (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s12197-022-09582-y

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