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• W3128501064 startingPage "101303" @default.
• W3128501064 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." @default.
• W3128501064 created "2021-02-15" @default.
• W3128501064 creator A5000053431 @default.
• W3128501064 creator A5062964168 @default.
• W3128501064 date "2021-11-01" @default.
• W3128501064 modified "2023-10-18" @default.
• W3128501064 title "Does soft information determine credit risk? Text-based evidence from European banks" @default.
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• W3128501064 doi "https://doi.org/10.1016/j.intfin.2021.101303" @default.
• W3128501064 hasPublicationYear "2021" @default.
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