An Empirical Analysis of Default Risk for Listed Companies in India: A Comparison of Two Prediction Models

The objective of this study is to examine the performance of two default prediction models: the Z-score model using discriminant analysis, and the logit model on a dataset of 60 defaulted and 60 solvent companies. Financial ratios obtained from corporate balance sheets are used as independent variables while solvent/defaulted company (ratings assigned) is the dependent variable. Furthermore, for logistic regressions, an attempt is made to combine macro variables and dummy industry variables along with accounting ratios. The predictive ability of the proposed Z score model is higher when compared to both the Altman original Z-score model and the Altman model for emerging markets. The research findings establish the superiority of logit model over discriminant analysis and demonstrate the significance of accounting ratios in predicting default. Every business entity undertakes a variety of operational activities to carry on business. By necessity, the outcome of at least some of such activities may be unpredictable. This introduces an element of risk for every organization. Among the different risks that an organization is faced with, credit risk is perhaps one of the oldest financial risks, though there have not been many instruments to manage and hedge this type of risk till recently. Earlier, the focus had been primarily on market risk and bulk of the academic research was focused on this risk. However, there has been an upsurge in research on credit risk with increasing emphasis being given to its modeling and evaluation. Credit risk pervades virtually all financial transactions and includes a wide array of functions from agency downgrades to failure to service debt liquidation. With the innovations in new financial instruments, risk management techniques and with the global meltdown, credit risk has assumed prominence. At the centre of credit risk is the risk of default: implying failure on the part of a corporate to service the debt obligation. In the emerging markets like India, credit rating agencies (CRAs) have been the predominant source for assessing the credit quality of borrowers. Since upgrades and downgrades of ratings can impact the price of traded debt and equity, market participants are interested in developing good forecasting models. With the implementation of Basel II norms globally, banks are increasingly developing their own internal ratings-based models; developing internal scores. However, a credit rating or a credit score is not the same as directly estimating the probability of default. Thus, academicians and practitioners across the world advocate improvements in methodology and applications of credit ratings and also propose several credit risk models to predict default on debt obligations. With the movement of financial markets towards a more quantitative methodology and the constantly growing number of credit instruments, there is an increasing need felt for quantitative models to help analyze and mitigate this risk. Despite a plethora of mathematical models available, there has been little effort, specifically in an emerging market economy such as India to develop a default prediction model. Thus, a default prediction model that can quantify the credit risk by predicting the probability that a corporate defaults in meeting the financial obligation can be specifically useful to the lenders. Traditionally the credit risk literature has taken two approaches to measure default on debt. One is the structural approach which is based on market variables, and the second is the statistical approach or the reduced approach which factors in information from the financial statements.