Forecasting credit ratings of EU banks - Vasilios Plakandaras; Periklēs Gkonkas; Theophilos Papadimitriou; Efterpi Doumpa; Maria Stefanidou (International Journal of Financial Studies, 2020)
The aim of this study is to forecast credit ratings of E.U. banking institutions, as dictated by Credit Rating Agencies (CRAs). To do so, we developed alternative forecasting models that determine the non-disclosed criteria used in rating. We compiled a sample of 112 E.U. banking institutions, including their Fitch assigned ratings for 2017 and the publicly available information from their corresponding financial statements spanning the period 2013 to 2016, that lead to the corresponding ratings. Our assessment is based on identifying the financial variables that are relevant to forecasting the ratings and the rating methodology used. In the empirical section, we employed a vigorous variable selection scheme prior to training both Probit and Support Vector Machines (SVM) models, given that the latter originates from the area of machine learning and is gaining popularity among economists and CRAs. Our results show that the most accurate, in terms of in-sample forecasting, is an SVM model coupled with the nonlinear RBF kernel that identifies correctly 91.07% of the banks' ratings, using only 8 explanatory variables. Our findings suggest that a forecasting model based solely on publicly available financial information can adhere closely to the official ratings produced by Fitch. This provides evidence that the actual assessment procedures of the Credit Rating Agencies can be fairly accurately proxied by forecasting models based on freely available data and information on undisclosed information is of lower importance.
Keywords: 330; credit ratings; machine learning; Support Vector Machines; banks