The Bayesian Additive Classification Tree Applied to Credit Risk Modelling
AbstractWe 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.
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Bibliographic InfoPaper provided by Sonderforschungsbereich 649, Humboldt University, Berlin, Germany in its series SFB 649 Discussion Papers with number SFB649DP2008-003.
Length: 24 pages
Date of creation: Jan 2008
Date of revision:
Classi¯cation and Regression Tree; Financial Ratio; Misclassification Rate; Accuracy Ratio;
Other versions of this item:
- Zhang, Junni L. & Härdle, Wolfgang K., 2010. "The Bayesian Additive Classification Tree applied to credit risk modelling," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 54(5), pages 1197-1205, May.
- 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
This paper has been announced in the following NEP Reports:
- NEP-ALL-2008-01-12 (All new papers)
- NEP-CMP-2008-01-12 (Computational Economics)
- NEP-DCM-2008-01-12 (Discrete Choice Models)
- NEP-ECM-2008-01-12 (Econometrics)
- NEP-ORE-2008-01-12 (Operations Research)
- NEP-RMG-2008-01-12 (Risk Management)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Wolfgang Härdle & Rouslan Moro & Dorothea Schäfer, 2007.
"Estimating Probabilities of Default With Support Vector Machines,"
SFB 649 Discussion Papers, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany
SFB649DP2007-035, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
- Härdle, Wolfgang Karl & Moro, Rouslan A. & Schäfer, Dorothea, 2007. "Estimating probabilities of default with support vector machines," Discussion Paper Series 2: Banking and Financial Studies, Deutsche Bundesbank, Research Centre 2007,18, Deutsche Bundesbank, Research Centre.
- Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 38(4), pages 367-378, February.
- Wolfgang Karl Härdle & Dedy Dwi Prastyo, 2013. "Default Risk Calculation based on Predictor Selection for the Southeast Asian Industry," SFB 649 Discussion Papers, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany SFB649DP2013-037, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
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