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...

This study examines the distribution of extreme values in daily currency changes for nine Asian countries. Using an improved estimator, extreme changes in Asian currencies can generally be represented by Frechet distributions. Our results are robust to the choice of the numeraire currency, the...

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...

We conduct a thorough analysis on the role played by the unobserved systematic risk factor in default prediction. We find that this latent factor outweighs the observed systematic risk factors and can substantially improve the in-sample predictive accuracy at the firm, rating group, and...