Predicting Probability of Debt Default : A Study of Corporate Debt Market in India and other Countries

Default by corporate is problem of concern for banking and financial services industry along with economy. Timely prediction of default is imperative for the bankers and regulators to devise and take preventive actions. There has been large numbers of studies across the world and in India too few studies conducted for Indian data. An extensive review of literature reveals that the subject of default prediction has not explored much in India. In India along with rest of the world, the numbers of defaults have been increasing every year. This creates scope for further studies.A comparative study has been conducted on the data of three countries, namely India, the US and the UK using four methods; MDA, logit, reduced form and structural models. The study for the Indian sample has developed 24 models for three methods; MDA, logit and reduced form model for two sample periods large sample period and small sample period on a sample of 2450 case over ten years from 1st April 2004 to 31st March 2014 and on two years data from 1st April 2014 to 31st March 2016, the developed models have been validated for the forward period as well as for the out of sample firms. Structural model has been tested on Indian data. The study for the American sample has developed three models for the three methods; MDA, logit and reduced form model. Structural model has been tested on American data. The study for the British sample has developed three models for the three methods; MDA, logit and reduced form model. Structural model has been tested on British data. So in this study total 30 models have been developed.The study uses multiple discriminant analysis and logistic regression analysis to calculate composite scores which are used to predict default. The structural model uses option mathematics to give probability of default with help of market value of assets, book value of debt and asset drift rate. Reduced form model uses Poisson regression to estimate intensity of default which is transformed into probability of default.The findings of the study for Indian sample suggest that logit model yields the highest classification accuracy of 97.7% and reduced form model yields the lowest classification accuracy of 82.3%. The models for small sample period are robust but classification accuracies for the models for large sample period are higher for different methods. The classification accuracies of 24 developed models for Indian sample has higher than many studies conducted in India and other countries. The findings of the study on American sample suggest that logit model yields the highest classification accuracy of 95% and structural model yields the lowest classification accuracy of 85.9% and classification accuracies of developed models higher than many studies. The findings of the study on British sample suggest that structural model yields the highest classification accuracy of 81.6% and reduced form model yields the lowest classification accuracy of 67.9%.The findings on variables suggest that all the three types of variables namely accounting, market and economic variables are significant for all the three samples from India, the US and the UK. In case of India, it is observed that all the variables which are significant for large sample period are significant for small sample period indicating the in long run firms tend to follow a certain pattern and default can be predicted with the help of a set of certain variables as far as default predict is concerned. However in short run market variables seem play minor role in default prediction. From the analysis of variables it is evident that total book value of debt with respect to total assets is the most important variable among all the variables with debt component. The biggest limitation of this study is that the models for small sample period are more robust while the models for large sample period have higher classification accuracy. The whole study is facing the problem of sample and selection biases