A Robust Data-Mining Approach to Bankruptcy Prediction

Authors


Mehdi Divsalar, Faculty of Management and Accounting, Allameh Tabatabai University, Tehran, Iran. E-mail: mehdi.divsalar@hotmail.com

ABSTRACT

In this study, new variants of genetic programming (GP), namely gene expression programming (GEP) and multi-expression programming (MEP), are utilized to build models for bankruptcy prediction. Generalized relationships are obtained to classify samples of 136 bankrupt and non-bankrupt Iranian corporations based on their financial ratios. An important contribution of this paper is to identify the effective predictive financial ratios on the basis of an extensive bankruptcy prediction literature review and upon a sequential feature selection analysis. The predictive performance of the GEP and MEP forecasting methods is compared with the performance of traditional statistical methods and a generalized regression neural network. The proposed GEP and MEP models are effectively capable of classifying bankrupt and non-bankrupt firms and outperform the models developed using other methods. Copyright © 2011 John Wiley & Sons, Ltd.

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