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Combining accounting data and a structural model for predicting credit ratings: Empirical evidence from European listed firms

Michael Doumpos, Dimitrios Niklis, Constantin Zopounidis and Kostas Andriosopoulos

Journal of Banking & Finance, 2015, vol. 50, issue C, 599-607

Abstract: Ratings issued by credit rating agencies (CRAs) play an important role in the global financial environment. Among other issues, past studies have explored the potential for predicting these ratings using a variety of explanatory factors and modeling approaches. This paper describes a multi-criteria classification approach that combines accounting data with a structural default prediction model in order to obtain improved predictions and test the incremental information that a structural model provides in this context. Empirical results are presented for a panel data set of European listed firms during the period 2002–2012. The analysis indicates that a distance-to-default measure obtained from a structural model adds significant information compared to popular financial ratios. Nevertheless, its power is considerably weakened when market capitalization is also considered. The robustness of the results is examined over time and under different rating category specifications.

Keywords: Credit ratings; Rating agencies; Black–Scholes–Merton model; Multi-criteria decision making (search for similar items in EconPapers)
JEL-codes: C44 G24 G13 (search for similar items in EconPapers)
Date: 2015
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Handle: RePEc:eee:jbfina:v:50:y:2015:i:c:p:599-607