Differential Predictors of Financial Distress in Listed Versus Unlisted Indian Firms: A Machine Learning Approach
Abstract
This study identifies the key determinants of financial distress and evaluates alternative prediction models for both listed and unlisted firms in India. It uses financial data from 1,473 NSE-listed and 472 unlisted Indian firms (2010–2023). Six firm characteristics – profitability, liquidity, solvency, capital structure, operating efficiency, and size – are measured using financial ratios, with market performance additionally included for listed firms. Feature selection identifies the best ratio per characteristic. Classical logit and multiple machine learning models are compared for distress prediction. It is found that leverage, firm size, and capital structure are significant predictors for both firm types. However, optimal measures differ: listed firms are better captured by ROA, working capital to total assets, and sales to total assets, whereas net profit margin, current ratio, and debt collection period perform better for unlisted firms. Machine learning models outperform the logit model, with Random Forest emerging as the best predictor. The study is among the first to simultaneously cover both listed and unlisted Indian firms and to apply machine learning for identifying distress determinants as well as prediction, moving beyond classical statistical approaches. However, the study is limited only to Indian market data, hence restricting cross-market generalisation. Future research should extend to additional emerging markets and more advanced modelling architectures. The findings of the study offer key insights for policy-makers and regulators in designing early-warning frameworks, for commercial banks – in credit risk assessment, for corporate management – in financial monitoring, and for academics – in advancing distress modelling methodology.
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