Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/79590
Authors: 
Härdle, Wolfgang Karl
Prastyo, Dedy Dwi
Hafner, Christian
Year of Publication: 
2012
Series/Report no.: 
SFB 649 Discussion Paper 2012-030
Abstract: 
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.
Subjects: 
SVM
Genetic algorithm
global optmimum
default prediction
JEL: 
C14
C45
C61
C63
G33
Document Type: 
Working Paper

Files in This Item:
File
Size
936.15 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.