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

Author

Listed:
  • Manel Hamdi

    (International Finance Group Tunisia, El Manar University, Tunisia)

  • Sami Mestiri

    (Applied Economics and Simulation, Monastir University, Tunisia)

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.

Suggested Citation

  • Manel Hamdi & Sami Mestiri, 2014. "Bankruptcy prediction for Tunisian firms : An application of semi-parametric logistic regression and neural networks approach," Economics Bulletin, AccessEcon, vol. 34(1), pages 133-143.
    • Handle: RePEc:ebl:ecbull:eb-13-00802
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    References listed on IDEAS

    as
    1. Zhang, Guoqiang & Y. Hu, Michael & Eddy Patuwo, B. & C. Indro, Daniel, 1999. "Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis," European Journal of Operational Research, Elsevier, vol. 116(1), pages 16-32, July.
    2. Deakin, Eb, 1972. "Discriminant Analysis Of Predictors Of Business Failure," Journal of Accounting Research, Wiley Blackwell, vol. 10(1), pages 167-179.
    3. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    4. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    5. Juliana Yim & Heather Mitchell, 2005. "A comparison of corporate distress prediction models in Brazil: hybrid neural networks, logit models and discriminant analysis," Nova Economia, Economics Department, Universidade Federal de Minas Gerais (Brazil), vol. 15(1), pages 73-93, January-A.
    6. McNelis, Paul D., 2004. "Neural Networks in Finance," Elsevier Monographs, Elsevier, edition 1, number 9780124859678.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Šlefendorfas Gediminas, 2016. "Bankruptcy Prediction Model for Private Limited Companies of Lithuania," Ekonomika (Economics), Sciendo, vol. 95(1), pages 134-152, January.
    2. Manel Hamdi & Sami Mestiri & Adnène Arbi, 2024. "Artificial Intelligence Techniques for Bankruptcy Prediction of Tunisian Companies: An Application of Machine Learning and Deep Learning-Based Models," JRFM, MDPI, vol. 17(4), pages 1-14, March.
    3. Mestiri, Sami & Farhat, Abdejelil, 2018. "Credit Risk Prediction based on Bayesian estimation of logistic regression model with random effects," MPRA Paper 119960, University Library of Munich, Germany.

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    More about this item

    Keywords

    Bankruptcy prediction; semi-parametric logistic regression; artificial neural networks;
    All these keywords.

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • G0 - Financial Economics - - General

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