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


  • Dean P. Foster
  • Robert A. Stine


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.

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  • Dean P. Foster & Robert A. Stine, 2001. "Variable Selection in Data Mining: Building a Predictive Model for Bankruptcy," Center for Financial Institutions Working Papers 01-05, Wharton School Center for Financial Institutions, University of Pennsylvania.
  • Handle: RePEc:wop:pennin:01-05

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    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. Barrios, Erniel B. & Mina, Christian D., 2009. "Profiling Poverty with Multivariate Adaptive Regression Splines," Discussion Papers DP 2009-29, Philippine Institute for Development Studies.
    3. 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.

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