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Rst–Gcbr‐Clustering‐Based Rga–Svm Model For Corporate Failure Prediction

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  • Fedya Telmoudi
  • Mohamed El Ghourabi
  • Mohamed Limam

Abstract

Corporate bankruptcy is perceived as a shocking event. Several researchers focused on the prediction of these phenomena using various methods aiming to avoid high generated costs. In this paper, a new hybrid approach is proposed to deal with corporate failure prediction. Based on financial ratios as input data and in order to predict if the business unit will fail or not, our approach integrates rough set theory, Gaussian case‐based reasoning‐clustering, real‐valued genetic algorithm with support vector machines. This combination is justified by a high accuracy rate, reaching 100% at 1 year before failure and 94.0925% at 3 years before failure. Copyright © 2011 John Wiley & Sons, Ltd.

Suggested Citation

  • Fedya Telmoudi & Mohamed El Ghourabi & Mohamed Limam, 2011. "Rst–Gcbr‐Clustering‐Based Rga–Svm Model For Corporate Failure Prediction," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 18(2-3), pages 105-120, April.
    • Handle: RePEc:wly:isacfm:v:18:y:2011:i:2-3:p:105-120
    DOI: 10.1002/isaf.323
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    References listed on IDEAS

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