Financial Performance and Corporate Distress: Searching for Common Factors for Firms in the Indian Registered Manufacturing Sector

Jessica Thacker () and Debdatta Saha ()
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Jessica Thacker: South Asian University
Debdatta Saha: South Asian University

Computational Economics, 2025, vol. 65, issue 6, No 26, 3883 pages

Abstract: Abstract This paper knits the concepts of financial performance and financial distress in a unified framework. The machine learning algorithm of extreme gradient boosting (XGBoost) is employed to identify the set of factors predicting financial distress and performance and panel logistic regressions indicate the direction of influence and significance of these common factors. The XGBoost algorithm indicates the existence of some common factors, such as lagged net profit margin, growth of profit after tax, lagged assets turnover ratio, growth of sales and log of total asset. Additionally, past performance is found to impact current financial distress and vice-versa. The regression results shows that profit growth significantly improves financial performance while reducing corporate distress. This calls for a common framework to analyze these two phenomena for registered firms.

Keywords: Financial performance; Financial distress; Extreme gradient boosting; Machine learning; Panel logistic regression; Registered firms (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10614-024-10620-6

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