Determining the Priorities of CAMELS Dimensions Based on Bank Performance

  • Mehmet Pekkaya
  • Figen Erol Demir
Part of the Contributions to Economics book series (CE)


Banks’ performances are important not only for the stability/growth of the firms and economic situation of a country; it is also important for the stability/growth of the world economy. The aim of this study is to determine the priorities of CAMELS dimensions with respect to bank performance via AHP method and to present the results as an information to researchers, investors and decision-makers. Furthermore, this study shows the feasibility of many statistical hypothesis tests by separately generated priority series from expert’s views based on performance and bankruptcy risk of banks.

CAMELS, used for bank performance appraisal, is a financial ratio analysis comparing the ratios of banks with the industries. Along with evaluating the determined priorities of CAMELS dimensions based on performance of banks, the differences of the views between the priorities based on risk of bankruptcy and performance of banks, the view differences according to the demographic characteristics of the experts, etc., are also examined. According to analysis, “Asset” (24.75%) is the most important dimension of CAMELS, and then “Earnings” (19.16%), “Liquidity” (18.54%) and “Management” (17.68%) are thought as following important dimensions with respect to bank performance. Dimensions of “Sensitivity to market risk” (11.11%) and “Capital” (10.03%) are observed as weak dimensions.


MCDM AHP Hypothesis testing CAMELS Bank performance 


  1. Ahsan, M. K. (2016). Measuring financial performance based on CAMEL: A study on selected Islamic Banks in Bangladesh. Asian Business Review, 6(13), 47–56.CrossRefGoogle Scholar
  2. Akçakanat, Ö., Eren, H., Aksoy, E., & Ömürbek, V. (2017). Performance evaluation by entropy and WASPAS methods at banking sector. SDU the Journal of Faculty of Economics and Administrative Sciences, 22(2), 285–300.Google Scholar
  3. Akkoç, S., & 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.Google Scholar
  4. Aspal, P. K., & Dhawan, S. (2014). Financial performance assessment of banking sector in India: A case study of old private sector banks. The Business & Management Review, 5(3), 196–211.Google Scholar
  5. Atyeh, M. H., Yasin, J., & Khatib, A. M. (2015). Measuring the performance of the Kuwaiti banking sector before and after the recent financial crisis. Business & Financial Affairs, 4(3), 1–3.Google Scholar
  6. Barr, R. S., Killgo, K. A., Siems, T. F., & Zimmel, S. (2002). Evaluating the productive efficiency and performance of US commercial banks. Managerial Finance, 28(8), 3–25.CrossRefGoogle Scholar
  7. Bhatia, A., & Mahendru, M. (2015). Assessment of technical efficiency of public sector banks in India using data envelopment analysis. Eurasian Journal of Business and Economics, 8(15), 115–140.CrossRefGoogle Scholar
  8. Chatzi, I. G., Diakomihalis, M. N., & Chytis, E. Τ. (2015). Performance of the Greek banking sector pre and throughout the financial crisis. Journal of Risk and Control, 2(1), 45–69.Google Scholar
  9. Chauhan, K. A., Shah, N. C., & Rao, R. V. (2008). The analytic hierarchy process as a decision-support system in the housing sector: A case study. World Applied Sciences Journal, 3(4), 609–613.Google Scholar
  10. Dash, M. (2017). A model for bank performance measurement integrating multivariate factor structure with multi-criteria PROMETHEE methodology. Asian Journal of Finance & Accounting, 9(1), 310–332.CrossRefGoogle Scholar
  11. Dodd, F. J., Donegan, H. A., & McMaster, T. B. M. (1993). A statistical approach to consistency in AHP. Mathematical and Computer Modelling, 18(6), 19–22.CrossRefGoogle Scholar
  12. Doumpos, M., & Zopounidis, C. (2010). A multicriteria decision support system for bank rating. Decision Support Systems, 50, 55–63.CrossRefGoogle Scholar
  13. Ecer, F. (2013). Türkiye’deki Özel Bankaların Finansal Performanslarının Karşılaştırılması: 2008–2011 Dönemi. AIBU Sosyal Bilimler Enstitüsü Dergisi, 13(2), 171–189.Google Scholar
  14. Ghasempour, S., & 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.Google Scholar
  15. Ginevičius, R., & Podviezko, A. (2013). The evaluation of financial stability and soundness of Lithuanian banks. Ekonomska Istraživanja-Economic Research, 26(2), 191–208.CrossRefGoogle Scholar
  16. Gökalp, F. (2015). Comparing the financial performance of banks in Turkey by using Promethee method. Ege Strategic Research Journal, 6(1), 63–82.Google Scholar
  17. Güneysu, Y., Er, B., & Ar, İ. M. (2015). Türkiye’deki Ticari Bankalarin Performanslarinin AHS ve GIA Yöntemleri ile Incelenmesi. KTU SBE Sosyal Bilimler Dergisi, 9, 71–93.Google Scholar
  18. Hamzaçebi, C., & Pekkaya, M. (2011). Determining of stock investments with grey relational analysis. Expert Systems with Applications, 38(8), 9186–9195.CrossRefGoogle Scholar
  19. Ishaq, A. B., Karim, A., Ahmed, S., & Zaheer, A. (2016). Evaluating performance of commercial banks in Pakistan: “An application of Camel model”. Journal of Business & Financial Affairs, 5(1), 1–30.Google Scholar
  20. Jha, S., Hui, X., & Sun, B. (2013). Commercial banking efficiency in Nepal: Application of DEA and Tobit model. Information Technology Journal, 12(2), 306–314.CrossRefGoogle Scholar
  21. Mohiuddin, G. (2014). Use of CAMEL model: A study on financial performance of selected commercial banks in Bangladesh. Universal Journal of Accounting and Finance, 2(5), 151–160.Google Scholar
  22. Nouaili, M., Abaoub, E., & Ochi, A. (2015). The determinants of banking performance in front of financial changes: Case of trade banks in Tunisia. International Journal of Economics and Financial Issues, 5(2), 410–417.Google Scholar
  23. Ogunyemi, O., Ibiwoye, A., & Oyatoye, E. O. (2011). Analytic hierarchy process for prioritizing production functions: Illustration with pharmaceutical data. Journal of Economics and International Finance, 3(14), 749–760.Google Scholar
  24. Panja, S. (2017). Multivariate bank performance analysis using standardized CAMEL methodology and fuzzy analytical hierarchical process. Indian Journal of Science and Technology, 10(23), 1–17.CrossRefGoogle Scholar
  25. Pekkaya, M., & Akıllı, F. (2013). Statistical analysis and evaluation of airline service quality by SERVPERF-SERVQUAL scale. The International Journal of Economic and Social Research, 9(1), 75–96.Google Scholar
  26. Pekkaya, M., & Aktogan, M. (2014). Dizüstü bilgisayar seçimi: DEA, VIKOR ve TOPSIS ile Karşılaştırmalı bir Analiz. Ekonomik ve Sosyal Araştırmalar Dergisi, 10(1), 107–125.Google Scholar
  27. Pekkaya, M., & Başaran, S. (2011). Konaklama İşletmeleri Hizmet Kalitesi Boyutları Önem Derecelerinin AHP ile Belirlenmesi ve İşletmelerin Hizmet Kalitesine göre TOPSIS ile Sıralanması. Mali Ufuklar Dergisi, 5, 111–136.Google Scholar
  28. Pekkaya, M., & Çolak, N. (2013). Determining the priorities of ratings via AHP for the factors that effects in choosing professions for the University students. The Journal of Academic Social Science Studies, 6(2), 797–818.Google Scholar
  29. Pekkaya, M., & Demir, F. E. (2016). Determining the priorities of criteria in assessing the bankruptcy risk of the banks via AHP. International Journal of Management Economics and Business, ICAFR 16 Special Issue, 40–45.Google Scholar
  30. Pekkaya, M., & Zilifli, V. (2016). Determining the priorities of the criteria which the banks take in consideration in the assessment process of commercial credit. International Journal of Management Economics and Business, ICAFR 16 Special Issue, 201–210.Google Scholar
  31. Rezaei, M., & 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.CrossRefGoogle Scholar
  32. Rostami, M. (2015). Determination of Camels model on bank’s performance. International Journal of Multidisciplinary Research and Development, 2(10), 652–664.Google Scholar
  33. Saaty, T. L. (1980). The analytic hierarchy process. New York: McGraw-Hill.Google Scholar
  34. Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International Journal of Services Sciences, 1(1), 83–98.CrossRefGoogle Scholar
  35. Socol, A., & Dănuleţiu, A. E. (2013). Analysis of the Romanian banks’ performance through ROA, ROE and non-performing loans models. Annales Universitatis Apulensis Series Oeconomica, 15(2), 594–604.Google Scholar
  36. Tata, H. K., & Nimmagadda, V. S. (2016). Performance evaluation of banks through four phased DEA – A case study. International Journal of Industrial Engineering Research and Development, 7(1), 24–34.Google Scholar
  37. Toplu, H. Y. (2017). Effective ratios on financial performance with CAMELS approach: An application of panel regression on commercial banks in Turkey. Unpublished PhD Thesis, BEU Institute of Social Sciences, Zonguldak.Google Scholar
  38. Wanke, P., Kabir Hassan, M., & Gavião, L. O. (2017). Islamic banking and performance in the Asean banking industry: A TOPSIS approach with probabilistic weights. International Journal of Business and Society, 18(S1), 129–150.Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Mehmet Pekkaya
    • 1
  • Figen Erol Demir
    • 2
  1. 1.Business AdministrationBülent Ecevit UniversityZonguldakTurkey
  2. 2.Bülent Ecevit UniversityZonguldakTurkey

Personalised recommendations