Predicting Corporate Defaults: Evaluating Moody's Credit Rating Institute

Abstract: The ability of the Merton model and the logistic regression to accurately forecast corporate defaults is evaluated. Additionally, the probability-of-default (PD) estimates obtained from these two models are compared with the corresponding rating class historic default rates presented by Moody’s. Data for 56 defaulted and 272 healthy US publicly traded organizations serves as the basis for this study. Results reveal that: (i) the logistic regression is more accurate in distinguishing between defaulted and healthy companies, but provides overly conservative PD estimates; (ii) the Merton model struggles to correctly identify true defaults and true non-defaults, while providing default probabilities that are in line with historic default rates (iii) no framework was deemed superior in this context, ascertaining the difficulty associated with identifying the precise timing of a corporate default.