Measuring credit risk using qualitative disclosure

We use machine learning methods to create a comprehensive measure of credit risk based on qualitative information disclosed in conference calls and in management's discussion and analysis section of the 10-K. In out-of-sample tests, we find that our measure improves the ability to predict credit events (bankruptcies, interest spreads, and credit rating downgrades), relative to credit risk measures developed by prior research (e.g., z-score). We also find our measure based on conference calls explains within-firm variation in future credit events; however, we find little evidence that the measures of credit risk developed by prior research explain within-firm variation in credit risk. Our measure has utility for both academics and practitioners, as the majority of firms do not have readily available measures of credit risk, such as actively-traded CDS or credit ratings. Our study also adds to the growing body of research using machine-learning methods to gather information from conference calls and MD&A to explain key outcomes.

Automated summary

Machine learning methods can be utilized to create a comprehensive credit risk assessment based on information disclosed in conference calls and management's discussion and analysis section. This improved ability to predict credit events is beneficial for both academics and practitioners.