Measuring credit risk using qualitative disclosure

John Donovan (), Jared Jennings (), Kevin Koharki () and Joshua Lee ()
Additional contact information
John Donovan: University of Notre Dame
Jared Jennings: Washington University in St. Louis
Kevin Koharki: Purdue University
Joshua Lee: Brigham Young University

Review of Accounting Studies, 2021, vol. 26, issue 2, No 11, 815-863

Abstract: 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 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.

Keywords: Credit risk; Disclosure; Machine-learning; Textual analysis; G20; G23; G30; G32; G33; M40; M41 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

Downloads: (external link)
http://link.springer.com/10.1007/s11142-020-09575-4 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:reaccs:v:26:y:2021:i:2:d:10.1007_s11142-020-09575-4

Ordering information: This journal article can be ordered from
http://www.springer.com/accounting/journal/11142

DOI: 10.1007/s11142-020-09575-4

Access Statistics for this article

Review of Accounting Studies is currently edited by Paul Fischer

More articles in Review of Accounting Studies from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().