Models for Prediction of Industrial Insolvency of Manufacturing Companies in India

Abstract

Investors, activists and corporations across the world are emphasizing on prediction of insolvency well in advance so that corrective actions can be taken and erosion of funds can be prevented. For this purpose, this study attempts to construct models for forecasting industrial sickness and to validate the performance of these models. This paper proposes new models for prediction of industrial sickness or leading bankruptcy using three different techniques i.e. a model using Multi Discriminant Analysis (MDA), a model using MDA + PCA (Principle Component Analysis), and a model using ANN (Artificial Neural Network).This paper focuses to propose prediction models for bankruptcy which contribute more to this impact in emerging economies like India.

The results show that the forecasting ability of the models is higher than the empirical models already available such Altman’s Z score model, Ohlson’s model and model developed by Odom and Sharda to name a few. The prediction accuracy of MDA model is highest among proposed models for prediction of industrial sickness. These results are recommended to financial institutions, banks and executives. This study may also be used for evaluating the repayment behavior of a borrower. These models may be used by the potential investors for screening out undesirable investments. Since the models are of predictive nature, the investors may use it for portfolio selection.