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Analysis of Changes in Market Shares of Commercial Banks Operating in Turkey Using Computational Intelligence Algorithms

DOI: 10.1155/2014/649860

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This paper aims to model the change in market share of 30 domestic and foreign banks, which have been operating between the years 1990 and 2009 in Turkey by taking into consideration 20 financial ratios of those banks. Due to the fragile structure of the banking sector in Turkey, this study plays an important role for determining the changes in market share of banks and taking the necessary measures promptly. For this reason, computational intelligence methods have been used in the study. According to the research results, it is seen that it was not able to properly anticipate the data for the banking sector in the periods of financial crises (2000-2001 and 2008-2009). However, it is seen that, Simple Linear Regression is distinguished as a good algorithm among the computational intelligence algorithms for all periods between the years 1990 and 2009. 1. Introduction As a natural result of the financial liberalization in the economy and the banking industry in Turkey after 1980s, the competition in the banking industry increased significantly due to the reasons such as many new domestic and foreign players in the banking industry, release of the fund transfers especially from international markets, enabling the banks to make transactions in foreign currencies, advances in the technology, and introducing new services by the banks in the industry. Therefore, a bank, operating in the banking industry, can differentiate itself from the other banks only if it can develop new strategies. In recent years, because of economic and financial crisis, some of the public and private banks were bankrupted and some of them are merged and therefore they were forced to change how they operate. In this instance, a serious competition occurred among the surviving banks to take the market shares of the banks that have left the industry. The banks, which have evaluated the present circumstances, used cutting edge technology, and improved the scope of their products and services, were able to advance forward significantly. Thus, these advances create a necessary environment for such banks to improve their market shares. Therefore, evaluating their position in the market and developing new strategies in accordance with their positions became much more important. The presence of a tough competition between the banks besides the fragile structure of the banking sector in Turkey makes it important to determine the change in the market shares of banks and to take the necessary measures. For this reason, goal-oriented estimations that would be made by using computational


[1]  F. ?olak and A. Yigidim, Türk Bankac?l?k Sekt?ründe Kriz, Nobel Yay?n Da??t?m, Ankara, Turkey, 2001 (Turkish).
[2]  S. Oksay, “Finansal piyasalarda yeni yasal düzenlemeler ihtiyac? ve türk finans sistemi,” Sosyal Bilimler Enstitüsü ?neri Dergisi, 2000 (Turkish).
[3]  M. Ural, “bankac?l?k sistemimizde verimlilik,” D.E.ü.?.?.B.F. Dergisi, vol. 2, pp. 147–157, 1999 (Turkish).
[4]  Türkiye Bankalar Birli?i, “50. Y?l?nda Türkiye Bankalar Birli?i ve Türkiye’de Bankac?l?k Sistemi (1958–2007),” ?stanbul, Turkey, 2008 (Turkish).
[5]  Bankac?l?k Düzenleme ve Denetleme Kurumu, “Y?ll?k Rapor 2001,” 2002 (Turkish).
[6]  H. Seyido?lu, Uluslar Aras? ?ktisat, Geli?tirilmi?, Güzem Yay?nc?l?k, ?stanbul, Turkey, 13th edition, 1999 (Turkish).
[7]  B. Tunay, Finans Sisteminde Yeni Y?nelimler: Türk Finans Piyasalar?n?n Bugünü ve Gelece?i, Beta Bas?m yay?m Da??t?m, ?stanbul, Turkey, 2001 (Turkish).
[8]  H. Tun?, “Finansal Kriz ve Türkiye Ekonomisi,” ?SO Dergisi 421, 2001 (Turkish).
[9]  K. Duman, “Finansal kriz ve bankac?l?k sekt?rünün yeniden yap?land?r?lmas?,” Akdeniz üniversitesi ?.?.B.F. Dergisi, vol. 4, 2002 (Turkish).
[10]  I. Say?m, K. Duman, and A. Korkmaz, “Türkiye ekonomisinde finansal krizler: bir fakt?r analizi uygulamas?,” D.E.ü.?.?.B.F. Dergisi, vol. 1, pp. 45–69, 2004 (Turkish).
[11]  R. Karluk, Türkiye Ekonomisi: Tarihsel Geli?im Yap?sal ve Sosyal De?i?im, Beta Bas?m, ?stanbul, Turkey, 2004 (Turkish).
[12]  S. Uyar, Bankac?l?k Krizleri, Ziraat Matbaac?l?k, Ankara, Turkey, 2003 (Turkish).
[13]  Bankac?l?k Düzenleme ve Denetleme Kurumu, “Bankac?l?k Sekt?rü Yeniden De?erlendirme Program?,” 2001 (Turkish).
[14]  Türkiye Bankalar Birli?i, “Bankalar?m?z 2009,” ?stanbul, Turkey, 2010, (Turkish).
[15]  Türkiye Bankalar Birli?i, “Bankalar?m?z 2010,” ?stanbul, Turkey, 2011, (Turkish).
[16]  Weka Manual for Version 3-6-3.
[17]  M. Hall, G. Holmes, and E. Frank, “Generating rule sets from model trees,” in Proceedings of the 12th Australian Joint Conference on Artificial Intelligence, pp. 1–12, Springer, Sydney, Australia, 1999.
[18]  R. Kohavi, “The power of decision tables,” in Proceedings of the European Conference on Machine Learning (ECML '95), N. Lavrac and S. Wrobel, Eds., pp. 174–189, Springer, Berlin, Germany, 1995.
[19]  J. R. Quinlan, “Simplifying decision trees,” International Journal of Man-Machine Studies, vol. 27, no. 3, pp. 221–234, 1987.
[20]  T. Elomaa and M. K??ri?inen, “An analysis of reduced error pruning,” Journal of Artificial Intelligence Research, vol. 15, pp. 163–187, 2001.
[21]  F. Esposito, D. Malerba, and G. Semeraro, “A comparative analysis of methods for pruning decision trees,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 5, pp. 476–491, 1997.
[22]  M. K??ri?inen, T. Malinen, and T. Elomaa, “Selective rademacher penalization and reduced error pruning of decision trees,” Journal of Machine Learning Research, vol. 5, pp. 1107–1126, 2004.
[23]  I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann, San Francisco, Calif, USA, 1999.
[24]  J. Q. Candela, C. E. Rasmussen, and C. K. I. Williams, “Approximation methods for Gaussian process regression,” Tech. Rep.,
[25]  C. Walder, K. I. Kim, and B. Sch?lkopf, “Sparse multiscale Gaussian process regression,” in Proceedings of the 25th International Conference on Machine Learning (ICML '08), W. W. Cohen, A. McCallum, and S. T. Roweis, Eds., pp. 1112–1119, ACM Press, Helsinki, Finland.
[26]  M. J. Best and N. Chakravarti, “Active set algorithms for isotonic regression; a unifying framework,” Mathematical Programming, vol. 47, no. 3, pp. 425–439, 1990.
[27]  C. I. C. Lee, “The min-max algorithm and isotonic regression,” The Annals of Statistics, vol. 11, no. 2, pp. 467–477, 1983.
[28]  P. M. Pardalos and G. Xue, “Algorithms for a class of isotonic regression problems,” Algorithmica, vol. 23, no. 3, pp. 211–222, 1999.
[29]  V. de Simone, M. Marina, and G. Toraldo, “Isotonic regression problems,” in Encyclopedia of Optimization, C. A. Floudas and P. M. Pardalos, Eds., Kluwer Academic, Dordrecht, The Netherlands, 2001.
[30]  M. Ayer, H. D. Brunk, G. M. Ewing, W. T. Reid, and E. Silverman, “An empirical distribution function for sampling with incomplete information,” The Annals of Mathematical Statistics, vol. 26, no. 4, pp. 641–647, 1955.
[31]  R. E. Barlow, D. J. Bartholomew, J. M. Bremner, and H. D. Brunk, Statistical Inference under Order Restrictions, Wiley, New York, NY, USA, 1972.
[32]  D. L. Hanson, G. Pledger, and F. T. Wright, “On consistency in monotonic regression,” The Annals of Statistics, vol. 1, no. 3, pp. 401–421, 1973.
[33]  O. Burdakov, O. Sysoev, A. Grimvall, and M. Hussian, “An O(n2) algorithm for isotonic regression,” in Nonconvex Optimization and Its Applications, G. di Pillo and M. Roma, Eds., vol. 83 of Large-Scale Nonlinear Optimization Series, pp. 25–33, Springer, 2006.
[34]  H. Akaike, “A new look at the statistical model identification,” IEEE Transactions on Automatic Control, vol. 19, no. 6, pp. 716–723, 1974.
[35]  S. Ekinci, U. B. Celebi, M. Bal, M. F. Amasyali, and U. K. Boyaci, “Predictions of oil/chemical tanker main design parameters using computational intelligence techniques,” Applied Soft Computing Journal, vol. 11, no. 2, pp. 2356–2366, 2011.
[36]  H. Yan, Y. Jiang, J. Zheng, C. Peng, and Q. Li, “A multilayer perceptron-based medical decision support system for heart disease diagnosis,” Expert Systems with Applications, vol. 30, no. 2, pp. 272–281, 2006.
[37]  V. Havel, J. Martinovic, and V. Snasel, “Creating of conceptual lattices using multilayer perceptron,” in Proceedings of the International Workshop on Concept Lattices and Their Applications (CLA '05), R. Belohlavek and V. Snasel, Eds., pp. 149–157, Olomouc, Czech Republic, 2005.
[38]  A. B. Goktepe, E. Agar, and A. H. Lav, “Role of learning algorithm in neural network-based backcalculation of flexible pavements,” Journal of Computing in Civil Engineering, vol. 20, no. 5, pp. 370–373, 2006.
[39]  D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,” Nature, vol. 323, no. 6088, pp. 533–536, 1986.
[40]  R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, John Wiley & Sons, New York, NY, USA, 2001.
[41]  V. N. Vapnik, The Nature of Statistical Learning Theory, Springer, New York, NY, USA, 1995.
[42]  B. Keshari and S. M. Watt, “Hybrid mathematical symbol recognition using support vector machines,” in Proceedings of the 9th International Conference on Document Analysis and Recognition (ICDAR '07), pp. 859–863, IEEE Computer Society, Curutiba, Brazil, September 2007.
[43]  C.-M. Huang, Y.-J. Lee, D. K. J. Lin, and S.-Y. Huang, “Model selection for support vector machines via uniform design,” Computational Statistics and Data Analysis, vol. 52, no. 1, pp. 335–346, 2007.
[44]  E. Frias-Martinez, A. Sanchez, and J. Velez, “Support vector machines versus multi-layer perceptrons for efficient off-line signature recognition,” Engineering Applications of Artificial Intelligence, vol. 19, no. 6, pp. 693–704, 2006.
[45]  T. Benyang and D. Mazzoni, “Multiclass reduced-set support vector machines,” in Proceedings of the 23rd International Conference on Machine Learning (ICML '06), pp. 921–928, ACM, Pittsburgh, Pa, USA, June 2006.
[46]  J. N. S. Kwong and S. Gong, “Learning support vector machines for a multi-view face model,” in Proceedings of the British Machine Vision Conference (BMVC '99), T. P. Pridmore and D. Elliman, Eds., pp. 503–512, British Machine Vision Association, Nottingham, UK, 1999.
[47]  J. Platt, “Fast training of support vector machines using sequential minimal optimization,” in Advances in Kernel Methods-Support Vector Learning, B. Sch?lkopf, C. Burges, and A. Smola, Eds., MIT Press, 1999.
[48]  M. A. Hall, Correlation-based feature selection for machine learning [Ph.D. thesis], The University of Waikato, Hamilton, New Zealand, 1999.
[49]  J. G. Cleary and L. E. Trigg, “K*: an instance-based learner using on entropic distance measure,” in Proceedings of the 12th International Conference on Machine Learning (ICML '95), A. Prieditis and S. J. Russell, Eds., pp. 108–114, Morgan Kaufmann, Tahoe City, Calif, USA, 1995.
[50]  Y. Zhao, “Learning user keystroke patterns for authentication,” International Journal of Mathematics and Computer Science, vol. 1, pp. 149–154, 2005.
[51]  J. Friedman, T. Hastie, and R. Tibshirani, “Additive logistic regression: a statistical view of boosting,” The Annals of Statistics, vol. 28, no. 2, pp. 337–407, 2000.
[52]  A. Buja, T. Hastie, and R. Tibshirani, “Linear smoothers and additive models,” The Annals of Statistics, vol. 17, no. 2, pp. 453–555, 1989.
[53]  L. Breiman, “Bagging predictors,” Machine Learning, vol. 24, no. 2, pp. 123–140, 1996.
[54]  Türkiye Bankalar Birli?i, “Bankalar?m?z 2005,” Türkiye Bankalar Birli?i Yay?nlar?, ?stanbul, Turkey, 2006, (Turkish).
[55]  A. G. Karegowda, A. S. Manjunath, and M. A. Jayaram, “Comparative study of attribute selection using gain ratio and correlation based feature selection,” International Journal of Information Technology and Knowledge Management, vol. 2, pp. 271–277, 2010.


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