Improvements in rating models for the German corporate sector - Till Förstemann (Deutsche Bundesbank, 2011)
Group-specific estimations can significantly improve the predictive power of accountingbased rating models. This is shown using a binary logistic regression model applied to the Deutsche Bundesbank's USTAN dataset, which contains 300,000 financial statements provided by German companies for the years 1994 to 2002, i. e. throughout a complete business-cycle. The robustness and the representability of this result is verified through out-of-sample tests and through comparisons with a benchmark model which applies the variables of Moody's RiskCalcTM for Germany.
Keywords: G21; G33; C52; 330; Credit Risk; Credit Rating; Probability of Default; Logistic Regression