Bank Efficiency Forecasting Model Based on the Modern Banking Indicators Using a Hybrid Approach of Dynamic Stochastic DEA and Meta-Heuristic Algorithms

Document Type : Research Paper

Authors

1 Postdoctoral Research Student, Department of Industrial Management, Faculty of Social Sciences, Imam Khomeini International University (IKIU), Qazvin, Iran

2 Associate Professor, Department of Industrial Management, Faculty of Social Sciences, Imam Khomeini International University (IKIU), Qazvin, Iran

Abstract

Evaluating the efficiency of banks is crucial to orient their future decisions. In this regard, this paper proposes a new model based on dynamic stochastic data envelopment analysis in a fuzzy environment by considering the modern banking indicators to predict the efficiency of banks, which belongs to the category of NP-hard problems. To deal with the uncertainty in efficiency forecasting, the mean chance theory was used to express the constraints of the model and the expected value in its objective function to forecast the expected efficiency of banks. To solve the proposed model, two hybrid algorithms were designed by combining Monte Carlo (MC) simulation technique with Genetic Algorithm (GA) and Imperialist Competitive Algorithm (ICA). In order to improve the performances of MC-GA and MC-ICA parameters, the Response Surface Methodology (RSM) was applied to set their proper values. In addition, a case study in the modern banking industry was presented to evaluate the performance of the proposed model and the effectiveness of the hybrid algorithms. The results showed that the proposed model had high accuracy in predicting efficiency. Finally, to validate the designed hybrid algorithms, their results were compared with each other in terms of accuracy and convergence speed to the solution.

Keywords

Main Subjects


Article Title [فارسی]

مدل پیش بینی کارایی بانک براساس شاخص های بانکداری نوین با استفاده از رویکرد هیبریدی تحلیل پوششی داده های تصادفی پویا و الگوریتم های فراابتکاری

Authors [فارسی]

  • علی یعقوبی 1
  • صفر فضلی 2
1 دانشجوی پسادکتری، دپارتمان مدریت صنعتی، دانشکده علوم اجتماعی، دانشگاه بین المللی امام خمینی، قزوین، ایران
2 دانشیار دپارتمان مدیریت صنعتی، دانشکده علوم اجتماعی، دانشگاه بین المللی امام خمینی، قزوین، ایران
Abstract [فارسی]

ارزیابی کارایی شعب بانک ها برای جهت گیری تصمیمات آینده آنها بسیار مهم است. در این راستا، این مقاله مدل جدیدی جهت پیش بینی کارایی شعب بانک ها براساس تحلیل پوششی داده های تصادفی پویا در یک محیط فازی با لحاظ شاخص های بانکداری نوین ارائه می نماید که در گروه مسائل NP-hard قرار دارد. به جهت مواجهه با عدم قطعیت در پیش بینی کارایی، از تئوری متوسط شانس برای بیان محدودیت ها و از امیدریاضی در تابع هدف مدل برای پیش بینی کارایی مورد انتظار بانک ها استفاده شده است. برای حل مدل، دو الگوریتم هیبریدی از ترکیب شبیه سازی مونت کارلو (MC) با الگوریتم های ژنتیک (GA) و رقابت استعماری (ICA) طراحی می گردد. بمنظور بهبود عملکرد الگوریتم های هیبریدی ارائه شده، از تکنیک متدولوژی سطح پاسخ (RSM) جهت تعیین مقادیر پارامترهای آنها استفاده شده است. همچنین یک مطالعه موردی در صنعت بانکداری مدرن بمنظور ارزیابی عملکرد مدل ارائه شده و اثربخشی الگوریتم های هیبریدی ارائه می شود. نتایج نشان داد که مدل ارائه شده دقت بالایی در پیش بینی کارایی دارد. نهایتاً بمنظور اعتبارسنجی الگوریتم های هیبریدی طراحی شده، نتایج آن ها از ابعاد دقت و سرعت همگرایی به جواب مقایسه شده اند.

Keywords [فارسی]

  • تحلیل پوششی داده های تصادفی پویا
  • برنامه ریزی فازی
  • الگوریتم فراابتکاری هیبریدی
  • بانکداری مدرن
  • شبیه سازی مونت کارلو
Amirteimoori, A., Azizi, H., & Kordrostami, S. (2020). Double frontier two-stage fuzzy data envelopment analysis. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 28(01), 117-152. 
Arteaga, F. J. S., Tavana, M., Di Caprio, D., & Toloo, M. (2019). A dynamic multi-stage slacks-based measure data envelopment analysis model with knowledge accumulation and technological evolution. European Journal of Operational Research, 278(2), 448-462.
Atashpaz-Gargari, E., & Lucas, C. (2007, September). Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In 2007 IEEE congress on evolutionary computation (pp. 4661-4667). Singapore: Ieee. 
Avkiran, N. K., & Morita, H. (2020). Predicting Japanese bank stock performance with a composite relative efficiency metric: A new investment tool. Pacific-Basin Finance Journal, 18(3), 254-271.
Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9), 1078-1092.
Charnes, A., & Cooper, W. W. (1959). Chance-constrained programming. Management science, 6(1), 73-79. 
Cole, R. A., & Gunther, J. W. (1995). Separating the likelihood and timing of bank failure. Journal of Banking & Finance, 19(6), 1073-1089.
Cooper, W. W., Huang, Z., Lelas, V., Li, S. X., & Olesen, O. B. (1998). Chance constrained programming formulations for stochastic characterizations of efficiency and dominance in DEA. Journal of Productivity Analysis, 9(1), 53-79.
Cvilikas, A., & Jurkonyte-Dumbliauskiene, E. (2016). Assessment of risk management economic efficiency applying economic logistic theory. Transformations in Business & Economics, 15(3), 207-219. 
Dai, X., Liu, Y., & Qin, R. (2010, June). Modeling fuzzy data envelopment analysis with expectation criterion. In International Conference in Swarm Intelligence (pp. 9-16). Berlin, Heidelberg: Springer. 
Foroughi, A. A., & Shureshjani, R. A. (2017). Solving generalized fuzzy data envelopment analysis model: A parametric approach. Central European Journal of Operations Research, 25(4), 889-905.
Gaganis, C., Galariotis, E., Pasiouras, F., & Staikouras, C. (2020). Bank profit efficiency and financial consumer protection policies. Journal of Business Research, 118, 98-116.
Ghosh, I., & Rakshit, D. (2017). Performance evaluation of public sector and private sector banks in india by using CAMEL model–A comparative study. Research Bulletin, 43(2), 110-122.
Goldberg, D. E. (1989). Genetic algorithms in search. Optimization, and Machine Learning (1st. ed.). New York: Addison-Wesley. 
Hatami-Marbini, A. (2019). Benchmarking with network DEA in a fuzzy environment. RAIRO-Operations Research, 53(2), 687-703.
Hatami-Marbini, A., Ebrahimnejad, A., & Lozano, S. (2017). Fuzzy efficiency measures in data envelopment analysis using lexicographic multiobjective approach. Computers & Industrial Engineering, 105, 362-376.
Holland, J. H. (1975). Adaptation in natural and artificial systems. University of Michigan Press.
Hu, C. K., Liu, F. B., & Hu, C. F. (2017). Efficiency measures in fuzzy data envelopment analysis with common weights. Journal of Industrial & Management Optimization, 13(1), 237-249. 
Jafarian-Moghaddam, A. R., & Ghoseiri, K. (2011). Fuzzy dynamic multi-objective data envelopment analysis model. Expert Systems With Applications, 38(1), 850-855.
Kaymaz, I., & McMahon, C. A. (2005). A response surface method based on weighted regression for structural reliability analysis. Probabilistic Engineering Mechanics, 20(1), 11-17.
Keffala, M. R. (2020). How using derivative instruments and purposes affects performance of Islamic banks? Evidence from CAMELS approach. Global Finance Journal, 100520.
Kenneth, U. O., & Adeniyi, A. M. (2014). Prediction of bank failure using camel and market information: comparative Appraisal of some selected banks in Nigeria. Res J Finance Account, 5(3), 1-17.
Kuah, C. T., Wong, K. Y., & Wong, W. P. (2012). Monte Carlo data envelopment analysis with genetic algorithm for knowledge management performance measurement. Expert Systems with Applications, 39(10), 9348-9358.
Namakin, A., Najafi, S. E., Fallah, M., & Javadi, M. (2018). A New evaluation for solving the fully fuzzy data envelopment analysis with z-numbers. Symmetry, 10(9), 384-397. 
Papi, S., Khorramabadi, M., & Lashgarara, S. (2018). Estimating productivity of the provinces of Iran in the health sector using fuzzy data in Imprecise Data Envelopment Analysis (IDEA). Journal of Health Administration, 21(73), 35-48.
Pekkaya, M., & Demir, F. E. (2018). Determining the priorities of CAMELS dimensions based on bank performance. In Dincer H., Hacioglu Ü., & Yüksel S. (Eds.). Global approaches in financial economics, banking, and finance (pp. 445-463): Springer.
Peykani, P., Mohammadi, E., Emrouznejad, A., Pishvaee, M. S., & Rostamy-Malkhalifeh, M. (2019). Fuzzy data envelopment analysis: An adjustable approach. Expert Systems with Applications, 136, 439-452.
Punyangarm, V., Yanpirat, P., Charnsethikul, P., & Lertworasirikul, S. (2006). A Credibility Approach for Fuzzy Stochastic Data Envelopment Analysis (FSDEA). Proceeding of  the 7th Asia Pacific Industrial  Engineering and Management Systems Conference, Bangkok, Thailand, 1720, 633–644. 
Qin, R., & Liu, Y. K. (2010). A new data envelopment analysis model with fuzzy random inputs and outputs. Journal of Applied Mathematics and Computing, 33(1-2), 327-356.
Rostami, M. (2015). Determination of Camels model on bank’s performance. International Journal of Multidisciplinary Research and Development, 2(10), 652-664.
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.
Sengupta, J. (1982). Decision models in stochastic programming: Operational methods of decision making under uncertainty (vol. 7). North-Holland.
Tajeddini, K. (2011). The effects of innovativeness on effectiveness and efficiency. Education, Business and Society: Contemporary Middle Eastern Issues, 4(1), 6-18.
Toloo, M., & Nalchigar, S. (2009). A new integrated DEA model for finding most BCC-efficient DMU. Applied Mathematical Modelling, 33(1), 597-604.
Vives, X. (2019). Competition and stability in modern banking: A post-crisis perspective. International Journal of Industrial Organization, 64, 55-69.
Wanke, P., Azad, M. A. K., Barros, C. P., & Hassan, M. K. (2016). 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., Barros, C. P., & Nwaogbe, O. R. (2016). Assessing productive efficiency in Nigerian airports using Fuzzy-DEA. Transport Policy, 49, 9-19.
Yaghoubi, A., & Amiri, M. (2015). Designing a new multi-objective fuzzy stochastic DEA model in a dynamic‎ environment to estimate efficiency of decision making units (Case study: An Iranian petroleum company). Journal of Industrial Engineering and Management Studies, 2(2), 26-42.
Yu, M. M., Lin, C. I., Chen, K. C., & Chen, L. H. (2019). Measuring Taiwanese bank performance: A two-system dynamic network data envelopment analysis approach. Omega, 102145.