Corporate Default Prediction with Industry Effects: Evidence from Emerging Markets

Abstract

The accurate prediction of corporate bankruptcy for the firms in different industries is of a great concern to investors and creditors. Firm-specific data accompany with industry and macroeconomic factors offer a potentially large number of candidate predictors of corporate default. We employ a predictor selection procedure based on non-parametric regression and classification tree method (CART) and test its performance within a standard logistic regression model. Overall entire analyses indicate that the orientation between firm-level determinants and the probability of default is affected by each industry's characteristics. As well, our selection method represents an efficient way of introducing non-linear effects of predictor variables on the default probability.