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Does soft information determine credit risk? Text-based evidence from European banks

Published in Elsevier

This paper uses a supervised machine learning algorithm to extract relevant (soft) information from annual reports and examines whether such information determines credit risk (as measured by non-performing loans, Ohlson’s O-score, Altman’s Z-score, and credit rating downgrades). The paper also assesses how far both bank- and country-level characteristics influence variations in credit risks both within and between banks across 19 European countries between 2005 and 2017. Based on 1885 firm-year observations, we find that the text-based credit risk (soft) measure explains a substantial portion of the variation in NPLs, O-score, Z-score, and credit rating downgrades. We also find that bank-level characteristics and country-level characteristics are highly important for explaining variations in non-performing loans, O-score, and credit rating downgrades, as compared to Z-score. Overall, our results have implications for firms, regulators, and market participants who are seeking evidence on the credibility of annual reports in conveying relevant information that reflects actual credit risk.

About the journal
JournalData powered by TypesetJournal of International Financial Markets, Institutions and Money
PublisherData powered by TypesetElsevier
Open AccessNo