Neural network models and the prediction of bank bankruptcy
AbstractThe 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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Bibliographic InfoArticle provided by Elsevier in its journal Omega.
Volume (Year): 19 (1991)
Issue (Month): 5 ()
Contact details of provider:
Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/375/description#description
You can help add them by filling out this form.
CitEc Project, subscribe to its RSS feed for this item.
- Yuliya Demyanyk & Iftekhar Hasan, 2009.
"Financial crises and bank failures: a review of prediction methods,"
0904, Federal Reserve Bank of Cleveland.
- Demyanyk, Yuliya & Hasan, Iftekhar, 2010. "Financial crises and bank failures: A review of prediction methods," Omega, Elsevier, vol. 38(5), pages 315-324, October.
- Demyanyk , Yuliya & Hasan, Iftekhar, 2009. "Financial crises and bank failures: a review of prediction methods," Research Discussion Papers 35/2009, Bank of Finland.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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).
- 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.
- Ioannidis, Christos & Pasiouras, Fotios & Zopounidis, Constantin, 2010. "Assessing bank soundness with classification techniques," Omega, Elsevier, vol. 38(5), pages 345-357, October.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei).
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.