Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/79590 
Erscheinungsjahr: 
2012
Schriftenreihe/Nr.: 
SFB 649 Discussion Paper No. 2012-030
Verlag: 
Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk, Berlin
Zusammenfassung: 
Predicting default probabilities is at the core of credit risk management and is becoming more and more important for banks in order to measure their client's degree of risk, and for firms to operate successfully. The SVM with evolutionary feature selection is applied to the CreditReform database. We use classical methods such as discriminan analysis (DA), logit and probit models as benchmark On overall, GA-SVM is outperforms compared to the benchmark models in both training and testing dataset.
Schlagwörter: 
SVM
Genetic algorithm
global optmimum
default prediction
JEL: 
C14
C45
C61
C63
G33
Dokumentart: 
Working Paper

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