Predicting Banks' Financial Distress by Data Envelopment Analysis Model and CAMELS Indicators
Subject Areas : Business StrategyAbass Paidar 1 , Morteza Shafiee 2 , Fariborz Avazzadeh 3 , Hashem Valipour 4
1 - Department of Accounting and Management, Yasuj Branch, Islamic Azad University, Yasuj, Iran
2 - Department of Management, Shiraz Branch, Islamic Azad University, Shiraz, Iran
3 - Department of Accounting, Gachsaran Branch, Islamic Azad University, Gachsaran, Iran
4 - Department of Accounting, Firuzabad Branch, Islamic Azad,University, Firuzabad, Iran
Keywords: Data envelopment analysis, Banking Supervision, Financial Distress Prediction, CAMELS Indices, Banking Health and Stability,
Abstract :
Due to their inherent nature in the economy, banks have a fundamental and significant responsibility for capital formation. Therefore, evaluating their performance can help decision makers find the optimal solution and prevent financial distress. The purpose of this study is to evaluate the performance and forecast financial distress of banks listed on the Tehran Stock Exchange, based on CAMELS indicators and ِData Envelopment Analysis model. First, using the data of 17 banks in the fiscal year 2018, 5 levels of determining the health of banks, in the form of differences between the performance of these banks in terms of capital adequacy, quality of assets, quality of management, Earning and liquidity and sensitivity to market risk, It was found. And the studied banks were divided into two groups: healthy and helpless, based on CAMELS indices. Then, according to the effects of financial distress on banks, financial distress was predicted by Data Envelopment Analysis model slacks-based on measure of efficiency (SBM) and with a different approach. The results show that 61% of the predictions were correct by DEA technique and 39% of them were incorrect. Also, the results of this study showed that CAMELS financial ratios can be a good assessor for banks' financial distress.
Abdollahi Poor, M., Botshekan, M., Sargolzaei, M. 2020. Assessment of the Effect of CAMELS Indicators on Risk-Adjusted Return on Capital (RAROC) in the Banks listed in Iran’s Stock Market. ـJournal of Financial Management Perspective, 10(32): 57-80.
Ahmadyan, A. 2017. The Importance of Composition of Assets and Liabilities in Determining Banking Supervision Ratings. Economics Research, 17(65), 115-142.
Ahmadyan, A. 2018.Design of a Rating System in Banking Supervision in the Banking Network: CAMELS Approach. qjerp. 2018; 26 (85): 7-31
Ahmadyan, A., Gorji, M. 2017. Explaining the Model of Bankruptcy Prediction to Identify Healthy and Risky, Journal of Asset Management and Financing, 5 (3): 18-1.
Ahsan MK. 2016. Measuring Financial Performance Based on Camel: Astudy on Selected Islamic Banks in Bangladesh, Asian Business Review, 6(1/206), Issue 13.
Alfiyanti, M.H, Damayanti, C.R, Nurlaily, F. 2020. Analysis of Financial Distress Using the Altman Z-Score and Springate S-Score Methods (Study on Issuers of the Consumer Goods Industry Sector, Food & Beverages Sub-Sector Listed on the Indonesia Stock Exchange in 2014 -2018). Journal of Business Administration, 78(1): 76-85.
Atefifar, A., fathi, Z. 2020. Effect of Financial Health Indicators as Symbols of Bank Financial Crisis Using Logit Model Multivariate (A Case Study of Banks Accepted in Exchange). , 11(42): 333-361.
Azar, A., Safari, S. 2004. Modeling organizational excellence with data envelopment analysis approach. Teacher of Humanities, 8 (2): 1-34.
Banker, R.D., Charnes, A., Cooper WW. 1984. Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9): 1078-1092.
Chairunesia ,W., Bintara, R. 2019.The Effect of Good Corporate Governance and Financial Distress on Earnings Management in Indonesian and Malaysia Companies Entered in Asean Corporate Governance Scorecard. International Journal of Academic Research in Accounting, Finance and Management Sciences, 9(2): 224-236.
Charnes, A., Cooper W.W., Rhodes, E. 1978. Measuring the efficiency of decision making units. European Journal of the Operational Research, 2: 429–44.
Cielen, A, peeters, l., vanhoof, k. 2004. Bankruptcy prediction using data envelopment analysis. European Journal of Operational Research, 154: 526-532.
Condello, S., Del Pozzo A., Loprevite, S. 2017. Potential and Limitations of DEA as a Bankruptcy Prediction Tool in the Light of a Study on Italian Listed Companies, Applied Mathematical Sciences, 11(44): 2185 – 2207.
Cooper W., Seiford L., Ton K. 2017. Data covering analysis; Models and applications. Translated by Mir Hosseini, Tehran: Amirkabir University of Technology Publications.
Farel MJ. 1957. The measuerment of productine efficiency.journal of statistical, 3:181-253.
Hasas Yehaneh, Y., Habibi, R., Nazi, B. 2018. Impact of Asset Quality on Financial Distress in Banks. Quarterly Journal of Islamic Finance and Banking Studies, 3(6, 7):25-58.
Hosseini, S., Rashidi, Z. 2013. Bankruptcy Prediction of Companies listed Corporations in Tehran Stock Exchange by Using Decision Tree and Logistic Regression. Journal of Financial Accounting Research, 5(3):105-128.
Idrees S, Qayyum A. 2018.The impact of financial distress risk on equity returns: A case study of non-financial firms of Pakistan Stock Exchange. Journal of Economics Bibliography, 5(2): 49-59.
Islami Z., Bahramiznuz M., Rajabi M., Mihani, M. 2011, the need to develop a rating model for banks and present a proposed model, research plan, research department and risk control of Sepah Bank.
Jahanshahloo, G., Hosseinzadeh Lotfi, F., Nikomram, H. 2015. Data envelopment analysis and its applications. Second Edition Tehran: Islamic Azad University Publications Science and Research Branch.
Kamrul AM. 2016. Measuring Financial Performance Based on CAMEL: A Study on Selected Islamic Banking in Bangladesh, Asian Business Review. Mar, 47-57.
khanifar, H., Bazaz, Z., Tehrani, R., Mohaghegh Niya, M. 2015. Survey and Comparison of Performance of Govermental and Private Banks: An Application of CAMEL Model. Organizational Culture Management, 13(2): 437-461.
Madishetti S. 2013. Ownership Structure and Financial Performance Of Commercial Banks: A Comparative Study Of Twomajor Banks in Tanzania,Galaxy International Interdisciplinary Research Journal,1(2): 2347-691.
Masood O, Khan G, Shahid M, Aktan B. 2016. Predicting Islamic Banks performance Through CAMELS Rating Model, Banks and bank systems. 12-24.
Mehrani, S., Mehrani, K., Karami, G.H., Yashar, M. 2005. Investigating the Application of Zimski and Shirata Bankruptcy Prediction Patterns in Companies Listed in Tehran Stock Exchange, Accounting and Auditing Review, 105-131.
Mehregan M. 2019. Data Envelopment Analysis (Quantitative Models in Evaluating Organizational Performance), University Book Publishing: Tehran, Fourth Edition.
Muhmada. S.N., Hashima. H. 2015. Using the camel framework in asessing bank performance in Malaysia, International Journal of Economics,Management and Accounting , 23(1): 109-127.
Nemati, M., Tabatabaee, S.A. 2016. Determining the Factors Affecting Cost Inefficiency in Banks (Case Study: Banks Listed in Tehran Stock Exchange). Financial Economics, 10 (36): 123-146.
Oztorul, G. 2011. Performance evaluation of banks and banking groupsTurkey case, M.S., Middle East technical university in Ankara, 4(5): 1-18.
Podviezko, A., Ginevicius, R. (2010), Economic Criteria CHaracterising Bank Soundness and Stability. The 6th International Scientific Conference BUSINESS AND MANAGEMENT, Selected papers. Vilnius: Technika, pp. 1072-1079.
Prasad KVN, Ravinder G. 2012. A Camel Model Analysis of Nationalized Banks in India, International Journal of Trade and Commerce-IIARTC, 1(5): 50-72.
Ramezani, S.M., khorashadizadeh, M.A., Mohamadi yosho, E. 2017, Model to Evaluate and Predict the Financial Soundness of selected Banks in Iran: Using CAMELS Rating System. Qjerp, 25 (82):43-78.
Rehana K, Saba I. 2012. Gauging the Financial Performance of Banking Sector using CAMEL Model: Comparison of Conventional, Mixed and Pure Islamic Banks in Pakistan, International Research Journal of Finance and Economics, 82: 67-88.
Roman, A., Sargu A.C. 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.
Salhuteru, F., Wattimena, F.2015. Bank Performance with CAMELS Ratios towards earnings Management practices In State Banks and Private Banks, Advances in Social Sciences Research Journal, 2(3): 301-314.
Shafiee, M. 2017. Designing a Multi-level Data Envelopment Analysis Model to Evaluate the Efficiency of Financial Organizations, Journal of Operational Research in Its Applications (Applied Mathematics) - Lahijan Azad University, 14(2):41-66.
Soleimani, B., Nemati, M., Almasi, H. 2020. Evaluating the performance of private banks in Tehran Stock Exchange based on CAMEL model. Financial Economics, 14 (50):115-144.
Soudani, A. 2017. Ranking of Iranian Banks Based on the CAMELS International Indicators, Journal OF Monetary and Banking Research, 10(31): 141-171.
Taheri, M., rahmani, A., soleimani, G. 2019. Value relevance of risk disclosure in Iranian listed banks. Journal of Financial Accounting Research, 11(1):1-22.
Tone, K. 2001. A slack based measure of efficiency in data envelopment analysis. European Journal of Operational Research, 130: 498-509.
Trivedi AR, Elahi YA. 2015. A comparative analysis of performance of public & private sector banks in India through camel rating system, International journal of applied financial management perspectives, 4(3): 1724-1736.
Yakideh, K., Mahfozi, G., Saeidei Lohesara, F. 2018. Rating Iran's Banks based on CAMEL Indices Using RAM Model. Journal of Services Operation Management, 1(1):77-93.