single-jc.php

JACIII Vol.17 No.1 pp. 83-92
doi: 10.20965/jaciii.2013.p0083
(2013)

Paper:

Financial Institution Failure Prediction Using Adaptive Neuro-Fuzzy Inference Systems: Evidence from the East Asian Economic Crisis

Worawat Choensawat* and Piruna Polsiri**

*School of Science and Technology, Bangkok University, Rama 4 Road, Klong-Toey, Bangkok 10110, Thailand

**Faculty of Business Administration, Dhurakij Pundit University, 110/1-4 Prachachuen Road, Laksi, Bangkok 10210, Thailand

Received:
September 10, 2012
Accepted:
November 23, 2012
Published:
January 20, 2013
Keywords:
failure prediction models, financial sector fragility, early warning systems, adaptive neuro-fuzzy inference systems (ANFIS), East Asian economic crisis
Abstract

This paper introduces the use of Adaptive Neuro-Fuzzy Inference Systems (ANFIS) into the area of finance for Thai firms. This study started with collecting financial data from 82 finance companies and 15 commercial banks operating in the period 1992-1997, before the East Asian economic crisis occurred. Financial data on failed and non-failed firms were then examined to develop fuzzy rules based on CAMEL variables. ANFIS is applied to the area of finance for Thai firms for constructing failure prediction models. These models show that prediction accuracy is greater than 90 percent for one to five years prior to failure, indicating the robustness of models over time. In experiments, models yield more accurate forecasting than a logistic model that has been used in the area of finance for Thai firms. The purpose of this study is to present thatmodels using ANFIS are better suited for financial data sets with high nonlinearity than a logistic model.

Cite this article as:
Worawat Choensawat and Piruna Polsiri, “Financial Institution Failure Prediction Using Adaptive Neuro-Fuzzy Inference Systems: Evidence from the East Asian Economic Crisis,” J. Adv. Comput. Intell. Intell. Inform., Vol.17, No.1, pp. 83-92, 2013.
Data files:
References
  1. [1] I. E. Altman, “Financial Ratios, discriminant analysis and the prediction of corporate bankruptcy,” The J. of Finance, Vol.23, No.4, pp. 589-609, 1968.
  2. [2] I. E. Altman, “Application of Classification Techniques in Business, Banking, and Finance, Contemporary Studies in Economic & Financial Analysis,” Greenwich: JAI Press, 1981.
  3. [3] I. E. Altman and P. Narayanan, “An international survey of business failure classification models,” Financial Markets, Institiutions & Instruments, Vol.6, No.2, pp. 1-57, 1997.
  4. [4] W. Beaver, “Financial ratios as predictors of failure,” J. of Accounting Research, Vol.4, pp. 71-111, 1996.
  5. [5] E. Deakin, “A discriminant analysis of predictors of business failure,” J. of Accounting Research, pp. 167-179, 1972.
  6. [6] P. Meyer and H. Pifer, “Prediction of bank failure,” J. of Finance, Vol.25, pp. 853-868, 1970.
  7. [7] J. Ohlson, “Financial ratios and the probabilistic prediction of bankruptcy,” J. of Accounting Research, Vol.18, pp. 109-131, 1980.
  8. [8] R. Pettaway and J. Sinkey, “Establishing on-site bank examination priorities: An early-warning system using accounting and market information,” J. of Finance, Vol.35, pp. 137-150, 1980.
  9. [9] M. Blum, “Failing company discriminant analysis,” J. of Accounting Research, Vol.12, No.1, pp. 1-25, 1974.
  10. [10] D. Martin, “Early warning of bank failure: a Logit regression approach,” J. of Banking & Finance, pp. 249-276, 1977.
  11. [11] J. Sinkey, “A multivariate statistical analysis of the characteristics of problem banks,” J. of Finance, Vol.30, pp. 21-36, 1975.
  12. [12] A. Atiya, “Bankruptcy prediction for credit risk using neural networks: a survey and new results,” IEEE Trans. on Neural Networks, Vol.12, No.4, pp. 929-935, 2001.
  13. [13] P. Coats and L. Fant, “Recognizing financial distress patterns using a neural network tool,” Financial Management, Vol.22, No.3, pp. 142-155, 1993.
  14. [14] E. Fernández and I. Olmeda, “Bankruptcy prediction with artificial neural networks,” In From Natural to Artificial Neural Computation, Vol.930 of Lecture Notes In Computer Science (Proc. of the Int. Workshop on Artificial Neural Networks), pp. 1142-1146, Springer Berlin, Heidelberg, 1995.
  15. [15] L. Salchenberger, E. Cinar, and N. Lash, “Neural networks for financial diagnosis,” Decision Sciences, Vol.23, pp. 889-916, 1992.
  16. [16] G. Zhang, M. Hu, B. Patuwo, and D. Indro, “Artificial neural networks in bankruptcy prediction: general framework and crossvalidation analysis,” European J. of Operation Research, Vol.116, No.1, pp. 16-32, 1999.
  17. [17] G. G. Towell and J. W. Shavlik, “Extracting refined rules from knowledge-based neural networks,” Machine Learning, Vol.13, No.1, pp. 71-101, 1993.
  18. [18] M. Miller, S. Hui, and W. Tierney, “Validation techniques for logistic regression models,” Statistics in Medicine, Vol.10, No.8, pp. 1213-1226, 1991.
  19. [19] P. Polsiri and P. Jiraporn, “Political connections, ownership structure, and financial institution failure,” J. of Multinational Financial Management, Vol.22, No.1, pp. 39-53, 2012.
  20. [20] M. Tarawneh, “A comparison of financial performance in the banking sector: some evidence from Omani commercial banks,” Int. Research J. of Finance and Economics, Vol.3, pp. 101-112, 2006.
  21. [21] R. Kouser and I. Saba, “Gauging the financial performance of banking sector using CAMEL model: comparison of conventional, mixed and pure Islamic banks in Pakistan,” Int. Research J. of Finance and Economics, Vol.82, pp. 67-88, 2012.
  22. [22] A. Demirguc-Kunt and E. Detragiache, “Monitoring banking sector fragility: a multivariate logit approach with an application to the 1996-1997 banking crisis,” Brooking Papers on Economic Activity, Vol.14, pp. 287-307, 2000.
  23. [23] B. Eichengreen and A. Rose, “Staying afloat when the wind shifts: External factors and emerging-market banking crisis,” Unpublished working paper, NBER, 1998.
  24. [24] J. Furman and J. Stiglitz, “Economic crisis: evidence and insights from east asia,” Brooking Papers on Economic Activity, Vol.1, pp. 1-135, 1998.
  25. [25] G. Kaminsky and C. Reinhart, “The twin crisis: the causes of banking and balance-of-payments problems,” American Economic Review, Vol.89, pp. 473-500, 1999.
  26. [26] S. Radelet and J. Sachs, “The east asian financial crisis: diagnosis, remedies, prospects,” Brooking Papers on Economic Activity, Vol.1, pp. 1-90, 1998.
  27. [27] P. Bongini, S. Claessens, and G. Ferri, “The political economy of distress in east asian financial institutions,” Brooking Papers on Economic Activity, Vol.19, pp. 5-25, 2001.
  28. [28] F. Aminian, E. Suarez, M. Aminian, and D. Walz, “Forecasting economic data with neural networks,” Computational Economics, Vol.28, No.1, pp. 71-88, 2006.
  29. [29] C. Zopounidis and M. Doumpos, “A multicriteria decision aid methodology for sorting decision problems: the case of financial distress,” Computational Economics, Vol.14, No.3, pp. 197-218, 1999.
  30. [30] I. Olmeda and E. Fernández, “Hybrid classifiers for financial multicriteria decision making: the case of bankruptcy prediction,” Computational Economics, Vol.10, No.4, pp. 317-335, 1997.
  31. [31] K. Sookhanaphibarn, P. Polsiri, W. Choensawat, and F. Lin, “Application of neural networks to business bankruptcy analysis in Thailand,” Int. J. of Computational Intelligence Research, Vol.3, No.1, pp. 91-96, 2007.
  32. [32] P. Ravikumar and V. Ravi, “Bankruptcy prediction in banks by an ensemble classifier,” In IEEE Int. Conf. on Industrial Technology 2006 (ICIT 2006), pp. 2032-2036, 2006.
  33. [33] S. Reynolds, R. Fowles, J. Gander, W. Kunaporntham, and S. Ratanakomut, “Forecasting the Probability of Failure of Thailand���s Financial Companies in the Asian Financial Crisis,” Economic Development and Cultural Change, Vol.51, No.1, pp. 237-246, 2002.
  34. [34] K. Urapeepatanapong, S. Sethsathira, and C. Okanurak, “New bankruptcy act to boost Thai economy,” Int. Financial Law Review, Vol.17, No.4, pp. 33-7, 1998.
  35. [35] S. Tirapat and A. Nittayagasetwat, “An investigation of Thai listed firms financial distress using macro and micro variables,” Multinational Finance J., Vol.3, No.2, pp. 103-125, 1999.
  36. [36] S. Pongsatat, J. Ramage, and H. Lawrence, “Bankruptcy prediction for large and small firms in Asia: a comparison of Ohlson and Altman,” J. of Accounting and Croporate Governance, Vol.1, No.2, pp. 1-13, 2004.
  37. [37] C. Charumilind, R. Kali, and Y. Wiwattanakantang, “Connected Lending: Thailand before the Financial Crisis,” The J. of Business, Vol.79, No.1, pp. 181-218, 2006.
  38. [38] P. Polsiri and K. Sookhanaphibarn, “Corporate Distress Prediction Models Using Governance and Financial Variables: Evidence from Thai Listed Firms during the East Asian Economic Crisis,” J. of Economics and Management, Vol.5, No.2, pp. 273-304, 2009.
  39. [39] Z. Himer, V.Wertz, J. Kovacs, and U. Kortela, “Neuro-fuzzy model of flue gas oxygen content,” Proc. of Modeling Identification and Control, Grindelwald, Switzerland, Vol.412, ACTA Press, Canada, 2004.
  40. [40] E. Ikonen and K. Najim, “Fuzzy neural networks and application to the FBC process,” IEE Proc. of Control Theory and Applications, Vol.143, pp. 259-269, 1996.
  41. [41] R. Jang, C. Sun, and E. Mizutani, “Neuro-fuzzy and soft computation,” Prentice Hall, 1997.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Mar. 05, 2021