Keywords

credit risk, disclosure, machine-learning, textual analysis

Abstract

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 our ability to predict future credit events (future bankruptcies, future interest spreads, and future credit rating downgrades) relative to existing 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 existing 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.

Original Publication Citation

Donovan, J., J. Jennings, K. Koharki, and J. Lee. 2021. "Measuring Credit Risk Using Qualitative Disclosure," Review of Accounting Studies, 26, 815-863.

Document Type

Peer-Reviewed Article

Publication Date

2021

Publisher

Review of Accounting Studies

Language

English

College

Marriott School of Business

Department

Accountancy

University Standing at Time of Publication

Full Professor

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Accounting Commons

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