UC Berkeley

In the literature of predicting corporate default, it is an ad-hoc process to select the predictors and different models often use different predictors. We study the predictors of U.S corporate default by Forward Stepwise and Lasso model selection methods. Out of 30 candidate default predictors that have been used in the default-predicting literature, we identify a set of eight default predictors that have strong effects in predicting default using the U.S corporate default data from 1984-2009. We compare the eight default predictors' predicting effect over the past three major economic recessions and find that the recession in early 1990 and the recent sub-prime mortgage crisis share some common default characteristics, while the recession in 2000 is different from the other two. We then present a decision-based default prediction framework where we incorporate the default forecaster's loss utility into default classification and derive an optimal decision rule for this classification problem. By combining the default forecaster's loss utility into Support Vector Machines(SVMs), we show that minimizing the utility adjusted hinge loss is consistent with minimizing utility adjusted classification loss. Our empirical classification result of the decision-based Support Vector Machines demonstrates more classification accuracy and flexibilities in meeting different default forecasters' goals in comparison to traditional statistical methods.