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

Acheampong, A. K. and Elshandidy, T. (2021) Does soft information determine credit risk? Text-based evidence from European banks*. Journal of International Financial Markets, Institutions and Money. 75 1042-4431.

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Item Type:	Article
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.
Creators:	Acheampong, Albert Kwame and Elshandidy, Tamer
Faculties, Divisions and Institutes:	Faculties > Faculty of Business & Law > Accounting & Finance
Date:	25 November 2021
Date Type:	Publication
Journal or Publication Title:	Journal of International Financial Markets, Institutions and Money
Volume:	75
Language:	English
DOI:	https://doi.org/10.1016/j.intfin.2021.101303
ISSN:	1042-4431
Status:	Published / Disseminated
Refereed:	Yes
Related URLs:	https://www.sciencedirect.com/science/ar... https://pure.northampton.ac.uk/en/public...
URI:	http://nectar.northampton.ac.uk/id/eprint/16949

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