Bankruptcy prediction for Tunisian firms: An application of semi-parametric logistic regression and neural networks approach

Manel Hamdi () and Sami Mestiri
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Manel Hamdi: International Finance Group Tunisia, El Manar University, Tunisia

Economics Bulletin, 2014, vol. 34, issue 1, 133-143

Abstract: The paper uses two approaches, semi-parametric logistic regression model and artificial neural networks, to predict bankruptcy of Tunisian companies. A sample of 528 Tunisian firms for the period (1999-2006), was used to investigate the performance of these two approaches. The empirical results indicate that the quality of model prediction of the neural networks is better than the semi-parametric logistic regression model in terms of comparing the rates of misclassification and the area under curve (AUC) measures of the two proposed techniques. This research concludes that neural nets are a very powerful tool in bankruptcy prediction.

Keywords: Bankruptcy prediction; semi-parametric logistic regression; artificial neural networks (search for similar items in EconPapers)
JEL-codes: C5 G0 (search for similar items in EconPapers)
Date: 2014-01-30
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Citations: View citations in EconPapers (3)

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