The Bayesian Additive Classification Tree Applied to Credit Risk Modelling

Junni L. Zhang and Wolfgang Karl Härdle

No SFB649DP2008-003, SFB 649 Discussion Papers from Humboldt University, Collaborative Research Center 649

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 classi- fication 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 variables, but the sum of the trees offers a flexible and robust model. Through several benchmark examples, we show that the BACT has excellent performance. We apply the BACT technique to classify whether firms would be insolvent. 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: Classi¯cation and Regression Tree; Financial Ratio; Misclassification Rate; Accuracy Ratio (search for similar items in EconPapers)
JEL-codes: C14 C11 C45 C01 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cmp, nep-dcm, nep-ecm, nep-ore and nep-rmg
Date: 2008-01
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