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The Bayesian Additive Classification Tree applied to credit risk modelling

Author

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  • Zhang, Junni L.
  • Härdle, Wolfgang K.

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 variables, but the sum of the trees offers a flexible and robust model. Through several benchmark examples, we show that the BACT shows 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 is a serious competitor to the logit model, CART, the Support Vector Machine, random forest and gradient boosting.

Suggested Citation

  • Zhang, Junni L. & Härdle, Wolfgang K., 2010. "The Bayesian Additive Classification Tree applied to credit risk modelling," Computational Statistics & Data Analysis, Elsevier, vol. 54(5), pages 1197-1205, May.
    • Handle: RePEc:eee:csdana:v:54:y:2010:i:5:p:1197-1205
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    References listed on IDEAS

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    1. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
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    Cited by:

    1. Ying Chen & Yangkai Guo & Maoguo Wu, 2020. "A Simplified Variable Analysis of Credit Ratings for Small Chinese Enterprises Based on Support Vector Machine," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 12(6), pages 1-45, June.
    2. Hamidreza Arian & Seyed Mohammad Sina Seyfi & Azin Sharifi, 2020. "Forecasting Probability of Default for Consumer Loan Management with Gaussian Mixture Models," Papers 2011.07906, arXiv.org.
    3. Wolfgang Karl Härdle & Dedy Dwi Prastyo, 2013. "Default Risk Calculation based on Predictor Selection for the Southeast Asian Industry," SFB 649 Discussion Papers SFB649DP2013-037, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    4. Wolfgang Karl Härdle & Dedy Dwi Prastyo & Christian Hafner, 2012. "Support Vector Machines with Evolutionary Feature Selection for Default Prediction," SFB 649 Discussion Papers SFB649DP2012-030, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    5. Lamprinakou, Stamatina & Barahona, Mauricio & Flaxman, Seth & Filippi, Sarah & Gandy, Axel & McCoy, Emma J., 2023. "BART-based inference for Poisson processes," Computational Statistics & Data Analysis, Elsevier, vol. 180(C).

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