MEVDUAT BANKALARININ UZUN VADELİ PERFORMANSLARININ ÇOK KRİTERLİ KARAR VERME YÖNTEMLERİ İLE DEĞERLENDİRİLMESİ: TÜRKİYE ÖRNEĞİ

Abstract

Bu çalışmada, entegre bir ÇKKV çerçevesi ile Türkiye'de faaliyet gösteren mevduat bankalarının uzun vadeli performanslarının ölçülmesi amaçlanmaktadır. Mevduat bankalarının performansı değerlendirilirken borsa göstergelerinin etkisi de dikkate alınmıştır. Uzun vadeli performans göstergeleri elde etmek için 2014-2018 yılları arasında seçilen oranların aritmetik ortalaması hesaplanmıştır. Kriterlerin ağırlıkları En İyi-En Kötü Yöntemi ile belirlenmiştir. Bankaların uzun vadeli performansını değerlendirmek için ARAS, EDAS, MOORA, OCRA ve TOPSIS olmak üzere beş farklı ÇKKV yöntemi kullanılmıştır. En büyük öneme sahip olan finansal oran, uzman değerlendirmelerine göre likit varlık/toplam varlık oranıdır. Ortalama aktif getirisi ve özsermaye/toplam aktifler oranı, nihai puanla yüksek oranda ilişkili kriterler olarak belirlenmiştir.

Keywords

• Banka Performansı
• Finansal Performans
• Borsa Göstergeleri
• Çok Kriterli Karar Verme
• En İyi-En Kötü Yöntemi

LONG-TERM PERFORMANCE EVALUATION OF DEPOSIT BANKS WITH MULTI-CRITERIA DECISION MAKING TOOLS: THE CASE OF TURKEY

Abstract

In this study, it is aimed to measure the long term performance of deposit banks operating in Turkey with an integrated MCDM framework. The effect of stock market indicators is also considered when evaluating the performance of deposit banks. The arithmetic average of the selected ratios between 2014 and 2018 is calculated to obtain long term performance indicators. The weights of the criteria are set with the Best-Worst Method. Five different MCDM tools, namely ARAS, EDAS, MOORA, OCRA and TOPSIS, are used to evaluate the long-term performance of banks. The financial ratio, which has the highest importance, is liquid asset / total assets ratio, according to the expert evaluations. The average return on assets and shareholders’ equity/total assets ratio is determined as highly correlated criteria with the final score.

Keywords

• Bank Performance
• Financial Performance
• Stock Market Indicators
• Multi-Criteria Decision Making
• Best-Worst Method

References

1. Akgül, Y. (2019), “Çok Kriterli Karar Verme Yöntemleriyle Türk Bankacilik Sisteminin 2010-2018 Yılları Arasindaki Performansinin Analizi”, Finans Ekonomi ve Sosyal Araştırmalar Dergisi (FESA), 4(4), 567-582.
2. Akkoç, S. ve Vatansever, K. (2013), “Fuzzy Performance Evaluation with AHP and Topsis Methods: Evidence from Turkish Banking Sector After the Global Financial Crisis”, Eurasian Journal of Business and Economics, 6(11), 53–74.
3. Albayrak, Y. E. ve Erkut, H. (2005), “Analytic Hierarchy Process Approach in Bank Performance Evaluation”, İstanbul Teknik Üniversitesi Dergisi, 4(6), 47–58.
4. Amile, M., Sedaghat, M. ve Poorhossein, M. (2013), “Performance Evaluation of Banks Using Fuzzy AHP and TOPSIS, Case study: State-owned Banks, Partially Private and Private Banks in Iran”, Caspian Journal of Applied Sciences Research, 2(3), 128–138.
5. Angelov, P. ve Gu, X. (2019), Empirical Approach to Machine Learning, Springer.
6. Bayyurt, N. (2013), “Ownership Effect on Bank's Performance: Multi Criteria Decision Making Approaches on Foreign and Domestic Turkish Banks”, Procedia-Social and Behavioral Sciences, 99, 919-928.
7. Beheshtinia, M. A. ve Omidi, S. (2017), “A Hybrid MCDM Approach for Performance Evaluation in the Banking Industry”, Kybernetes, 46(8), 1386–1407.
8. Bozdogan, T., Akyuz, Y. ve Hantekin, E. (2013), “Evaluation of the Financial Performance with Analytic Hierarchy Process: An Application in the Banking Sector”, Finans Politik & Ekonomik Yorumlar, 20(575), 71–83.

9. Brauers, W. K. M. ve Zavadskas, E. (2006), “The MOORA method and Its Application to Privatiziation in a Transition Economy”, Control and Cybernetics, 35(2), 445–469.
10. Celen, A. (2014), “Comperative Analysis of Normalization Procedures in TOPSIS Method: with an Application to Turkish Deposit Banking Market”, Informatica, 25(2), 185–208.
11. Cetın, M. K. ve Cetın, E. I. (2010), “Multi-Criteria Analysis of Banks’ Performances”, International Journal of Economics and Finance Studies, 2(2), 73-78.
12. Chan, S. G. ve Karim, M. Z. A. (2010), “Bank Efficiency and Macro-Economic Factors: The Case of Developing Countries”, Global Economic Review, 39(3), 269-289.
13. Chitnis, A. ve Vaidya, O. S. (2018), “Efficiency Ranking Method Using SFA and TOPSIS (ERM-ST): Case of Indian Banks”, Benchmarking, 25(2), 471–488.
14. Çalışkan, E. ve Eren, T. (2016), “Bankaların Performanslarının Çok Kriterli Karar Verme Yöntemiyle Değerlendirilmesi”, Ordu Üniversitesi Bilim ve Teknoloji Dergisi, 6(2), 85-107.
15. Demireli, E. (2010), “TOPSIS Multi-Criteria Decision Making Method: An Examination on State Owned Commercial Banks in Turkey”, Journal of Entrepreneurship and Development, 5(1), 101–112.
16. Dincer, H. ve Gorener, A. (2011), “Dynamic Performance Analysis Via Analytic Hierarchy Process and VIKOR Technique: An Application for Banking Sector”, Istanbul Ticaret Universitesi Sosyal Bilimler Dergisi, 10(19), 109–127.
17. Dincer, H., Yüksel, S. ve Kartal, M. T. (2016), “Evaluating the Corporate Governance Based Performance of Participation Banks in Turkey with the House of Quality Using An Integrated Hesitant Fuzzy MCDM”, BDDK Bankacılık ve Finansal Piyasalar Dergisi, 10(1), 9-33.
18. Dogan, M. (2013), “Measuring Bank Performance with Gray Relational Analysis: The Case of Turkey”, Ege Akademik Bakis (Ege Academic Review), 13(2), 215–225.
19. Elsayed, E. A., Daqood, A. K. S. ve Karthikeyan, R. (2017), “Evaluating Alternatives Through the Application of TOPSIS Method with Entropy Weight”, International Journal of Engineering Trends and Technology, 42(2), 60–66.
20. Fukuyama, H., Färe, R. ve Weber, W. L. (2018), “Valuing and Ranking Japanese Banks: Application to Japan Post Bank and Mizuho Bank”, Journal of the Operational Research Society, 69(12), 2021-2033.
21. Ghasempour, S. ve Salami, M. (2016), “Ranking Iranian Private Banks Based on the CAMELS Model Using the AHP Hybrid Approach and TOPSIS”, International Journal of Academic Research in Accounting, Finance and Management Sciences, 6(4), 52–62.
22. Ghorabaee, M. K., Zavadskas, E. K., Olfat, L. ve Turskis, Z. (2015), “Multi-Criteria Inventory Classification Using a New Method of Evaluation Based on Distance from Average Solution (EDAS)”, Informatica (Netherlands), 26(3), 435-451.
23. Gundogdu, A. (2015), “Measurement of Financial Performance Using TOPSIS Method for Foreign Banks of Established in Turkey Between 2003-2013 Years”, International Journal of Business and Social Science, 6(1), 139–151.
24. Ho, C. T. (2006), “Measuring Bank Operations Performance: An Approach Based on Grey Relation Analysis”, Journal of the Operational Research Society, 57(4), 337–349.
25. Hunjak, T. ve Jakovčević, D. (2001), “AHP Based Model for Bank Performance Evaluation and Rating”, 6th ISAHP, Berne, August 2-4.
26. Hwang, C. ve Yoon, K. (1981), Multiple Attribute Decision Making: Methods and Applications, A State of the Art Survey, Springer-Verlag.
27. Kandemir, T. ve Karatas, H. (2016), “The Comparison of Financial Performances of Depository Banks by Multi-criteria Decision Making Methods: An Application on the Banks Traded in Borsa Istanbul (2004-2014)”, İnsan ve Toplum Bilimleri Araştırmaları Dergisi, 5(7), 1766–1776.
28. Keskin, E., İnan, E. A. ve Ünsal, Ü. (2019), 60th Year of Banks Association of Turkey and Banking System in Turkey, İstanbul: The Bank Association of Turkey.
29. Kosmidou, K. ve Zopounidis, C. (2008), “Measurement of Bank Performance in Greece”, South-Eastern Europe Journal of Economics, 1, 79–95.
30. Kundakci, N. (2017), “An integrated Multi-Criteria Decision Making Approach for Tablet Computer Selection”, European Journall of Multidisciplinary Studies, 5(1), 36–48.
31. Mandic, K., Delibasic, B., Knezevic, S. ve Benkovic, S. (2014), “Analysis of the Financial Parameters of Serbian Banks Through the Application of the Fuzzy AHP and TOPSIS Methods”, Economic Modelling, 43, 30-37.
32. Mateo, J. R. S. C. (2015), Management Science, Operations Research and Project Management Modelling, Evaluation, Scheduling, Monitoring, Gurray: Gower Publishing Limited.
33. Mishkin, F. S. ve Eakins, S. G. (2015), Financial Markets and Institutions, Boston: Pearson Education.
34. Onder, E., Tas, N. ve Hepsen, A. (2013), “Performance Evaluation of Turkish Banks Using Analytical Hierarchy Process and TOPSIS Methods”, Journal of International Scientific Publication, 7(1), 470–503.
35. Oral, C. (2016), “Evaluating the Financial Performances of Privately Owned Deposit Banks in Turkey by TOPSIS Method”, Journal of Business Research Turk, 8(1), 448–455.
36. Özbek, A. (2015), “Performance Analysis of Public Banks in Turkey”, International Journal of Business Management and Economic Research (IJBMER), 6(3), 178-186.
37. Ozdemir, A. ve Demireli, E. (2013), “A Comparative Financial Performance Analysis of Deposit Banks with ANP-TOPSIS and ANP-VIKOR Integrated Approaches: An Application on Istanbul Stock Exchange (XU Bank)”, Finans Politik & Ekonomik Yorumlar, 50(584), 59-80.
38. Paksoy, S. ve Tıras, M. F. (2017), “Investigating Banks’ Performance for Turkey: An Application of PROMETHEE Method”, Çukurova Üniversitesi İİBF Dergisi, 18(1), 143-159.
39. Parkan, C. (1994), “Operational Competitiveness Ratings of Production Units”, Managerial and Decision Economics, 15(3), 201–221.
40. Rakocevic, S. ve Dragasevic, Z. (2009), “Analysis of the Efficiency of Banks in Montenegro Using the AHP”, ISAHP, Pittsburgh.
41. Ren, J., Liang, H. ve Chan, F. T. S. (2017), “Urban Sewage Sludge, Sustainability, and Transition for Eco-City: Multi-criteria sustainability assessment of technologies based on Best-Worst Method”, Technological Forecasting and Social Change, 116, 29-39.
42. Rezaei, J. (2015), “Best-worst Multi-Criteria Decision-Making Method”, Omega (United Kingdom), 53, 49-57.
43. Rezaei, M. ve Ketabi, S. (2016), “Ranking the Banks through Performance Evaluation by Integrating Fuzzy AHP and TOPSIS Methods: A Study of Iranian Private Banks”, International Journal of Academic Research in Accounting, Finance and Management Sciences, 6(3), 19-30.
44. Saunders, A. ve Millon Cornett, M. (2019), Financial Markets and Institutions, McGraw-Hill Education.
45. Seçme, N. Y., Bayrakdaroǧlu, A. ve Kahraman, C. (2009), “Fuzzy Performance Evaluation in Turkish Banking Sector Using Analytic Hierarchy Process and TOPSIS”, Expert Systems with Applications, 36(9), 11699-11709.
46. Shaverdi, M., Akbari, M. ve Fallah Tafti, S. (2011), “Combining Fuzzy MCDM with BSC Approach in Performance Evaluation of Iranian Private Banking Sector”, Advances in Fuzzy Systems, 1-12.
47. Sisman, B. ve Doğan, M. (2016), “The Evaluations of Financial Performance in Turkish Banks by Using Fuzzy AHP and Fuzzy MOORA”, Yönetim ve Ekonomi, 23(2), 353–371.
48. Taşkın, F. D. (2011), “Türkiye’de Ticari Bankaların Performansını Etkileyen Faktörler”, Ege Akademik Bakış, 11(2), 289-298.
49. Topak, M. S. ve Çanakçioğlu, M., (2019), “Banka Performansının ENTROPİ ve COPRAS Yöntemi İle Değerlendirilmesi: Türk Bankacılık Sektörü Üzerine Bir Araştırma”, Mali Çözüm Dergisi, 29(154), 107-132.
50. Tunay, K. B. ve Akhisar, I. (2015), “Investigating Performances of Public and Private Banks in Turkey by Using Grey Relation Analysis”, Management International Conference, Slovenia.
51. Tzeng, G. H. ve Huang, J. J. (2011), Multiple Attribute Decision Making: Methods and Applications, Boca Raton: CRC Press.
52. Uckun, N. ve Girginer, N. (2011), “Investigating Performances of Public and Private Banks in Turkey by Using Grey Relation Analysis”, Akdeniz İİBF Dergisi, 21, 46–66.
53. Uygurtürk, H. ve Korkmaz, T. (2012), “Finansal Performansın TOPSIS Çok Kriterli Karar Verme Yöntemi ile Belirlenmesi: Ana Metal Sanayi İşletmeleri Üzerine Bir Uygulama”, Eskişehir Osmangazi Üniversitesi İktisadi ve İdari Bilimler Dergisi, 7(2), 95-115.
54. Wanke, P., Azad, M. A. K., Barros, C. P. ve Hassan, M. K. (2016), “Predicting Efficiency in Islamic Banks: An Integrated Multicriteria Decision Making (MCDM) Approach”, Journal of International Financial Markets, Institutions and Money, 45, 126-141.
55. Wu, H. Y., Tzeng, G. H. ve Chen, Y. H. (2009), “A Fuzzy MCDM Approach for Evaluating Banking Performance Based on Balanced Scorecard”, Expert Systems with Applications, 36(6), 10135-10147.
56. Yüksel, S., Dinçer, H. ve Emir, Ş. (2017), “Comparing the Performance of Turkish Deposit Banks by Using DEMATEL, Grey Relational Analysis (GRA) and MOORA Approaches”, World Journal of Applied Economics, 3(2), 26–47.
57. Zavadskas, E. K., Turskıs, Z. ve Vilutiene, T. (2010), “Multiple Criteria Analysis of Foundation Instalment Alternatives by Applying Additive Ratio Assessment (ARAS) Method”, Archives of Civil and Mechanical Engineering, 10(3), 123-141.
58. Zopounidis, C. ve Pardalos, P. M. (2010), Handbook of Multicriteria Analysis, Verlag Berlin: Springer.