The bayesian additive classification tree applied to credit risk modelling

Junni L. Zhang and Wolfgang Härdle

No 2008-003, SFB 649 Discussion Papers from Humboldt University Berlin, Collaborative Research Center 649: Economic Risk

Abstract: We propose a new nonlinear classification method based on a Bayesian sum-of-trees model, the Bayesian Additive Classification Tree (BACT), which extends the Bayesian Additive Regression Tree (BART) method into the classification context. Like BART, the BACT is a Bayesian nonparametric additive model specified by a prior and a likelihood in which the additive components are trees, and it is fitted by an iterative MCMC algorithm. Each of the trees learns a different part of the underlying function relating the dependent variable to the input variable, but the sum of the trees offers a flexible and robust model. Through several benchmark examples, we show that the BACT has excellent performance. This practical example is very important for banks to construct their risk profile and operate successfully. We use the German Creditreform database and classify the solvency status of German firms based on financial statement information. We show that the BACT outperforms the logit model, CART and the Support Vector Machine in identifying insolvent firms.

Keywords: Classification and Regression Tree; Financial Ratio; Misclassification Rate; Accuracy Ratio (search for similar items in EconPapers)
JEL-codes: C01 C11 C14 C45 (search for similar items in EconPapers)
Date: 2008
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