Corporate Default Prediction: Models, Drivers and Measurements
Thesis or dissertation
University of Exeter
Reason for embargo
To enable future publication of the research
This thesis identifies the optimal set of corporate default drivers and examines the prediction performance of corporate default measurement tools, using a sample of companies in the United States from 1970 to 2009. In the discussion of optimal default drivers, feature selection techniques including the t-test and stepwise methods are used to filter relevant default information collected from previous empirical studies. The optimal default driver information set consists of quantitative parameters from accounting ratios, market indices, macroeconomic indicators, default history, and firm age. While both accounting ratios and market information dominate the explanatory ability, followed by default history, macroeconomic indicators contribute additional explanation for default risk. Moreover, industry effects show significance across alternative models, with the retail industry presenting as the sector with highest risk. The results are robust in both traditional and advanced random models. In investigating the optimal prediction method, two newly developed random models, mixed logit and frailty model, are tested for their theoretical superiority in capturing default clusters and unobservable information for default risk. The prediction ability of both models has been improved upon using the extended optimal set of default drivers. While the mixed logit model provides better prediction accuracy and shows stability in robustness checks, the frailty model benefits from computational efficiency and explains default clusters more thoroughly. This thesis further compares the prediction performance of large dimensional models across five categories based on the default probabilities transferred from alternative results in different models. Besides the traditional assessment criteria - covering the receiver operating characteristic curve, accuracy ratios, and classification error rates – this thesis thoroughly evaluates forecasting performance using innovative proxies including model stability under financial crisis, profitability and misclassification costs for creditors using alternative risk measurements. The practical superiority of the two advanced random models has been verified further in the comparative study.
PhD in Finance