Machine learning methods in asset pricing are often criticized for their black box nature. We study this issue by predicting corporate bond returns using interpretable machine learning on a high-dimensional bond characteristics dataset. We achieve state-of-the-art performance while maintaining...

We analyze the costs and benefits of intermediaries for government-sponsored enterprise (GSE) mortgages using regulatory data. We find evidence of lenders pricing for observable and unobservable default risk independently from the GSEs. These findings are explained using a model of competitive...

Implementing a state-of-the-art machine learning technique for causal identification from text data (C-TEXT), we document that patents authored by female inventors are under-cited relative to those authored by males. Relative to what the same patent would be predicted to receive had the lead...

This paper studies how tightening monetary policy transmits to the economy through the mortgage market and sheds new light on the distributional consequences at both the individual and regional levels. We find that credit supply factors, specifically restrictions on the debt-to-income (DTI)...