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...

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...

Inspired by the linear predictability and nonlinearity found in the finance literature, this article examines the nonlinear predictability of the excess returns. The relationship between the excess returns and the predicting variables is recursively modeled by a neural-network model, which is...