Studying ant Prediction the Performance of Iran's Banking System by Using Kohonen, ANN and Panel data Models

اقتصاد پولی مالی

Article 7, Volume 21, Issue 8, January 2015, Pages 153-174   

Document Type: پژوهشی

DOI: 10.22067/pm.v21i8.45863

Authors

Taghi Ebrahimi Salary^* ; Seyed Mohammad Fahimifard; Hanif Kheirkhah

Abstract

Abstract
In this research the comparative prediction of Iran's banking system (included 14 banks) was carried out by using econometric and artificial neural network models. Accordingly, at first, by using the Kohonen neural network model, the considered banks were divided into two categories of high performance and low performance groups and then using the output of Kohonen neural network model, financial proportions and Panel Data econometric model, the performance of Iran's banking system was estimated for the period 2004-2010 and finally by using models evaluation criteria, the performance of Panel Data and ANN models was compared.
The results of Kohonen neural network model indicated that from 14 considered bank, 4 banks belong to high performance group and 10 banks are belong to low performance group. Also the results of Panal Data estimations showed that “capital income/total income "portion has the lowest and “cash/total deposits", has the haighes effect on the Iran's banking system. Finally the results of models comparison stated that the ANN model outperforms the Panel Data model to predict the performance of Iran's banking system.

Keywords

performance of banking system; prediction; Panel Data econometric model; Kohonen model; ANN model

References

[1] Ahmadpour, H. (2006), Estimating a commercial bank efficiency using data envelopment analysis (DEA), thesis for M. A. degree, University of Mazandaran (in Persian).##

[2] Ahmadi, A. A. Moghadasan, M. H. Mehrkish, M. H. Moarefi, F. (2012), Evaluating the electronic banking performance in public banks, quarterly of public organizations management, 1(1): 65-74 (in Persian).##

[3] Iranzadeh, S. and Barghi, A. (2009), Ranking and evaluating the bank performance using principal component analysis (PCA) technic, case of central branches of Mine and Industry bank, (Resercher) management quarterly, 6(14): 47-61 (in Persian).##

[4] Divandari, A. Seied Javadin, S. R. Nahavandian, M. Aghazadeh, H. (2008), Studying the relationship between marketing and performance of Iran’s commercial banks, Economic researchs, 17-40 (in Persian).##

[5] Al-Osaimy, M. H. (1995). A Neural Networks System for Predicting Islamic Banks Performance. JKAU: Econ. & Adm., Vol. 11, pp. 33-46.##

[6] Dreca N. (2012). Evaluation of Financial Performance of Banking Sector: Evidence from Bosnia and Herzegovina, Croatia, Serbia and Slovenia. Journal of Economic and Social Studies, 2(2): 65-94.##

[7] Eken, M. H and Kale. S. (2011). Measuring bank branch performance using Data Envelopment Analysis (DEA): The case of Turkish bank branches. African Journal of Business Management, 5(3): 889-901.##

[8] Greene, W. H. (2002). Econometric Analysis, Fifth edition. Prentice Hall Upper Saddle River, New Jersey.##

[9] Haykin, S. (1994). Neural Networks: A Comprehensive Foundation. Macmillan, New York.##

[10] Karayiannis, N. B. and A. N. Venetsanopoulos (1993). Artificial Neural Networks: Learning Algorithms. Performance Evaluation and Applications. Kluwer Academic Publishers, Boston, USA.##

[11] Kosmidou, K. and Zopounidis, C. (2008). MEASUREMENT OF BANK PERFORMANCE IN GREECE. South-Eastern Europe Journal of Economics, 1: 79-95##

[12] Makridakis, S., Hibon, M. (2000). The M-competition: Results, conclusions and implications. International Journal of Forecasting 16,451-476.##

[13] Mananhar, R. and Tang, J. C. S. (2002). The Evaluation of Bank Branch Performance Using Data Envelopment Analysis A Framework School of Management. Asian Institute of Technology, PO Box 4, Klong Luang, Pathumthani 12120, Thailand.##

[14] Marcellino, M. (2004). Forecast Pooling for Short Time Series of Macroeconomic Variables”. Oxford Bulletin of Economic and Statistics 66, 91-112.##

[15] Watson, M. (2001). Combination Forecasts of Output Growth in A Seven-Country Data Set, (with James Stock). Forthcoming Journal of Forecasting.##