Predicting Financial Distress for Organizational Sustainability in India: A Comparative Study of Logistic Regression, LDA, and ANN Approaches

Abstract

Predicting corporate financial distress remains crucial for the growth and stability of the global economic and financial landscape. Accurately forecasting a company's financial health is essential for business leaders, policymakers, shareholders, and regulatory bodies to make timely and informed decisions that promote sustainable growth. This study evaluates the likelihood of insolvency among Indian non-financial service sector firms from 2012 to 2022, examining the predictive accuracy of artificial neural networks (ANN), logistic regression (LR), and linear discriminant analysis (LDA) in forecasting bankruptcy. Using a panel dataset spanning eleven years, the study applies all three models to assess their effectiveness. Results show that the Logit model achieved the highest accuracy at 87.28%, outperforming ANN's training accuracy of 85.39%, testing accuracy of 86.39%, and LDA's 72.02% accuracy. Findings from this investigation are expected to benefit managers, depositors, regulatory bodies, shareholders, and other stakeholders in the service sector as they strive to manage their interests effectively.