Firms default when they fail to pay either the instalment of the principal amount, the interest of the loan raised from the commercial banks or financial institutions; or when they fail to oblige the bond or debenture holders. Default is a worldwide problem that impacts holistically the financial credibility of the firms, its business operations, and ultimately the economic growth of the country in which the firm is incorporated. The default prediction process has drawn the considerable attention of the various regulators, accounting practitioners, bankers, scholars across the countries. The early warning signal of the defaults are imperative for the management, investors, creditors, and bankers so that they can take proactive or remedial measures to overcome the flaws of credit rating agencies, creating an internal credit risk system, deciding optimal capital structure. Further, the credit risk modeling is necessary for pricing the riskier bonds and also to sanction the loan to the firms.
The present study has attempted to predict the default events of selected Indian corporate from selected 13 sectors. The total sample firms included in the study are 580 (320 Non-defaulted, 260 Defaulted) firms listed in the Indian stock exchange. The period of research commences from 1st April 2004 and ends at 31st March 2019. The study incorporated 5 default prediction methods namely Multiple Discriminant Analysis, Altman Original, Calibrated, Logistic Regression, and Structural Model. The study developed models for each selected sector using MDA and Logistic Regression. The firm-specific sample data is collected from the 13 Indian sectors namely Chemicals, Construction and Engineering, Electronics, Hotels, Infrastructure, Pharmaceuticals, Plastic & Fibre, Realty, Software, Steel, Sugar, Textile and Miscellaneous. Further, the study amalgamated the sample cases of the selected 13 sectors into one group called Complete Sample. The study developed 28 (14 MDA and 14 Logit) default prediction models. The classification accuracy of the developed MDA model has been compared with Altman Original and Calibrated model for in-sample data. The developed MDA and developed Logit model is validated on the out-of-sample data for each selected sector and Complete Sample. The Structural Model which is based upon the option pricing method was applied to predict the default and non-default cases of each selected firm from 13 sectors and Complete Sample. Further, the study compared the in-sample classification results of developed MDA model, developed Logit model & Structural Model function. Alongside, the comparison of developed MDA and developed Logit Model also conducted for out-of-sample data. Additionally, the study performed the advanced default projection (foreward testing) in which the potential default events have been predicted for the time horizon: from 8 years before to 1 year before or within the actual default occurrence of the selected defaulted firms from the selected sectors.
The analysis deduced from the empirical results of the developed MDA models conveys that the model predicted defaulted cases as non-defaulted which generates a high value of Type II Errors that makes the model less effective. Amongst all developed MDA models, the results of the MDA model developed for Hotels sector depicted the considerable discriminatory and predictive power. The developed models have classified fairly the sample cases of the Construction and Engineering, Electronics, Hotels, Infrastructure, Pharmaceuticals, Plastic and Fibre and Software sectors. The MDA and Logit model have been developed using 21 and 23 independent variables respectively. The independent variables included in the study belong to accounting, market, economic and qualitative variables. The study found that in developed MDA model the following accounting variables performed significantly well namely NI/TA, WC/TA, EBIT/TA, TBD/TA, and RE/TA. The classification result of in-sample data demonstrated that the MDA model attained satisfactory predictive accuracies for Chemicals, Steel, Pharmaceuticals, Plastic & Fibre, Hotels and Electronics which range from 90% to 87% in conjunction with troublesome values of Type II Errors. The developed MDA models have been validated on the out-of-sample data of the selected sectors. The validation accuracy obtained by MDA model did not provide acceptable results except for Electronics and Sugar sectors that are 76% and 74% respectively.
The developed Logit model provided better results than the developed MDA model with all respects such as robustness, effectiveness, classification accuracy and the significance of the independent variables. The results of the various tests conducted on the developed Logit model suggested that all the independent variables have a significant impact on the dependent variables, the developed Logit model is found highly competent for Software sector and Complete Sample. However, the developed Logit model could only explain maximum 47% and 45% variation in the dependent variables of Software and Electronics sector which is highest amongst selected sectors. Whereas, the independent variables of the Construction and Engineering, Electronics, Hotels and Pharmaceuticals sector did explain the variation in the dependent variables with 71%, 70%, 71%, and 55% accuracies respectively. Further, the results suggest that the model is specified & best fitting in the selected sample data. The overall classification accuracies for the in-sample data attained by the model is quite satisfactory for Chemicals, Construction and Engineering, Electronics, Pharmaceuticals, Plastic and Fibre, and Steel sectors, none of these sectors have achieved less than 90% accuracy. There was negligible rate of Type I Errors for all the sectors, However, the Type II Error was very high for few selected sectors namely Realty, Textile, Chemicals and Complete Sample that range from 83% to 62%. The validation results highlighted that the Logit model outperformed the MDA model for the out-of-sample data as well. This model attained higher accuracy levels for Pharmaceuticals, Plastic and Fibre, and Complete Sample that arrayed from 92% to 89%. The most predictive independent variables found in the 14 developed Logit models are WC/TA, NI/TA, RE/TA, LOG (TA/GNP), and Y.
The Structural Model is applied to all selected sectors and Complete Sample. The classification results achieved by structure model are contrary to the results attained by MDA and Logit models. The Structural Model did achieve higher accuracies in the prediction of defaulted cases that range from 100% to 87% across the sectors and Complete Sample. However, the accuracies obtained while predicting the non-defaulted cases are not satisfactory rather it generated elevated values of Type I Error. The accuracy levels obtained in the prediction of the non-defaulted cases lies from 4% to 30% only; due to this, the overall accuracy of the Structural Model deteriorated. Therefore, the overall accuracy rate accomplished by the Structural Model arrayed from 18% to 35% only.
The result of the Advanced Default Projection (foreward testing) study advocates the superiority of the Structural Model over the developed MDA and developed Logit model. The Structural Model adequately diagnosed the potential default events even 8 years or 5 years before the actual default occurrence that too with high accuracy. The highest accuracy levels achieved by the Structural Model are 91%, 89% and 83% for Realty, Hotels and Construction and Engineering sectors respectively. The higher accuracy levels accomplished by the MDA model are 60%, 50% and 40% for Hotels, Construction and Engineering, Sugar sectors and Complete Sample. The developed Logit model did not perform well in advanced default projection study. This developed Logit model derived only 13%, 10% and 9% accuracies for the Chemicals, Software and Infrastructure sectors for the prediction of potential default event. Rather, the developed Logit model attained very high prediction accuracy for the “Failed” category of time horizon.