Abstract
The strong relationship between bank failure and economic growth attaches far more importance to the predictability of bank failures. Consequently, numerous statistical prediction models exist in the literature focusing on this particular subject. Besides, artificial intelligence techniques began to attain an increasing level of importance in the literature due to their predictive success. This study distinguishes itself from the similar ones in the sense that it presents a comparison of three different artificial intelligence methods, namely support vector machines (SVMs), radial basis function neural network (RBF-NN) and multilayer perceptrons (MLPs); in addition to subjecting the explanatory variables to principal component analysis (PCA). The extent of this study encompasses 37 privately owned commercial banks (17 failed, 20 non-failed) that were operating in Turkey for the period of 1997–2001. The main conclusions drawn from the study can be summarized as follows: (i) PCA does not appear to be an effective method with respect to the improvement of predictive power; (ii) SVMs and RBF demonstrated similar levels of predictive power; albeit SVMs was found to be the best model in terms of total predictive power; (iii) MLPs method stood out among the SVMs and RBF methods in a negative sense and exhibits the lowest predictive power.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Bandalos D. L., Boehm-Kaufman M. R. (2009) Four common misconceptions in exploratory factor analysis. In: Lance Charles E., Vandenberg Robert J. (eds) Statistical and Methodological Myths and Urban Legends: Doctrine, Verity and Fable in the Organizational and Social Sciences. Routledge, New York, pp 61–87
Bell T. B. (1997) Neural nets or the logit model: A comparison of each model’s ability to predict commercial bank failures. International Journal of Intelligent Systems in Accounting Finance and Management 6: 249–264
Benli K. Y. (2005) Bankalarda mali başarısızlı ğın öngörülmesi lojistik regresyon ve yapay sinir ağı karşılaştırması. Gazi Üniversitesi Endüstriyel Sanatlar Eğitim Fakültesi Dergisi 16: 31–46
Boyacıoglu M. A., Kaya Y., Baykan Ö K. (2009) Predicting bank financial failures using neural networks, support vector machines and multivariate statistical methods: A comparative analysis in the sample of savings deposit insurance fund (SDIF) transferred banks in Turkey. Expert Systems with Applications 36(2): 3355–3366
BRSA (2009). From crisis to financial stability: Turkey experience. The Banking Regulation and Supervision Agency, Department of Strategy Development, Ankara, Turkey.
Canbas S., Cabuk A., Kilic S. B. (2005) Prediction of commercial bank failure via multivariate statistical analysis of financial structure: The Turkish case. European Journal of Operational Research 166: 528–546
Çinko M., Avcı E. (2008) CAMELS dereceleme sistemi ve Türk ticari bankacılık sektöründe başarısızlık tahmini. BDDK Bankacılık ve Finansal Piyasalar 2(2): 25–48
Ekinci, A., Erdal, H. İ. (2011). An application on prediction of bank failure in Turkey. İktisat İşletme ve Finans Dergisi, 26 (298).
Gorgel, P., Kilic, N., Ucan, O. N., Osman, O., & Sertbas A. (2008). Mammographic Mass Classification Using Wavelet Based Support Vector Machine. In: International Workshop III: Mini Symposium on Application of Wavelets to Real World Problem, İstanbul.
Hong W. (2008) Rainfall forecasting by technological machine learning models. Applied Mathematics and Computation 200(1): 41–57
Kachigan Sam Kash (1986) Statistical Analysis: An Interdisciplinary Introduction to Univariate & Multivariate Methods. Radius Press, New York
Kilic, N., Gorgel P., Ucan O.N., & Kala A. (2008). Multifont Ottoman Character Recognition using Support Vector Machine, IEEE International Symposium on Control Communication and Signal Processing (ISCCSP08), Malta
Kim K. (2003) Financial time series forecasting using support vector machines. Neurocomputing 55(1–2): 307–319
Kolari J., Glennon D, Shin H., Caputo M. (2002) Predicting large US commercial bank failures. Journal of Economics and Business 54(4): 361–387
Lee Y. (2007) Application of support vector machines to corporate credit rating prediction. Expert Systems with Applications 33(1): 67–74
Mardia K. V., Kent J. J., Bibby J. M. (1979) Multivariate Analysis. Academic Press Limited, London
Martin D. (1977) Early warning of bank failure: A logit regression approach. Journal of Banking and Finance 1: 249–276
Meyer P. A., Pifer H. W. (1970) Prediction of bank failures. The Journal of Finance 25(4): 853–858
Olmeda I., Fernandez E. (1997) Hybrid classiers for financial multicriteria decision making: The case of bankruptcy prediction. Computational Economics 10(4): 317–335
Pai P., Hong W. (2005) Forecasting regional electricity load based on recurrent support vector machines with genetic algorithms. Electric Power Systems Research 74(3): 417–425
Ravi V., Kurniawan H., Thai P. N. K., Kumar R. (2008) Soft computing system for bank performance prediction. Applied Soft Computing 8(1): 305–315
Sinkey J. F. (1975) A multivariate statistical analysis of the characteristics of problem banks. Journal of Finance 30(1): 21–36
Stevens James P. (2002) Applied Multivariate Statistics for the Social Sciences, 4th Edn. Lawrence Erlbaum Associates Publishers, New Jersey
Swicegood P., Clark J. A. (2001) Off-site monitoring for predicting, bank under performance: A comparison of neural networks, discriminant analysis and professional human judgment. International Journal of Intelligent Systems in Accounting, Finance and Management 10: 169–186
Tam K. Y., Kiang M. (1992) Predicting bank failures: A neural network approach. Decision Sciences 23: 926–947
Tam K. Y. (1991) Neural network models and the prediction of bank bankruptcy. Omega 19(5): 429–445
Wang J., Zhu W., Zhang W., Sun D. (2009) A trend fixed on firstly and seasonal adjustment model combined with the ε-SVR for short-term forecasting of electricity demand. Energy Policy 37(11): 4901–4909
West R. C. (1985) A factor analytic approach to bank condition. Journal of Banking and Finance 9: 253–266
Yu L., Wang S., Lai K. (2008) Forecasting crude oil price with an EMD-based neural Network ensemble learning paradigm. Energy Economics 30(5): 2623–2635
Author information
Authors and Affiliations
Corresponding author
Electronic Supplementary Material
The Below is the Electronic Supplementary Material.
Rights and permissions
About this article
Cite this article
Erdal, H.I., Ekinci, A. A Comparison of Various Artificial Intelligence Methods in the Prediction of Bank Failures. Comput Econ 42, 199–215 (2013). https://doi.org/10.1007/s10614-012-9332-0
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10614-012-9332-0