Combining accounting data and a structural model for predicting credit ratings: empirical evidence from European listed firmsCombining accounting data and a structural model for predicting credit ratings: empirical evidence from European listed firms Peer-Reviewed Journal Publication Δημοσίευση σε Περιοδικό με Κριτές 2015-11-052015enRatings 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.http://creativecommons.org/licenses/by/4.0/Journal of Banking and Finance50599-607 Michael Doumpos Δουμπος Μιχαλης Niklis Dimitrios Νικλης Δημητριος Zopounidis Konstantinos Ζοπουνιδης Κωνσταντινος Andriosopoulos Kostas Elsevier Credit ratings Rating agencies Black–Scholes–Merton model Multi-criteria decision making