Article2011

RST–GCBR‐CLUSTERING‐BASED RGA–SVM MODEL FOR CORPORATE FAILURE PREDICTION

Fedya Telmoudi · Mohamed El Ghourabi · Mohamed Limam

20 citations0/0 files storedIntelligent systems in accounting, finance and management/Intelligent systems in accounting, finance & management

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Financial Distress and Bankruptcy Prediction Artificial Intelligence in Healthcare Data Mining and Machine Learning Applications

Abstract

SUMMARY 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.

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References

1968FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY 1966Financial Ratios As Predictors of Failure 1988SCRIPTS, PLANS, GOALS, AND UNDERSTANDING 1984Methodological Issues Related to the Estimation of Financial Distress Prediction Models 2001Application of support vector machines in financial time series forecasting 2006Bankruptcy prediction in banks and firms via statistical and intelligent techniques – A review 2005Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters 1990A neural network model for bankruptcy prediction

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