CART-based selection of bankruptcy predictors for the logit model |
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Authors: | Arjana Brezigar-Masten Igor Masten |
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Affiliation: | 1. Department of Computer Science, Faculty of Computer Science and Management, Wroc?aw University of Science and Technology, Wybrze?e Wyspiańskiego 27, 50-370, Wroc?aw, Poland;2. Department of Operations Research, Wroc?aw University of Science and Technology, Wybrze?e Wyspiańskiego 27, 50-370, Wroc?aw, Poland |
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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. |
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