Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/50001
Authors: 
Förstemann, Till
Year of Publication: 
2011
Series/Report no.: 
Discussion Paper Series 2: Banking and Financial Studies 2011,11
Abstract: 
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.
Subjects: 
Credit Risk
Credit Rating
Probability of Default
Logistic Regression
JEL: 
G21
G33
C52
ISBN: 
978-3-86558-745-9
Document Type: 
Working Paper

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