An intelligent software model design for estimating deposit banks profitability with soft computing techniques

Ferdi Sönmez, Şahamet Bülbül

Abstract


Profitability of Turkish banking sector gained importance after national and international financial crisis happened in the last decade, which revealed the need to make a research on profitability and the factors determining profitability. In recent years, new techniques of soft computing (SC) like genetic algorithms (GAs), fuzzy logic (FL) and especially artificial neural networks (ANNs) have been applied into the financial domain to solve the domain issues because of their successful applications in nonlinear multivariate situations. An adaptive system was needed due to the fact that insufficient use of application software programs for SC and the fact that single software is only applicable for specific model. Furthermore, even though ANNs have been applied to many areas; little attention has been paid to estimation of bank profitability with ANNs. This article is intended to analyze and estimate the profitability of deposit banks in Turkey with an adaptive software model of ANNs which have not been previously applied for this context, comprehensively. The results from the software model, which processes the factors affecting profitability, indicate that all of the variables used have significant impacts in varying proportions on profitability and that obtained estimations achieved the targeted and acceptable performance of success. This software model is expected to provide easiness on estimating bank profitability, since giving successful estimations and not being affected by user differences. Additionally, it is aimed to construct a software model for being used in different fields of study and financial domain.

Keywords


bank profitability; Turkish banking sector; soft computing techniques; artificial neural networks; multilayer perceptron; Levenberg-Marquardt back propagation algorithm

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References


ACARAVCI S.K., CALIM, A.E. Turkish banking sector's protability factors [online]. International Journal of Economics and Financial Issues. 2013, 3(1), pp. 27-41 [viewed 2014-10-20]. Available from: http://www.econjournals.com/index.php/ije/article/view/343/pd.

AKKOC S. 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 credit card data. European Journal of Operational Research. 2012, 222(1), pp. 168-178, doi: 10.1016/j.ejor.2012.04.009.

ALBERTAZZI U., GAMBACORTA L. Bank protability and the business cycle. Journal of Financial Stability. 2009, 5(4), pp. 393-400, doi: 10.1016/j.jfs.2008.10.002.

ALPER D., ANBAR A. Bank specic and macroeconomic determinants of commercial bank protability: empirical evidence from Turkey [online]. Business and Economics Research Journal. 2011, 2(2), pp. 139-152 [viewed 2014-10-20]. Available from: http://papers.ssrn.com/sol3/papers.cfm? abstract id=183134.

AL-QAHERI H., HASSANIEN A.E., ABRAHAM A. Discovering stock price prediction rules using rough sets. Neural Network World. 2008, 18(3), pp. 181-198.

ALTAS D., CILINGIRTURK A.M., GULPINAR V. Analyzing the process of the articial neural networks by the help of the social network analysis [online]. New Knowledge Journal of Science. 2013, 2(2), pp. 80-91 [viewed 2014-11-20]. Available from: http:// http://uard.bg/les/custom les/les/documents/New%20knowledge/year2 n2/paper altas y2n2.pd.

ALTUNOZ U. Bankalarn nansal basarszlklarnn yapay sinir aglarmodeli cercevesinde tahmin edilebilirligi [online]. Dokuz Eylul Universitesi _Iktisadi ve _Idari Bilimler Fakultesi Dergisi. 2013, 28(2), pp. 189-217 [viewed 2014-11-20]. In Turkish. Available from: http://iibf.deu.edu.tr/deuj/index.php/cilt1-sayi1/article/download/342/pdf 30.

ANASTASAKIS L., MORT N. Neural network-based prediction of the USD/GBP exchange rate: the utilisation of data compression techniques for input dimension reduction [on-line]. University of Sheffield, 2000 [viewed 2014-10-30]. Technical Report. Available from: http://www.researchgate.net/ prole/Leonidas Anastasakis/publication.

ANI W.U., UGWUNTA D.O., EZEUDU I.J., UGWUANYI G.O. An empirical assessment of the determinants of bank protability in Nigeria: bank characteristics panel evidence. Journal of Accounting and Taxation. 2012, 4(3), pp. 38-43, doi: 10.5897/JAT11.034.

ANYAECHE C.O., IGHRAVWE D.E. Predicting performance measures using linear regression and neural network: a comparison. African Journal of Engineering Research. 2013, 1(3), pp. 84-89 [viewed 2014-09-20]. Available from: http://www.netjournals.org/z AJER 13 028.htm.

ARSLAN M.H., CEYLAN M., KOYUNCU T. An ANN approaches on estimating earthquake performances of existing RC buildings. Neural Network World. 2012, 22(5), pp. 443-458, doi: 10.14311/NNW.2012.22.027.

ATHANASOGLOU P.P., BRISSIMIS S.N., DELIS M.D. Bank-specic, industry specic and macroeconomic determinants of bank protability. Journal of International Financial Markets, Institutions and Money. 2006, 18(2), pp. 121-136, doi: 10.1016/j.intn.2006.07.001.

ATSALAKIS G.S., VALAVANIS K.P. Surveying stock market forecasting techniques - part II: soft computing methods. Expert Systems with Applications. 2009, 36(3), Part 2, pp. 5932-5941, doi: 10.1016/j.eswa.2008.07.006.

AYSAN A.G., ABBASOGLU O.F. Concentration, competition, efficiency and protability of the Turkish banking sector in the post-crises period [online]. Banks and Bank Systems. 2007, 3(2), pp. 106-115 [viewed 2013-10-10]. Available from: http://mpra.ub.uni-muenchen.de/5494/1/ MPRA paper 5494.pd.

BESSIS J. Risk Management in Banking. 3rd ed. UK: John Wiley & Sons, 2010.

BOYACIOGLU M.A., KARA Y., BAYKAN, O.K. Predicting bank nancial 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. 2009, 36(2), Part 2, pp. 3355-3366, doi: 10.1016/j.eswa.2008.01.003.

BRANDEJSKY T. Evolutionary system to model structure and parameters regression. Neural Network World. 2012, 22(2), pp. 181-194, doi: 2012/NNW.2012.22.011.

CAO L. Support vector machines experts for time series forecasting. Neurocomputing. 2003, 51, pp. 321-329, doi: 10.1016/S0925-2312(02)00577-5.

CHAKRABORTY S., SHARMA S. Prediction of corporate nancial health by articial neural network. International Journal of Electronic Finance. 2007, 1(4), pp. 442-459, doi: 10.1504/IJEF.2007.012898.

CHEN M.Y. A Hybrid model for business failure prediction-utilization of particle swarm optimization and support vector machines. Neural Network World. 2011, 21(2), pp. 129-152, doi: 10.14311/NNW.2011.21.009.

CHEN M.-Y., FAN M.-H., CHEN Y.-L., WEI H.-M. Design of experiments on neural network's parameters optimization for time series forecasting in stock markets. Neural Network World. 2013, 23(4), pp. 369-393, doi: 10.14311/NNW.2013.23.023.

CHIROMA H., ABDULKAREEM S., ABUBAKAR A., USMAN M.J.. Computational intelligence techniques with application to crude oil price projection: a literature survey from 2001- 2012. Neural Network World. 2013, 23(6), pp. 523-551, doi: 10.14311/NNW.2013.23.032.

COAKLEY J.R., BROWN C.E. Articial neural networks in accounting and nance: modeling issues. International Journal of Intelligent Systems in Accounting, Finance & Management. 2000, 9(2), pp. 119-144, doi: 10.1002/1099-1174(200006)9:23.0.CO;2-Y.

CURAK M., POPOSKI K., PEPUR S. Protability determinants of the Macedonian banking sector in changing environment. Procedia - Social and Behavioral Sciences. 2012, 44, pp. 406-416, doi: 10.1016/j.sbspro.2012.05.045.

DEMUTH H., BEALE M., HAGAN M. Neural Network Toolbox 6 User's Guide. Natick, MA: The MathWorks Inc, 2009.

DIETRICH A., WANZENRIED G. Determinants of bank protability before and during the crisis: evidence from Switzerland. Journal of International Financial Markets, Institutions & Money. 2011, 21, pp. 310-320, doi: 10.2139/ssrn.1370245.

DUNIS CH.L., JALILOV J. Neural network regression and alternative forecasting techniques for predicting nancial variables. Neural Network World. 2002, 12(2), pp. 113-139.

FANNING K.M, COGGER K.O. A Comparative analysis of articial neural networks using nancial distress prediction. Intelligent Systems in Accounting, Finance and Management. 1994, 3(4), pp. 241-252, doi: 10.1002/j.1099-1174.1994.tb00068.x.

GIRDEN E.R. Evaluating research articles from start to nish. Thousand Oaks, CA: Sage Publications, 2001.

GUNGOR B. Turkiye'de faaliyet gosteren yerel ve yabancbankalarn karllk seviyelerini etkileyen faktorler: panel veri analizi. _Iktisat _ Isletme ve Finans. 2007; 22(258), pp. 40-63, doi: 10.3848/iif.2007.258.1464. In Turkish.

HAGAN M.T., MENHAJ M.B. Training feed-forward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks. 1994, 5(6), pp. 989-993, doi: 10.1109/72.329697.

HAN S., CHEN R-C. Using SVM with nancial statement analysis for prediction of stocks [online]. Communications of the IIMA. 2007, 7(4), pp. 63-72 [viewed 2014-05-15]. Available from: http://www.iima.org/index.php?option=com phocadownload&view=category&download=77%3Ausing-svm-with-nancial-statement-analysis-for-prediction-of-stocks&id=8%3A2007-volume-7-issue-4&Itemid=6.

HAN Y.,WANG B. Investigation of listed companies credit risk assessment based on different learning schemes of BP neural network. International Journal of Business and Management. 2011, 6(2), pp. 204-207, doi: 10.5539/ijbm.v6n2p204.

HIPPERT H.S., PEDREIRA C.E., SOUZA R.C. Neural networks for short-term load forecasting: a review and evaluation. IEEE Transactions on Power Systems. 2001, 16(1), pp. 44-55, doi: 10.1109/59.910780.

HONGJIU L., RIEG R., YANRONG H. Performance comparison of articial intelligence methods for predicting cashflow. Neural Network World. 2012, 22(6), pp. 549-564, doi: 10.14311/NNW.2012.22.034.

KALAYCI S. SPSS UygulamalC ok Degiskenli _ Istatistik Teknikleri. Turkey-Ankara: Asil Yaynlar, 2010. In Turkish.

KANAS A., VASILIOU D., ERIOTIS N. Revisiting bank protability: a semi-parametric approach. Journal of International Financial Markets, Institutions and Money. 2012, 22(4), pp. 990-1005, doi: 10.1016/j.intn.2011.10.003.

KARHUNEN J. Robust PCA methods for complete and missing data. Neural Network World. 2011, 21(5), pp. 357-392, doi: 10.14311/NNW.2011.21.022.

KUMAR P.R., RAVI V. Bankruptcy prediction in banks and rms via statistical and intelligent techniques. European Journal of Operational Research. 2007, 180(1), pp. 1-28, doi: 10.1016/j.ejor.2006.08.043.

LAVANYA V., PARVEENTAJ M. Foreign currency exchange rate (FOREX) using neural network [online]. International Journal of Science and Research. 2013, 2(10), pp. 174-177 [viewed 2014-04-15]. Available from: http://www.ijsr.net/archive/v2i10/MDIwMTMzMjc=.pd.

MIN J.H., LEE Y-C. Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters. Expert Systems with Applications. 2005, 28, pp. 603-614, doi: 10.1016/j.eswa.2004.12.008.

NASIR M.L., JOHN R.I., BENNETT S.C., RUSSELL D.M., PATEL A. Predicting corporate bankruptcy using articial neural networks. Journal of Applied Accounting Research. 2000, 5(3), pp. 30-52, doi: 10.1108/96754260080001017.

OZKAN C., OZTURK C., SUNAR F., KARABOGA D. The Articial bee colony algorithm in training articial neural network for oil spill detection. Neural Network World. 2011, 21(6), pp. 549-564, doi: 10.14311/NNW.2011.21.028.

PARASIZ I. Para Banka ve Finansal Piyasalar. Turkey-Bursa: Ezgi Kitabevi Publications, 2013. In Turkish.

PASIOURAS F., KOSMIDOU K. Factors in uencing the protability of domestic and foreign commercial banks in the European Union. Research in International Business and Finance. 2007, 21, pp. 222-237, doi: 10.1016/j.ribaf.2006.03.007.

QIUHONG S., JIAN G. Specic performance prediction based on BP neural network. International Journal of Digital Content Technology and its Applications. 2013, 7(6), pp. 514-521, doi: 10.4156/jdcta.vol7.issue6.58.

RAVI V., KURNIAWAN H., THAI P.N.K., KUMAR P.R. Soft computing system for bank performance prediction. Applied Soft Computing. 2008, 8(1), pp. 305-315, doi: 10.1016/j.asoc.2007.02.001.

RAVI V., ZIMMERMANN H.J. A neural network and fuzzy rule base hybrid for pattern classication. Soft Computing. 2001, 5(2), pp. 152-159, doi: 10.1007/s005000000071.

SAEED M.S. Bank-related, industry-related and macroeconomic factors affecting bank protability: a case of the United Kingdom [online]. Research Journal of Finance and Accounting. 2014, 5(2), pp. 42-50 [viewed 2014-05-15]. Available from: http://www.iiste.org/Journals/index.php/ RJFA/article/view/10678/1088.

SAYILGAN G., YILDIRIM, O. Determinants of protability in Turkish banking sector: 2002-2007. International Research Journal of Finance and Economics. 2009, 28, pp. 207-214.

SHEELA K.G., DEEPA, S.N. Review on methods to x number of hidden neurons in neural networks. Mathematical Problems in Engineering. 2013, 2013, 11 pp., doi: 10.1155/2013/425740.

SRINIVASA K.G., SRIDHARAN K., DEEPA S.P., VENUGOPAL K.R., PATNAIK L.M. EASOM: an efficient soft computing method for predicting the share values. In: Proceedings of International Conference on Articial Intelligence and Applications, Innsburg, Austria. Innsburg: IASTED, 2004, pp. 264-269.

THANGAVELU S.M., JIUNN A.B., ANG J.B. Financial development and economic growth in Australia: an empirical analysis. Empirical Economics. 2004, 29, pp. 247-260, doi: 10.1007/s00181-003-0163-7.

TRUJILLO-PONCE A. What determines the protability of banks? Evidence from Spain. Accounting & Finance. 2013, 53(2), pp. 561-586, doi: 10.1111/j.1467-629X.2011.00466.x.

TSAI C-F., WU J-W. Using neural network ensembles for bankruptcy prediction and credit scoring. Expert Systems with Applications. 2008, 34, pp. 2639-2649, doi: 10.1016/j.eswa.2007.05.019.

WALLRAFEN J., PROTZEL P., POPP H. Genetically optimized neural network classiers for bankruptcy prediction-an empirical study. In: Proceedings of the 29th Annual Hawaii International Conference on System Sciences, Wailea, Hawaii. 1996, 2, pp. 419-426, doi: 10.1109/HICSS.1996.495427.

WASHINGTON S. KARLAFTIS M., MANNERING, F. Statistical and Econometric Methods for Transportation Data Analysis. 2nd ed. Boca Raton, FL: Chapman and Hall/CRC Press, 2011.

YANG C.-H., LIAO M.-Y., CHEN P.-L., HUANG M.-T., HUANG C.-W., HUANG J.-S., CHUNG J.-B. Constructing nancial distress prediction model using group method of data handling technique. In: Proceedings of International Conference on Machine Learning and Cybernetics, Baoding. IEEE, 2009, 5, pp. 2897-2902, doi: 10.1109/ICMLC.2009.5212590.

YETILMEZSOY K., OZKAYA B., CAKMAKCI M. Articial intelligence-based prediction models for environmental engineering. Neural Network World. 2011, 21(3), pp. 193-218, doi: 10.14311/NNW.2011.21.012.

YILDIZ B., AKKOC S. Banka nansal basarszlklarnn sinirsel bulank ag yontemi ile ongorusu [online]. BDDK Bankaclk ve Finansal Piyasalar. 2009, 3(1), pp. 9-36 [viewed 2013-08-15]. In Turkish. Available from: https://www.bddk.org.tr/WebSitesi/turkce/Raporlar/BDDK Dergi/6732makale1%20akkoc.pd.

ZADEH L.A. The roles of soft computing and fuzzy logic in the conception, design and deployment of intelligent system. In: IEEE Asia Pacic Conference on Circuits and Systems, Seoul, Korea. IEEE, 1996, pp. 3-4, doi: 10.1109/APCAS.1996.585470.

ZHANG G., PATUWO B.E., HU M.Y. Forecasting with articial neural networks: The state of the art. International Journal of Forecasting. 1998, 14(1), pp. 35-62, doi: 10.1016/S0169-2070(97)00044-7.




DOI: http://dx.doi.org/10.14311/NNW.2015.%25x

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