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The bayesian additive classification tree applied to credit risk modelling

Author

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  • Zhang, Junni L.
  • Härdle, Wolfgang Karl

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.

Suggested Citation

  • Zhang, Junni L. & Härdle, Wolfgang Karl, 2008. "The bayesian additive classification tree applied to credit risk modelling," SFB 649 Discussion Papers 2008-003, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    • Handle: RePEc:zbw:sfb649:sfb649dp2008-003
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    References listed on IDEAS

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    1. Härdle, Wolfgang Karl & Moro, Rouslan A. & Schäfer, Dorothea, 2007. "Estimating probabilities of default with support vector machines," SFB 649 Discussion Papers 2007-035, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    2. Härdle, Wolfgang Karl & Moro, Rouslan A. & Schäfer, Dorothea, 2007. "Estimating probabilities of default with support vector machines," SFB 649 Discussion Papers 2007-035, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
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    More about this item

    Keywords

    Classification and Regression Tree; Financial Ratio; Misclassification Rate; Accuracy Ratio;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics

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