Advanced Search
MyIDEAS: Login to save this paper or follow this series

The Bayesian Additive Classification Tree Applied to Credit Risk Modelling

Contents:

Author Info

  • Junni L. Zhang
  • Wolfgang Härdle

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.

Download Info

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
File URL: http://sfb649.wiwi.hu-berlin.de/papers/pdf/SFB649DP2008-003.pdf
Download Restriction: no

Bibliographic Info

Paper provided by Sonderforschungsbereich 649, Humboldt University, Berlin, Germany in its series SFB 649 Discussion Papers with number SFB649DP2008-003.

as in new window
Length: 24 pages
Date of creation: Jan 2008
Date of revision:
Handle: RePEc:hum:wpaper:sfb649dp2008-003

Contact details of provider:
Postal: Spandauer Str. 1,10178 Berlin
Phone: +49-30-2093-5708
Fax: +49-30-2093-5617
Email:
Web page: http://sfb649.wiwi.hu-berlin.de
More information through EDIRC

Related research

Keywords: Classi¯cation and Regression Tree; Financial Ratio; Misclassification Rate; Accuracy Ratio;

Other versions of this item:

Find related papers by JEL classification:

This paper has been announced in the following NEP Reports:

References

References listed on IDEAS
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.:
as in new window
  1. 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.
  2. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 38(4), pages 367-378, February.
Full references (including those not matched with items on IDEAS)

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as in new window

Cited by:
  1. 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.

Lists

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

Statistics

Access and download statistics

Corrections

When requesting a correction, please mention this item's handle: RePEc:hum:wpaper:sfb649dp2008-003. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (RDC-Team).

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.