Applying a two-stage TOPSIS approach and stepwise regression in evaluating bank performance: evidence from Turkish banks
-
Received August 8, 2019;Accepted December 2, 2019;Published December 13, 2019
-
Author(s)Link to ORCID Index: https://orcid.org/0000-0002-9536-5541Link to ORCID Index: https://orcid.org/0000-0002-8335-8619
-
DOIhttp://dx.doi.org/10.21511/bbs.14(4).2019.11
-
Article InfoVolume 14 2019, Issue #4, pp. 114-125
- TO CITE АНОТАЦІЯ
-
Cited by3 articlesJournal title: MANAS Sosyal Araştırmalar DergisiArticle title: Borsa İstanbul Banka Endeksi’nde (BİST Banka) Yer Alan Bankaların Performanslarının TOPSİS Yöntemi İle AnaliziDOI: 10.33206/mjss.962252Volume: 11 / Issue: 2 / First page: 612 / Year: 2022Contributors: Selcuk KENDİRLİ, Sevim ERGENOĞLUJournal title: Yönetim Bilimleri DergisiArticle title: EVALUATION OF TURKISH FACTORING COMPANY PERFORMANCES USING TOPSIS METHODDOI: 10.35408/comuybd.836726Volume: 20 / Issue: 43 / First page: 29 / Year: 2022Contributors: Alper OVAJournal title: Journal of Intelligent & Fuzzy SystemsArticle title: Selection of Best E-Rickshaw-A Green Energy Game Changer: An Application of AHP and TOPSIS MethodDOI: 10.3233/JIFS-202406Volume: 40 / Issue: 6 / First page: 11217 / Year: 2021Contributors: Arijit Ghosh, Munmun Dey, Sankar Prasad Mondal, Azharuddin Shaikh, Anirban Sarkar, Banashree Chatterjee
- 1098 Views
-
145 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
As a crucial component of the financial sector, banks play an intermediary role in creating and providing financial services to customers. Therefore, the evaluation of banking sector activity is important for stakeholders and managers. This paper investigates the key criteria in analyzing bank performance and efficiency and the relative performance of Turkish banks in terms of the pre-determined criteria during 2008–2018. This study aims to introduce a robust and easy-to-calculate mathematical model for estimating bank performance using stepwise regression and TOPSIS methods. The TOPSIS ranking of banks from the best to the worst allows establishing that the bank with the highest mean score is Akbank (AB), while Ziraat Bank (ZB) and Garanti Bank (GB) follow AB over the period. The results of the stepwise regression analysis show that managing non-performing loans and expenses (both personnel and interest expenses) are critical to high performance in the banking sector.
- Keywords
-
JEL Classification (Paper profile tab)G21, C60, C50
-
References36
-
Tables10
-
Figures1
-
- Figure 1. Performance scores for the second stage
-
- Table 1. Turkish banks included in the study
- Table 2. Financial performance criteria
- Table 3. Descriptive statistics of financial ratios (criteria)
- Table 4. Performance scores of the first stage
- Table 5. Rankings of banks at the first stage
- Table 6. Stepwise regression analysis results
- Table 7. Diagnostic test results
- Table 8. Stationarity test results
- Table 9. Performance scores of the second stage
- Table 10. Second stage rankings of banks
-
- Akyüz, Y., & Boratav, K. (2003). The Making of Turkish Financial Crisis. World development, 31(9), 1549-1566.
- Alper, C. E., & Öniş, Z. (2004). The Turkish Banking System and the IMF in the Age of Capital Account Liberalization. New Perspectives on Turkey, 30, 25-55.
- Assaf, A. G., Matousek, R., & Tsionas, E. G. (2013). Turkish bank efficiency: Bayesian estimation with undesirable outputs. Journal of Banking & Finance, 37(2), 506-517.
- Aysan, A. F., & Ceyhan, Ş. P. (2008). What determines the banking sector performance in globalized financial markets? The case of Turkey. Physica A: Statistical Mechanics and its Applications, 387(7), 1593-1602.
- Barros, C. P., & Wanke, P. (2015). An analysis of African airlines efficiency with two-stage TOPSIS and neural networks. Journal of Air Transport Management, 44, 90-102.
- Berger, A. N., & Humphrey, D. B. (1997). Efficiency of financial institutions: International survey and directions for future research. European Journal of Operational Research, 98(2), 175-212.
- Chao, C., Yu, M., & Wu, H. (2015). An application of the dynamic network DEA Model: The case of banks in Taiwan. Emerging Markets Finance and Trade, 51(1), 133-151.
- Doğan, M. (2013). Measuring Bank Performance with Gray Relational Analysis: The Case of Turkey. Ege Akademik Bakış Dergisi, 13(2), 215-226.
- Dong, Y., Firth, M., Hou, W., & Yang, W. (2016). Evaluating the performance of Chinese commercial banks: A comparative analysis of different types of banks. European Journal of Operational Research, 252(1), 280-295.
- Doumpos, M., & Zopounidis, C. (2010). A multicriteria decision support system for bank rating. Decision Support Systems, 50(1), 55-63.
- Ertugrul, I., & Karakasoglu, N. (2009) Performance Evaluation of Turkish Cement Firms with Fuzzy Analytic Hierarchy Process and TOPSIS Methods. Expert Systems with Applications, 36, 702-715.
- Fernandes, F. D. S., Stasinakis, C., & Bardarova, V. (2018). Two-stage DEA-Truncated Regression: Application in banking efficiency and financial development. Expert Systems with Applications, 96, 284-301.
- Fethi, M. D., & Pasiouras, F. (2010). Assessing bank efficiency and performance with operational research and artificial intelligence techniques: A survey. European journal of operational research, 204(2), 189-198.
- Fukuyama, H., & Matousek, R. (2011). Efficiency of Turkish banking: Two-stage network system. Variable returns to scale model. Journal of International Financial Markets, Institutions and Money, 21(1), 75-91.
- Gavurova, B., Belas, J., Kocisova, K., & Kliestik, T. (2017). Comparison of selected methods for performance evaluation of Czech and Slovak commercial banks. Journal of Business Economics and Management, 18(5), 852-876.
- Goldberger, A. S. (1961). Stepwise least squares: residual analysis and specification error. Journal of the American Statistical Association, 56(296), 998-1000.
- Hsiao, C., Shen, Y. & Bian, W. (2015). Evaluating the effectiveness of China’s financial reform – The efficiency of China’s domestic banks. China Economic Review, 35, 70-82.
- Huang, T. H., Lin, C. I., & Chen, K. C. (2017c). Evaluating efficiencies of Chinese commercial banks in the context of stochastic multistage technologies. Pacific-Basin Finance Journal, 41, 93-110.
- Hwang, C. L., & Yoon, K. (1981). Multiple attribute decision making. New York: Springer.
- Lee, J. Y., & Kim, D. (2013). Bank performance and its determinants in Korea. Japan and the World Economy, 27, 83-94.
- Mandic, K., Delibasic, B., Knezevic, S., & 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.
- Paradi, J. C., Rouatt, S., & Zhu, H. (2011). Two-stage evaluation of bank branch efficiency using data envelopment analysis. Omega, 39(1), 99-109.
- Rawlings, J. O., Pantula, S. G., & Dickey, D. A. (2001). Applied regression analysis: a research tool. Springer Science & Business Media.
- Salim, R., Arjomandi, A., & Seufert, J. H. (2016). Does corporate governance affect Australian banks’ performance? Journal of International Financial Markets, Institutions and Money, 43, 113-125.
- Sayılgan, G., & Yıldırım, O. (2009). Determinants of Profitability in Turkish Banking Sector: 2002-2007. International Research Journal of Finance and Economics, 28, 207-214.
- Seçme, N. Y., Bayrakdaroğlu, A., & Kahraman, C. (2009). Fuzzy performance evaluation in Turkish banking sector using analytic hierarchy process and TOPSIS. Expert Systems with Applications, 36(9), 11699-11709.
- Sekmen, T., Akkus, O., & Sıklar, I. (2015). Competitive Conditions in the Turkish Banking Systems. Journal of Business, Economics and Finance, 4(1), 53-68.
- Staub, R. B., Souza, G. D. S., & Tabak, B. M. (2010). Evolution of bank efficiency in Brazil: A DEA approach. European Journal of Operational Research, 202(1), 204-213.
- Svitalkova, Z. (2014). Comparison and evaluation of bank efficiency in selected countries in EU. Procedia Economics and Finance, 12, 644-653.
- The Banks Association of Turkey. (2019). The Banking System in Turkey: Selected Ratios.
- Titko, J., & Jureviciene, D. (2013). DEA application at cross-country benchmarking: Latvian vs. Lithuanian banking sector. Procedia Social and Behavioral Sciences, 110, 1124-1135.
- Wang, K., Huang, W., Liu, Y., (2014). Efficiency measures of the Chinese commercial banking system using an additive two-stage DEA. Omega 44, 5-20.
- Wanke, P., Azad, M. A. K., & Barros, C. P. (2016a). Predicting efficiency in Malaysian Islamic banks: A two-stage TOPSIS and neural networks approach. Research in International Business and Finance, 36, 485-498.
- Wanke, P., Azad, M. A. K., Barros, C. P., & Hassan, M. K. (2016b). Predicting efficiency in Islamic banks: An integrated multicriteria decision making (MCDM) approach. Journal of International Financial Markets, Institutions and Money, 45, 126-141.
- Wanke, P., Azad, M. A. K., & Barros, C. P. (2016c). Efficiency factors in OECD banks: A ten-year analysis. Expert Systems with Applications, 64, 208-227.
- Wu, H. Y., Tzeng, G. H., & Chen, Y. H. (2009). A fuzzy MCDM approach for evaluating banking performance based on Balanced Scorecard. Expert Systems with Applications, 36(6), 10135-10147.
-
Ownership structure and bank performance
Hamdi Agustin , Sri Indrastuti , Amris Rusli Tanjung , Muhammad Said doi: http://dx.doi.org/10.21511/bbs.13(1).2018.08Banks and Bank Systems Volume 13, 2018 Issue #1 pp. 80-87 Views: 1898 Downloads: 501 TO CITE АНОТАЦІЯThe purpose of this study is to evaluate the performance of banks in Indonesia. Specifically, this study has examined the static effect of ownership structure on bank performance in Indonesia over the period 1995–2006. The sample consists of 74 banks, namely 56 private banks, 15 community development banks (BPD), and three federal banks from 1995 to 2006. The data was analyzed using least-squares regression method, the general least squares method, and the method of random effects. The findings of this study show that the BPD performed better compared to private banks. This indicates that BPDs have better performance rather than private banks which is due to the fact that customers can be able to pay loans, they have special knowledge on that area and the performance of BPD is supervised by local government. In addition, the amount of equity, economic growth, financial crisis, and the financial ratios affect the performance of the bank. However, bank status has no effect on bank performance.
-
The impact of oil price crisis on financial performance of commercial banks in Bahrain
Iqbal Thonse Hawaldar , Babitha Rohit , Prakash Pinto , Rajesha T. M. doi: http://dx.doi.org/10.21511/bbs.12(4).2017.01Banks and Bank Systems Volume 12, 2017 Issue #4 pp. 4-16 Views: 1881 Downloads: 428 TO CITE АНОТАЦІЯOil export is the major source of revenue for the countries in the Middle East. Their economies are sensitive to fluctuations in oil prices. The present study examines the impact of oil crisis on the performance of selected banks of Kingdom of Bahrain using profitability, efficiency, capital adequacy and liquidity ratios in the pre-crisis and crisis periods. The study reveals that there is no significant difference in the performance of banks in the pre-crisis and crisis period. The results indicate that there is a significant difference in the performance of conventional banks and Islamic banks in the pre-crisis period. However, there is no significant difference in the performance of conventional banks and Islamic banks during the crisis period.
-
Digital banking impact on Turkish deposit banks performance
Banks and Bank Systems Volume 13, 2018 Issue #3 pp. 48-57 Views: 1801 Downloads: 968 TO CITE АНОТАЦІЯThe technological developments in the banking sector have significant implications for banks and are dramatically changing the way retail banks conduct their business. Banks can invest in digital banking (DB) services either to acquire a strategic advantage or because doing so has become a strategic necessity. This study is organized to examine if DB service channels have any positive or negative impact on Turkish deposit banks’ performance. With this aim in mind, in the first stage of the proposed DEA model, physical assets are used. Then, in the second stage, DB service channels are added to see if they have any impact on banks’ performance. The results show that the banks are investing in DB services just to keep the competition as it is. In other words, they invest in DB services as a strategic necessity. DB services do not provide any strategic advantage to any banks in terms of financial performance or efficiency since the banks are already efficient. Investing in DB only helped to preserve their strategic positions. The Turkish deposit banking industry is very competitive and very profitable, and it is necessary to invest in DB services just to keep the competition as it is.