Comparison of Bankruptcy Prediction Models: Evidence from India

Abstract

The purpose of this paper is to develop and compare the performance of bankruptcy prediction models using multiple discriminant analysis, logistic regression and neural network for listed companies in India. Accordingly bankruptcy prediction models are developed, over the three years prior to bankruptcy using financial ratios. The sample consists of 72 bankrupt and 72 non-bankrupt companies over the period 1991-2013. The results indicate that compared to multiple discriminant analysis and logistic regression, neural network has the highest classification accuracy for all the three years prior to bankruptcy. This study will be useful to financial institutions, investors, creditors and auditors to identify companies that are likely to experience bankruptcy.