Dokuz Eylül University
Şebnem Yılmaz BALAMAN
Dokuz Eylül University

Published 2017-04-21

How to Cite

SELİM, H., & Yılmaz BALAMAN, Şebnem. (2017). PERFORMANCE EVALUATION AND DISTRESS PREDICTION FOR EFFECTIVE RISK MANAGEMENT IN FINANCE SECTOR: AN INTEGRATED DECISION MAKING PROCEDURE. Business & Management Studies: An International Journal, 5(1), 58–94. https://doi.org/10.15295/bmij.v5i1.99


Considering its important role in the socio-economic status of the developing countries, finance sector, which is one of the core components of the service sector, is the focus of this study. The main drivers of this study are, to explore the most significant factors influencing the performance of the financial institutions in a risky environment, to evaluate the economic and financial performances using the selected factors and predict the future distress/bankruptcy possibility of the institutions by a comparative analysis employing a quantitative three-step decision making procedure. To explore the viability of the proposed approach, an up-to-date and comprehensive application on commercial banks operating in Turkish Banking sector is presented by using a wide range of financial ratios. To this aim, 44 commercial banks operating in Turkish financial sector are assessed as healthy and non-healthy by using 57 selected fundamental financial ratios to provide a comprehensive insight to the bank managers, investors, government units and rating agencies to predict the financial performances of banks and make related decisions when a risky socio-economic environment is a matter of a country.


Download data is not yet available.


  1. Abu-Alkheil, A.M., Burghof, H-P., & Khan, W.A. (2013). Comparative performance of Islamic and conventional banks in Europe. American Journal of Finance and Accounting, 3(1), 1 – 23.
  2. Alam, P., Booth, D., Lee, K., & Thordarson T. (2000). The use of fuzzy clustering algorithm and self-organizing neural networks for identifying potentially failing banks: an experimental study. Expert Systems with Applications, 18(3), 185–199.
  3. Alper, D. & Anbar, A. (2011). Bank Specific and Macroeconomic Determinants of Commercial Bank Profitability: Empirical Evidence from Turkey. Business and Economics Research Journal, 2(2), 139-152.
  4. Altman, E. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23, 589-609.
  5. Baidya, M.K., & Mitra, D. (2012). An analysis of the technical efficiency of Indian public sector banks through DEA approach. International Journal of Business Performance Management, 13 (3/4), 341 – 365.
  6. Banker, R.D., Charnes, A., & Cooper, W.W. (1984). Models for the Estimation of Technical and Scale Inefficiencies in Data Envelopment Analysis. Management Science, 30, 1078-1092.
  7. 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.
  8. Betz, F., Oprica, S., Peltonen, T.A., & Sarlin, P. (2014). Predicting distress in European banks. Journal of Banking & Finance, 45, 225–241.
  9. Boyacioglu, M.A., Kara, Y., & Baykan, Ö.K. (2009). Predicting bank financial failures using neural networks, support vector machines and multivariate statistical methods: A comparative analysis in the sample of savings deposit insurance fund (SDIF) transferred banks in Turkey. Expert Systems with Applications, 36, 3355-3366.
  10. Canbaş, S., & Erol, C. (1985). Türkiye’de Ticaret Bankaları Sorunlarının Saptanması: Erken Uyarı Sistemine Giriş. Türkiye Ekonomisi ve Türk Ekonomi İlmi, 1, Marmara Üniversitesi Türkiye Ekonomi Araştırma Merkezi.
  11. Canbas, S., Cabuk, A., & Kilic, S.B. (2005). Prediction of commercial bank failure via multivariate statistical analysis of financial structures: The Turkish case. European Journal of Operational Research, 166, 528-546.
  12. Chen, T. (2005). A measurement of Taiwan's bank efficiency and productivity change during the Asian financial crisis. International Journal of Services Technology and Management, 6(6), 525 – 543.
  13. Celik, A.E., & Karatepe, Y. (2007). Evaluating and forecasting banking crises through neural network models: An application for Turkish banking sector. Expert Systems with Applications, 33, 809–815.
  14. Charnes, A., Cooper, W.W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2, 429-444.
  15. Cox, R.A.K., & Wang, G.W.-Y. (2014). Predicting the US bank failure: A discriminant analysis. Economic Analysis and Policy, http://dx.doi.org/10.1016/j.eap.2014.06.002.
  16. Demirguc-Kunt A, & Detragiache E. (1998). The determinants of banking crises in developing and developed countries. IMF Staff Papers, 45(1).
  17. Demyanyk, Y., & Hasan I. (2010). Financial crises and bank failures: A review of prediction methods. Omega, 38, 315-324.
  18. Evans, J., & Borders, A.L. (2014). Strategically Surviving Bankruptcy during a Global Financial Crisis: The Importance of Understanding Chapter 15. Journal of Business Research, 67, 2738–2742.
  19. 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, 189-198.
  20. Grice, J.S., & Ingram, R.W. (2001). Tests of the generalizability of Altman’s bankruptcy prediction model. Journal of Business Research, 54, 53 – 61.
  21. Huang, C., Dai, C., & Guo, M. (2015). A hybrid approach using two-level DEA for financial failure prediction and integrated SE-DEA and GCA for indicators selection. Applied Mathematics and Computation, 251, 431–441.
  22. Iturriaga, F.J.L., & Sanz, I.P. (2015). Bankruptcy visualization and prediction using neural networks: A study of U.S. commercial banks. Expert Systems with Applications, 42(6), 857–2869.
  23. Karaca, S.S. & Çiğdem, R. (2013). Türkiye Ekonomisi’nde Yaşanan 1994- 2001 Ekonomik Krizlerinin ve 2008 Küresel Ekonomik Krizinin İmalat Sanayi Sektörüne Etkilerinin Finansal Oranlar ile İncelenmesi. Business and Economics Research Journal, 4(3), 41-54.
  24. Kolari, J., Glennon, D., Shin, H., & Caputo, M. (2002). Predicting large US commercial bank failures. Journal of Economics and Business, 54, 361–387.
  25. Kumar, P.R., & Ravi, V. (2007). Bankruptcy prediction in banks and firms via statistical and intelligent techniques – A review. European Journal of Operational Research, 180, 1-28.
  26. Lanine, G., & Vennet R.V. (2006). Failure prediction in the Russian bank sector with logit and trait recognition models. Expert Systems with Applications, 30, 463–478.
  27. Lin, F., Liang, D., & Chen, E. (2011). Financial ratio selection for business crisis prediction. Expert Systems with Applications, 38, 15094-15102.
  28. Liu, J., & Tone, K. (2008). A multistage method to measure efficiency and its application to Japanese banking industry. Socio-Economic Planning Sciences, 42, 75–91.
  29. Looney, S.W., Wansley, J.W., & Lane, W.R. (1989). An examination of misclassifications with bank failure prediction models. Journal of Economics and Business, 41(4), 327–336.
  30. Mercan, M., Reisman,A., Yolalan, & R., Emeld, A.B. (2003). The effect of scale and mode of ownershipon the financial performance of the Turkish banking sector: results of a DEA-based analysis. Socio-Economic Planning Sciences, 37, 185–202.
  31. Öğüt, H., Doğanay M.M., Ceyla, N.B., & Aktaş, R. (2012). Prediction of bank financial strength ratings: The case of Turkey. Economic Modelling, 29, 632-640.
  32. Paradi, J.C., Zhu, H. (2013), A survey on bank branch efficiency and performance research with data envelopment analysis. Omega, 41, 61–79.
  33. Ravi, V., Kurniawan, H., Thai, P.N.K., & Kumar, P.R. (2008). Soft computing system for bank performance prediction. Applied Soft Computing, 8(1), 305–315.
  34. Ravisankar, P., & Ravi, V. (2010). Financial distress prediction in banks using Group Method of Data Handling neural network, counter propagation neural network and fuzzy ARTMAP. Knowledge-Based Systems, 23, 823-831.
  35. Rebai, S., Azaiez, M.N., & Saidane, D. (2012). Sustainable Performance Evaluation of Banks using a Multi-attribute Utility Model: An Application to French Banks. Procedia Economics and Finance, 2, 363–372.
  36. Sun, J., Li, H., Huang, Q., & He, K. (2014). Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches. Knowledge-Based Systems, 57, 41-56.
  37. Tam, K.Y. (1991. Neural network models and the prediction of bank bankruptcy. Omega, 19, 429–445.
  38. Tinoco, M.H., & Wilson, N. (2013). Financial distress and bankruptcy prediction among listed companies using accounting, market and macroeconomic variables. International Review of Financial Analysis, 30, 394–419.
  39. Yayar, R., & Baykara, H.V. (2012). TOPSIS Yöntemi ile Katılım Bankalarının Etkinliği ve Verimliliği Üzerine Bir Uygulama. Business and Economics Research Journal, 3(4), 21-42.
  40. Xu, X., & Wang, Y. (2009). Financial failure prediction using efficiency as a predictor. Expert Systems with Applications, 36, 366–373.