Comparative Analysis of Bankruptcy Prediction Models :An Indian Perspective

Abstract

Bankruptcy is a state of insolvency wherein the company or the person is not able to repay the creditors the debt amount. The purpose of this research 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. These bankruptcy prediction models were tested, over the three years prior to bankruptcy using financial ratios. The sample consists of 72 bankrupt and 72 non-bankrupt companies over the period 1991-2016. The results indicate that as compared to multiple discriminant analysis and logistic regression, neural network has the highest classification accuracy for all the three years prior to bankruptcy.