Corporate Default Prediction with Industry Effects: Evidence from Emerging Markets
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.