Analyzing Credit Risk Data: A Comparison of Logistic Discrimination, Classification Tree Analysis, and Feedforward Networks

COMPUTATIONAL STATISTICS, Vol 12 No. 2, March 26, 1997

Posted: 8 Apr 1997

See all articles by Gerhard Arminger

Gerhard Arminger

Bergische Universitat

Daniel Enache

Bergische Universitat

Thorsten Bonne

Bergische Universitat

Abstract

Three different discriminant techniques are applied and compared to analyze a complex data set of credit risks. A large sample is split into a training, a validation, and a test sample. The dependent variable is whether a loan is paid back without problems or not. Predictor variables are sex, job duration, age, car ownership, telephone ownership, and marital status. The statistical techniques are logistic discriminant analysis with a simple mean effects model, classification tree analysis, and a feedforward network with one hidden layer consisting of three units. It turns out, that in the given test sample, the predictive power is about equal for all techniques with the logistic discrimination as the best technique. However, the feedforward network produces different classification rules from the logistic discrimination and the classification tree analysis. Therefore, an additional coupling procedure for forecasts is applied to produce a combined forecast. However, this forecast turns out to be slightly worse than the logit model.

JEL Classification: C25, C35

Suggested Citation

Arminger, Gerhard and Enache, Daniel and Bonne, Thorsten, Analyzing Credit Risk Data: A Comparison of Logistic Discrimination, Classification Tree Analysis, and Feedforward Networks. COMPUTATIONAL STATISTICS, Vol 12 No. 2, March 26, 1997. Available at SSRN: https://ssrn.com/abstract=4801

Gerhard Arminger

Bergische Universitat

42097 Wuppertal
Germany

Daniel Enache (Contact Author)

Bergische Universitat ( email )

42097 Wuppertal
Germany

Thorsten Bonne

Bergische Universitat

42097 Wuppertal
Germany

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