CART-based selection of bankruptcy predictors for the logit model

Authors:
Arjana Brezigar-Masten;Igor Masten
Affiliations:
University of Primorska, Faculty of Mathematics, Natural Sciences and Information Technology, Glagoljaška 8, SI-6000 Koper, Slovenia and Institute of Macroeconomic Analysis and Development, G ...;University of Ljubljana, Faculty of Economics, Kardeljeva pl. 17, SI-1000 Ljubljana, Slovenia
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

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Abstract

Balance-sheet data offer a potentially large number of candidate predictors of corporate financial failure. In this paper we provide a novel predictor selection procedure based on non-parametric regression and classification tree method (CART) and test its performance within a standard logit model. We show that a simple logit model with dummy variables created in accordance with the nodes of estimated classification tree outperforms both standard logit model with step-wise-selected financial ratios, and CART itself. On a population of Slovenian companies our method achieves remarkable rates of precision in out-of-sample bankruptcy prediction. Our selection method thus represents an efficient way of introducing non-linear effects of predictor variables on the default probability in standard single-index models like logit. These findings are robust to choice-based sampling of estimation samples.