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

Albert Acheampong and Tamer Elshandidy

Journal of International Financial Markets, Institutions and Money, 2021, vol. 75, issue C

Abstract: 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.

Keywords: Credit risk; Machine learning; Repeated measures multilevel analysis; Corporate disclosure (search for similar items in EconPapers)
JEL-codes: G21 G28 G32 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfin:v:75:y:2021:i:c:s1042443121000226

DOI: 10.1016/j.intfin.2021.101303

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