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Neural network models and the prediction of bank bankruptcy

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  • Tam, KY
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    Abstract

    The number of failed banks has reached a high unparalleled since the great Depression. Research in developing predictive models for bank failures is therefore warranted and desirable in this turbulent period. In this paper, we present a neural network approach to bank failures prediction and compare its performance with existing models. Empirical results show that among alternative models, neural networks is a competitive instrument for evaluating the financial condition of a bank. The study concludes with a discussion on the potential and limitations of neural networks as a general modelling tool for financial applications.

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    File URL: http://www.sciencedirect.com/science/article/B6VC4-48M343B-C/2/f539e69932507acf122f4ee84515e54a
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    Bibliographic Info

    Article provided by Elsevier in its journal Omega.

    Volume (Year): 19 (1991)
    Issue (Month): 5 ()
    Pages: 429-445

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    Handle: RePEc:eee:jomega:v:19:y:1991:i:5:p:429-445

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    Related research

    Keywords: neural networks discriminant analysis bank failures prediction;

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    Cited by:
    1. Yuliya Demyanyk & Iftekhar Hasan, 2009. "Financial crises and bank failures: a review of prediction methods," Working Paper 0904, Federal Reserve Bank of Cleveland.
    2. Halil Erdal & Aykut Ekinci, 2013. "A Comparison of Various Artificial Intelligence Methods in the Prediction of Bank Failures," Computational Economics, Society for Computational Economics, vol. 42(2), pages 199-215, August.
    3. Younes Boujelbène & Sihem Khemakhem, 2013. "Prévision du risque de crédit : Une étude comparative entre l'Analyse Discriminante et l'Approche Neuronale," Working Papers hal-00905199, HAL.
    4. Dimitras, A. I. & Zanakis, S. H. & Zopounidis, C., 1996. "A survey of business failures with an emphasis on prediction methods and industrial applications," European Journal of Operational Research, Elsevier, vol. 90(3), pages 487-513, May.
    5. Barniv, Ran & Mehrez, Abraham & Kline, Douglas M., 2000. "Confidence intervals for controlling the probability of bankruptcy," Omega, Elsevier, vol. 28(5), pages 555-565, October.
    6. Westgaard, Sjur & van der Wijst, Nico, 2001. "Default probabilities in a corporate bank portfolio: A logistic model approach," European Journal of Operational Research, Elsevier, vol. 135(2), pages 338-349, December.
    7. Greta Falavigna, 2006. "Models for Default Risk Analysis: Focus on Artificial Neural Networks, Model Comparisons, Hybrid Frameworks," CERIS Working Paper 200610, Institute for Economic Research on Firms and Growth - Moncalieri (TO).
    8. Callen, Jeffrey L. & Kwan, Clarence C. Y. & Yip, Patrick C. Y. & Yuan, Yufei, 1996. "Neural network forecasting of quarterly accounting earnings," International Journal of Forecasting, Elsevier, vol. 12(4), pages 475-482, December.
    9. Ioannidis, Christos & Pasiouras, Fotios & Zopounidis, Constantin, 2010. "Assessing bank soundness with classification techniques," Omega, Elsevier, vol. 38(5), pages 345-357, October.
    10. Chiang, W. -C. & Urban, T. L. & Baldridge, G. W., 1996. "A neural network approach to mutual fund net asset value forecasting," Omega, Elsevier, vol. 24(2), pages 205-215, April.
    11. Akkoç, Soner, 2012. "An empirical comparison of conventional techniques, neural networks and the three stage hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) model for credit scoring analysis: The case of Turkish cred," European Journal of Operational Research, Elsevier, vol. 222(1), pages 168-178.
    12. Teija Laitinen & Maria Kankaanpaa, 1999. "Comparative analysis of failure prediction methods: the Finnish case," European Accounting Review, Taylor & Francis Journals, vol. 8(1), pages 67-92.
    13. Indro, D. C. & Jiang, C. X. & Patuwo, B. E. & Zhang, G. P., 1999. "Predicting mutual fund performance using artificial neural networks," Omega, Elsevier, vol. 27(3), pages 373-380, June.
    14. Younes Boujelb\`ene & Sihem Khemakhem, 2013. "Pr\'evision du risque de cr\'edit : Une \'etude comparative entre l'Analyse Discriminante et l'Approche Neuronale," Papers 1311.4266, arXiv.org.
    15. Premachandra, I.M. & Chen, Yao & Watson, John, 2011. "DEA as a tool for predicting corporate failure and success: A case of bankruptcy assessment," Omega, Elsevier, vol. 39(6), pages 620-626, December.

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