Using a comprehensive sample of 2,585 bankruptcies from 1990 to 2019, we benchmark the performance of various machine learning models in predicting financial distress of publicly traded U.S. firms. We find that gradient boosted trees outperform other models in one-year-ahead forecasts. Variable...

Using a dynamic model of strategic bargaining between equity and debt holders following default, we analyze the impact of shareholder bargaining power on the investment effects of debt overhang. Our empirical tests utilize a new measure of debt overhang wedge based on default probabilities...

In this paper we revisit the question of relative performance evaluation (RPE) in executive compensation. While previous literature has commonly rejected the use of RPE when using equity returns as performance measure, we argue that the total return of the firm is a preferable metric in RPE...

Credit rating agencies (CRAs) contend their ratings contain a quantitative assessment based on hard information, and a qualitative adjustment based on private information. We study if the qualitative portion of ratings, generated with the companies' own private information, contain valuable...

It is common to estimate equity betas for private firms or non-traded assets through a comparable company analysis, we test if the Random Forest algorithm can provide superior forecasts. In out-of-sample tests from 1992 to 2018, we find that Random Forest forecasts produce substantially lower...