Advanced Search
MyIDEAS: Login

Variable Selection in Data Mining: Building a Predictive Model for Bankruptcy

Contents:

Author Info

  • Dean P. Foster
  • Robert A. Stine

Abstract

We develop and illustrate a methodology for fitting models to large, complex data sets. The methodology uses standard regression techniques that make few assumptions about the structure of the data. We accomplish this with three small modifications to stepwise regression: (1) We add interactions to capture non-linearities and indicator functions to capture missing values; (2) We exploit modern decision theoretic variable selection criteria; and (3) We estimate standard error using a conservative approach that works for heteroscedastic data. Omitting any one of these modifications leads to poor performance. We illustrate our methodology by predicting the onset of personal bankruptcy among users of credit cards. This applications presents many challenges, ranging from the rare frequency of bankruptcy to the size of the available database. Only 2,244 bankruptcy events appear among some 3 million months of customer activity. To predict these, we begin with 255 features to which we add missing value indicators and pairwise interactions that expand to a set of over 67,000 potential predictors. From these, our method selects a model with 39 predictors chosen by sequentially comparing estimates of their significance to a series of thresholds. The resulting model not only avoids over-fitting the data, it also predicts well out of sample. To find half of the 1800 bankruptcies hidden in a validation sample of 2.3 million observations, one need only search the 8500 cases having the largest model predictions.

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://fic.wharton.upenn.edu/fic/papers/01/0105.pdf
Download Restriction: no

Bibliographic Info

Paper provided by Wharton School Center for Financial Institutions, University of Pennsylvania in its series Center for Financial Institutions Working Papers with number 01-05.

as in new window
Length:
Date of creation: Feb 2001
Date of revision:
Handle: RePEc:wop:pennin:01-05

Contact details of provider:
Postal: 3301 Steinberg Hall-Dietrich Hall, 3620 Locust Walk, Philadelphia, PA 19104.6367
Phone: 215.898.1279
Fax: 215.573.8757
Email:
Web page: http://fic.wharton.upenn.edu/fic/
More information through EDIRC

Related research

Keywords:

This paper has been announced in the following NEP Reports:

References

No references listed on IDEAS
You can help add them by filling out this form.

Citations

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

Cited by:
  1. Khandani, Amir E. & Kim, Adlar J. & Lo, Andrew W., 2010. "Consumer credit-risk models via machine-learning algorithms," Journal of Banking & Finance, Elsevier, vol. 34(11), pages 2767-2787, November.
  2. Alexandra Schwarz, 2011. "Measurement, Monitoring, and Forecasting of Consumer Credit Default Risk - An Indicator Approach Based on Individual Payment Histories," Schumpeter Discussion Papers sdp11004, Universit├Ątsbibliothek Wuppertal, University Library.
  3. Barrios, Erniel B. & Mina, Christian D., 2009. "Profiling Poverty with Multivariate Adaptive Regression Splines," Discussion Papers DP 2009-29, Philippine Institute for Development Studies.

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:wop:pennin:01-05. 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: (Thomas Krichel).

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.