We apply multiple machine learning (ML) methods to model loss given default (LGD) for corporate debt using a common dataset that is cross-sectional but collected over different time periods and shows much variation over time. We investigate the efficacy of three cross-validation (CV) schemes for...

Using a unique and comprehensive dataset of loan-level home equity lines of credit serviced by large US national banks, we confirm that default risk of home equity lines of credit increases at end of draw. More importantly, we quantify the increase in default risk with the size of positive...

We provide the first empirical analysis on the effects of credit default swaps (CDS) on corporate distress resolution with a focus on debt recovery rate. CDS contracts are settled shortly after the occurrence of credit events such as restructuring or bankruptcy filings and, presumably, should...

We examine the impact of the unobservable systematic risk factor on default prediction model performance. We find that including the unobservable systematic risk factor might help improve predictive accuracy, but it might not help improve rank ordering of firms by default risk. Rank ordering is...