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

Acheampong, A ORCID: https://orcid.org/0000-0002-9489-5751 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: 101303. ISSN 1042-4431

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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.

Item Type:	Journal article
Publication Title:	Journal of International Financial Markets, Institutions and Money
Creators:	Acheampong, A. and Elshandidy, T.
Publisher:	Elsevier BV
Date:	November 2021
Volume:	75
ISSN:	1042-4431
Identifiers:	Number Type 10.1016/j.intfin.2021.101303 DOI 1849066 Other
Divisions:	Schools > Nottingham Business School
Record created by:	Jeremy Silvester
Date Added:	15 Mar 2024 11:43
Last Modified:	15 Mar 2024 11:45
URI:	https://irep.ntu.ac.uk/id/eprint/51088

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