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A Robust Data-Mining Approach to Bankruptcy Prediction

โœ Scribed by Mehdi Divsalar; Habib Roodsaz; Farshad Vahdatinia; Ghassem Norouzzadeh; Amir Hossein Behrooz

Publisher
John Wiley and Sons
Year
2011
Tongue
English
Weight
465 KB
Volume
31
Category
Article
ISSN
0277-6693

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โœฆ Synopsis

ABSTRACT

In this study, new variants of genetic programming (GP), namely gene expression programming (GEP) and multiโ€expression programming (MEP), are utilized to build models for bankruptcy prediction. Generalized relationships are obtained to classify samples of 136 bankrupt and nonโ€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 nonโ€bankrupt firms and outperform the models developed using other methods. Copyright ยฉ 2011 John Wiley & Sons, Ltd.

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