TITLE:
A Survival Approach to Prediction of Default Drivers for Indian Listed Companies
AUTHORS:
Vandana Gupta
KEYWORDS:
Default Prediction, Survival Analysis, Cox Proportional Hazard, KMV, Credit Risk
JOURNAL NAME:
Theoretical Economics Letters,
Vol.7 No.2,
February
4,
2017
ABSTRACT: The objective of the research study is to identify
the key predictors that can explain default
risk for Indian listed companies using survival analysis. The author has applied the semi-parametric Cox proportional
hazard model to test the impact of financial ratios, capital market
ratios, macro-economic variables, size and age of companies, and the ownership
structure of promoters to a dataset of 859
companies panning across 10 sectors. Unlike traditional models on default prediction, survival models focus
on “time to default” as the dependent variable. The empirical
findings reveal that return on capital employed
(ROCE), return on net worth (ROE), interest coverage ratio, exchange rate
volatility, GDP growth rate, stock index, promoters holdings % and the percent of shares pledged are all
significant predictors of default. Among the market variables, it is seen that beta and
the ratio of market value of equity/book value of debt are statistically
significant variables in explaining default risk. The empirical findings also
generate the hazard ratio for each covariate which examines the predicted
change in the hazard for a unit increase in
the predictor. The author extends the research by applying the market-based KMV
structural model to obtain continuous observations of default probability and regressing the same against all thecovariates (Gupta et al., 2013) [1]. It is
observed that the set of significant covariates are almost common to
those generated by our survival approach. The study concludes in emphasizing the significance of survival models in default prediction as unlike traditional accounting-based and market-based
models, these models assess relationship between survival time and
covariates. The application of survival models
is strongly recommended for credit risk evaluation and modeling as structuring of loans can be done by lenders by assessing the survival times of
different firms across the entire observation period being considered.