Performance Assessment of Deposit Banks with CAMELS Analysis
Transkript
Performance Assessment of Deposit Banks with CAMELS Analysis
Asian Research Consortium Asian Journal of Research in Business Economics and Management Vol. 6, No. 2, February 2016, pp. 32-56. Asian Journal of Research in Business Economics and Management ISSN 2249-7307 A Journal Indexed in Indian Citation Index www.aijsh.org Performance Assessment of Deposit Banks with CAMELS Analysis using Fuzzy ANP- MOORA Approaches and an Application on Turkish Banking Sector Hasan Dinçer*; Ümit Hacıoğlu**; Serhat Yüksel*** *Associate Professor, School of Business and Management Sciences, Istanbul Medipol University, Kavacik, Istanbul, Turkey. **Associate Professor, School of Business and Management Sciences, Istanbul Medipol University, Kavacik, Istanbul, Turkey. ***Department of Board of Auditors, Finansbank, İstanbul, Turkey. DOI NUMBER-10.5958/2249-7307.2016.00009.8 Abstract Performance assessment in banking sector has been attached to investment decisions and efficiency analysis in the last decade. This study compares banking performance with using novel techniques in Turkey. The aim of this study is to assess the performance of Turkish deposit banks with the application of CAMELS analysis using Fuzzy ANP and MOORA approaches within the fuzzy environment. In this study, the novel hybrid model has been adopted to CAMELS analysis with related 17 different ratios for 23 deposits banks in Turkey. The major findings of this study are (i)“capital adequacy” is the most significant component of CAMELS approach, which contributes to banking stability and performance whereas “sensitivity to market risk” is the least important one among the other 4 major components, and (ii) when viewing the overall ranking, Bank 13 with the 13.1 percent of asset size in the sector is at the first rank for the CAMELS-based performance comparison, (iii) Bank 18with the 1.8 percent of asset size in the sector, has the lowest performance score, (iv) there is a positive relationship between asset size and banking performance in Turkey. Keywords: Banking; Performance; CAMELS; FUZZY; ANP; MOORA. 32 Dincer et al. (2016). Asian Journal of Research in Business Economics and Management, Vol. 6, No. 2, pp. 32-56. References Abreu, M. and Mendes, V. (2001). Commercial bank interest margins and profitability: evidence for some EU countries. In Pan-European Conference Jointly Organised by the IEFS-UK and University of Macedonia Economic & Social Sciences, Thessaloniki, Greece, 17-20. Atasoy, H. (2007). Türk bankacılık sektöründe gelir-gider analizi ve kârlılık performansının belirleyicileri. Türkiye Cumhuriyet Merkez Bankası, Uzmanlık Yeterlilik Tezi, Ankara. Athanasoglou, P. P., Brissimis, S. N. and Delis, M. D. (2008). Bank-specific, industry-specific and macroeconomic determinants of bank profitability. Journal of international financial Markets, Institutions and Money, 18, 121-136. Awdeh, A. (2005). Domestic banks‟ and foreign banks‟ profitability: differences and their determinants. Cass Business School, London. Babar, H. (2011). Camels Rating System for Banking Industry in Pakistan (Master Thesis). Umea School of Business, Pakistan. Balezentis, T. (2011).A Farming Efficiency Estimation Model Based on Fuzzy Multimoora.Management Theory and Studies for Rural Business and Infrastructure Development, 29, 43-52. Berger, A. N., Clarke, G. R., Cull, R., Klapper, L. and Udell, G. F. (2005). Corporate governance and bank performance: A joint analysis of the static, selection, and dynamic effects of domestic, foreign, and state ownership. Journal of Banking & Finance, 29, 2179-2221. Brauers, W.K.M. and Zavadskas, E.K. (2006).The MOORA Method and Its Application to Privatization in a Transition Economy.Control and Cybernetics, 35, 445-469. Brauers, W.K.M. and Zavadskas, E.K. (2012). Robustness of MULTIMOORA: A Method for Multi-Objective Optimization. Informatica, 23, 1-25. Chang, B., Kuo, C., Wu, C. and Tzeng, G. (2015).Using Fuzzy Analytic Network Process to Assess the Risks in Enterprise Resource Planning System Implementation.Applied Soft Computing, 28, 196-207. Chen, J. and Yang, Y. (2011).A Fuzzy ANP-Based Approach to Evaluate Region Agricultural Drought Risk.Procedia Engineering, 23, 822-827. Christopoulos, A., Mylonakis, J. and Diktapanidis, P. (2011). Could Lehman Brothers‟ Collapse Be Anticipated? An Examination Using CAMELS Rating System. International Business Research, 4, 11-19. Çağıl, G. and Mukhtarov, S. (2014). Azerbaycan Ticari Bankacılık Sektörünün CAMELS Yöntemi ile Performans Analizi. Marmara Üniversitesi Öneri Dergisi, 41, 77-94. 52 Dincer et al. (2016). Asian Journal of Research in Business Economics and Management, Vol. 6, No. 2, pp. 32-56. Çinko, M. and Avcı, E. (2008). CAMELS Derecelendirme Sistemi ve Türk Ticari Bankacılık Sektöründe Başarısızlık Tahmini. BDDK Bankacılık ve Finansal Piyasalar Dergisi, 2, 25-49. Dağdeviren, M., Yüksel, İ. and Kurt, M. (2008).A Fuzzy Analytic Network Process (ANP) Model to IdentifyFaulty Behavior Risk (FBR) in Work System.Safety Science, 46, 771-783. Dargi, A., Anjomshoae, A., Galankashi, M., Memari, A. and Tap, M. (2014). Supplier Selection: A Fuzzy-ANP Approach. Procedia Computer Science, 31, 691-700. Dash, M. and Das, A. (2010). A CAMELS Analysis of the Indian Banking Industry. Global Business Review, 2010, 11, 257-280. Demirgüç-Kunt, A.and Huizinga, H. (1999). Determinants of commercial bank interest margins and profitability: some international evidence. The World Bank Economic Review, 13, 379-408. Derviz, A. and Podpiera, J. (2004). Predicting Bank CAMELS and S&P Ratings: The Case of the Czech Republic. Czech National Bank Working Paper Series, 44, 117-130. Dinçer, H. (2015). Profit-based Selection Approach in Banking Sector Using Fuzzy AHP and MOORA Method. Global Business and Economics Research Journal, 4, 1-26. Dinçer, H., Gencer, G., Orhan, N. and Şahinbaş, K. (2011). A performance evaluation of the Turkish banking sector after the global crisis via CAMELS ratios. Procedia-Social and Behavioral Sciences, 24, 1530-1545. Ecer, F. (2013). Türkiye‟deki Özel Bankaların Finansal Performanslarının Karşılaştırılması: 20082011 Dönemi. AİBÜ Sosyal Bilimler Enstitüsü Dergisi, 2, 171-189 Gilbert, A., Meyer, A. and Vaughan, M. (2002) Could a CAMELS Downgrade Model Improve Off-Site Surveillance? The Federal Reserve Bank of St. Louis Working Papers, 84, 47-63. Goddard, J., Molyneux, P. and Wilson, J. O. (2004). The profitability of European banks: a cross‐sectional and dynamic panel analysis. The Manchester School, 72, 363-381. Gülhan, Ü. and Uzunlar, E. (2011).BankacılıkSektöründeKârlılığıEtkileyenFaktörler: TürkBankacılıkSektörüneYönelikBirUygulama.Atatürk ÜniversitesiSosyalBilimlerEnstitüsüDergisi, 15, 341-368. Güneri, A, Cengiz, M. and Seker, S. (2009). A fuzzy ANP Approach to Shipyard Location Selection. Expert Systems with Applications, 36, 7992, 7999. Hassan, M. K. and Bashir, A. H. M. (2003, December). Determinants of Islamic banking profitability. In 10th ERF Annual Conference, Morocco (pp. 16-18). Jiang, G., Tang, N., Law, E. and Sze, A. (2003). The profitability of banking sector in Hong Kong. Hong Kong Monetary Authority Quarterly Bulletin, 5-14. 53 Dincer et al. (2016). Asian Journal of Research in Business Economics and Management, Vol. 6, No. 2, pp. 32-56. Kandemir, T. and Demirel Arıcı, N. (2013). Mevduat Bankalarında CAMELS Performans Değerleme Modeli Üzerine Karşılaştırmalı Bir Çalışma (2002-2010). Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 1, 61-87. Kaur, P. and Mahanti, N. (2008).A Fuzzy ANP-based Approach for Selecting ERP Vendors.International Journal of Soft Computing, 3, 24-32. Kao, C. and Liu, S. (2004). Predicting Bank Performance with Financial Forecasts: A Case of Taiwan Commercial Banks.Journal of Banking & Finance, 28, 2353–2368. Kaufman, G. G. (1992). Capital in banking: past, present and future. Journal of Financial Services Research, 5, 385-402. Keeley, M. C.and Furlong, F. T. (1990). A reexamination of mean-variance analysis of bank capital regulation. Journal of Banking & Finance, 14, 69-84. Kosmidou, K. and Zopounidis, C. (2008). Measurement of bank performance in Greece. South Eastern Europe Journal of Economics, 6, 79-95. Maudos, J. (1998). Market structure and performance in Spanish banking using a direct measure of efficiency. Applied financial economics, 8, 191-200. Mekonnen, Y. and Kedir, H. (2015). Soundness of Ethiopian Banks. International Journal of Finance & Banking Studies, 4, 29-37 Mohanty, R., Agarwal, R., Choudhury, A. and Tiwari, M. (2005). A fuzzy ANP-based Approach to R&D Project Selection: A Case Study. International Journal of Production Research, 43, 5199-5216. Molyneux, P. and Thornton, J. (1992). Determinants of European bank profitability: A note. Journal of Banking and Finance, 16, 1173-1178. Naceur, S. B. (2003). The determinants of the Tunisian banking industry profitability: Panel evidence. Universite Libre de Tunis Working Papers. Nimalathasan, B. (2008). A comparative study of financial performance of banking sector in Bangladesh – An application of CAMELS rating. Annals of University of Bucharest, Economic and Administrative Series, 2, 141,152. Pasiouras, F. and Kosmidou, K. (2007).Factors influencing the profitability of domestic and foreign commercial banks in the European Union.Research in International Business and Finance, 21, 222-237. Persons, O. (1999). Using Financial Information to Differentiate Failed vs. Surviving Finance Companies in Thailand: An Implication for Emerging Economies. Multinational Finance Journal, 3, 127-145. 54 Dincer et al. (2016). Asian Journal of Research in Business Economics and Management, Vol. 6, No. 2, pp. 32-56. Roman, A. and Şargu, A. (2013). Analysing the Financial Soundness of the Commercial Banks in Romania: An Approach Based on the Camels Framework. Procedia Economics and Finance, 6, 703-712. Saaty, T.L. (1990). How to Make a Decision: The Analytical Hierarchy Process. European Journal of Operation Research, 48, 9-26. Sarker, A.A, (2006). CAMELS Rating System in the Context of Islamic Banking: A Proposed „S‟ for Shariah Framework. Journal of Islamic Economics, Banking and Finance, 2, 212-229. Saunders, A. and Schumacher, L. (2000). The determinants of bank interest rate margins: an international study. Journal of International Money and Finance, 19, 813-832. Shafiee, M. (2015). A Fuzzy Analytic Network Process Model to Mitigate the Risks Associated with Offshore Wind Farms. Expert Systems with Applications, 42, 2143-2152. Taşkın, F. D. (2011). Türkiye‟de Ticari Bankaların Performansını Etkileyen Faktörler. Ege Akademik Bakış, 11, 289-298. Thomson, J. (1991). Predicting Bank Failures in the 1980s. Federal Reserve Bank of St. Louis Economic Review, 1, 9-20. Tunay, K. B. and Silpar, A. M. (2006). Türk Ticari Bankacılık Sektöründe Karlılığa Dayalı Performans Analizi-I. Türkiye Bankalar Birliği, Araştırma Tebliğleri Serisi, 1. Türker Kaya, Y. (2001). Türk Bankacılık Sektöründe CAMELS Analizi. MSPD Çalışma Raporları, 1-20. Vatansever, K. and Uluköy, M. (2013).KurumsalKaynakPlanlamasıSistemlerininBulanıkAHP veBulanık MOORA YöntemleriyleSeçimi: ÜretimSektöründeBirUygulama. CBÜ SosyalBilimlerDergisi, 11, 274-293. Woo, S. (2011). Super Disclosure: The Flawed Credit Rating Regulatory Regime. New York University Law and Economics Working Papers, 1, 1-35. Yıldırım, O. (2008). TürkBankacılıkSektöründeKarlılığınBelirleyicileri, (BasılmamışDoktoraTezi, Ankara ÜniversitesiSosyalBilimlerEnstitüsü). Yüksel, S., Dinçer, H. and Hacıoğlu, Ü. (2015).CAMELS-based Determinants for the Credit Rating for Turkish Deposit Banks.International Journal of Finance and Banking Studies, 4, 1-17. Zadeh, L. (1997). Toward a Theory of Fuzzy Information Granulation and Its Centrality in Human Reasoning and Fuzzy Logic.Fuzzy Sets and System, 90, 111-127. 55
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