Abstract
The efficiency of banks has a critical role in development of sound financial systems of countries. Data Envelopment Analysis (DEA) has witnessed an increase in popularity for modeling the performance efficiency of banks. Such efficiency depends on the appropriate selection of input and output variables. In literature, no agreement exists on the selection of relevant variables. The disagreement has been an on-going debate among academic experts, and no diagnostic tools exist to identify variable misspecifications. A cognitive analytics management framework is proposed using three processes to address misspecifications. The cognitive process conducts an extensive review to identify the most common set of variables. The analytics process integrates a random forest method; a simulation method with a DEA measurement feedback; and Shannon Entropy to select the best DEA model and its relevant variables. Finally, a management process discusses the managerial insights to manage performance and impacts. A sample of data is collected on 303 top-world banks for the periods 2013 to 2015 from 49 countries. The experimental simulation results identified the best DEA model along with its associated variables, and addressed the misclassification of the total deposits. The paper concludes with the limitations and future research directions.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Adler, N., & Golany, B. (2002). Including principal component weights to improve discrimination in data envelopment analysis. Journal of the Operational Research Society, 53(9), 985–991.
Andor, M. A., Parmeter, C., & Sommer, S. (2019). Combining uncertainty with uncertainty to get certainty? Efficiency analysis for regulation purposes. European Journal of Operational Research, 274(1), 240–252.
Anouze, A. L. M., & Bou-Hamad, I. (2019). Data envelopment analysis and data mining to efficiency estimation and evaluation. International Journal of Islamic and Middle Eastern Finance and Management.
Arjomandi, A., Harvie, C., & Valadkhani, A. (2012). Measuring the banking efficiency and productivity changes using the Hicks-Moorsteen approach: The case of Iran.
Arsad, R., Abdullah, M. N., Alias, S., & Isa, Z. (2017). Selection input output by restriction using DEA models based on a fuzzy Delphi approach and expert information. Journal of Physics: Conference Series, 892(1), 012010.
Athanassopoulos, A. D. (1997). Service quality and operating efficiency synergies for management control in the provision of financial services: Evidence from Greek bank branches. European Journal of Operational Research, 98(2), 300–313.
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.
Barros, C. P., Peypoch, N., & Williams, J. (2010). A note on productivity change in European cooperative banks: The Luenberger indicator approach. International Review of Applied Economics, 24(2), 137–147.
Belasri, S., Gomes, M., & Pijourlet, G. (2020). Corporate social responsibility and bank efficiency. Journal of Multinational Financial Management, 54, 100612. https://doi.org/10.1016/j.mulfin.2020.100612
Benítez-Peña, S., Bogetoft, P., & Morales, D. R. (2020a). Feature selection in data envelopment analysis: A mathematical optimization approach. Omega, 96, 102068.
Benítez-Peña, S., Bogetoft, P., & Morales, D. R. (2020b). Feature Selection in Data EnvelopmentAnalysis: A Mathematical Optimization approach. Omega. https://doi.org/10.1016/j.omega.2019.05.00
Benston, G. J. (1965). Branch banking and economies of scale. The Journal of Finance, 20(2), 312–331.
Benston, G. J., & Smith, C. W. (1976). A transactions cost approach to the theory of financial intermediation. The Journal of Finance, 31(2), 215–231.
Berger, A. N., & Humphrey, D. B. (1992). Measurement and efficiency issues in commercial banking. In Output measurement in the service sectors (pp. 245–300). University of Chicago Press.
Bian, Y., & Yang, F. (2010). Resource and environment efficiency analysis of provinces in China: A DEA approach based on Shannon’s entropy. Energy Policy, 38, 1909–1917.
Bou-Hamad, I., Anouze, A. L., & Larocque, D. (2017). An integrated approach of data envelopment analysis and boosted generalized linear mixed models for efficiency assessment. Annals of Operations Research, 253(1), 77–95.
Bowlin, W. F. (1998). Measuring performance: An introduction to data envelopment analysis (DEA). The Journal of Cost Analysis, 15(2), 3–27.
Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140.
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.
Breiman, L., & Cutler, A. (2016). Random Forests for Scientific Discovery. línea]. https://www.stat.berkeley.edu/~breiman/RandomForests/berkeleyfiles/frame.htm.
Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and regression trees–crc press. Florida.
Brown, M., & Gardener, E. P. (2004). A frontier analysis comparison of banking ‘added value.’ The Service Industries Journal, 24(4), 41–65.
Camanho, A. S., & Dyson, R. G. (2005). Cost efficiency, production and value-added models in the analysis of bank branch performance. Journal of the Operational Research Society, 56, 483–494.
Canhoto, A., & Dermine, J. (2003). A note on banking efficiency in Portugal, new vs. old banks. Journal of Banking & Finance, 27(11), 2087–2098.
Casu, B., & Girardone, C. (2005). An analysis of the relevance of off-balance sheet items in explaining productivity change in European banking. Applied Financial Economics, 15(15), 1053–1061.
Casu, B., & Molyneux, P. (2003). A comparative study of efficiency in European banking. Applied economics, 35(17), 1865–1876.
Chang, H.-C., Yang, F.-J., & Wang, Y.-H. (2015). Evaluating the efficiency of vietnamese commercial banks by using data envelopment analysis approach. Journal of Accounting, Finance & Management Strategy, 10(1), 147–170.
Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444.
Chen, M.-J., Chiu, Y.-H., Jan, C., Chen, Y.-C., & Liu, H.-H. (2015). Efficiency and risk in commercial banks–hybrid DEA estimation. Global Economic Review, 44(3), 335–352.
Chen, N.-K. (2001). Bank net worth, asset prices and economic activity. Journal of Monetary Economics, 48(2), 415–436.
Chen, T.-Y. (1998). A study of bank efficiency and ownership in Taiwan. Applied Economics Letters, 5(10), 613–616.
Chen, T.-Y. (2002). Measuring firm performance with DEA and prior information in Taiwan’s banks. Applied Economics Letters, 9(3), 201–204.
Chen, T.-Y. (2004). A study of cost efficiency and privatisation in Taiwan’s banks: The impact of the Asian financial crisis. The Service Industries Journal, 24(5), 137–151.
Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In B. Krishnapuram, M. Shah, A. J. Smola, C. C. Aggarwal, D. Shen, R. Rastogi (Eds.), Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, San Francisco, CA, USA, August 13–17, 2016. ACM. pp. 785–794. https://doi.org/10.1145/2939672.2939785.
Chen, T. Y., & Yeh, T. L. (1998). A study of efficiency evaluation in Taiwan’s banks. International Journal of Service Industry Management., 9(5), 402–415. https://doi.org/10.1108/09564239810238820
Chen, T.-Y., & Yeh, T.-L. (2000). A measurement of bank efficiency, ownership and productivity changes in Taiwan. Service Industries Journal, 20(1), 95–109.
Cook, W. D., Tone, K., & Zhu, J. (2014). Data envelopment analysis: Prior to choosing a model. Omega, 44, 1–4.
Das, A., & Ghosh, S. (2005). Size, non-performing loan, capital and productivity change: Evidence from Indian state-owned banks. Journal of Quantitative Economics, 3(2), 48–66.
Das, A., & Ghosh, S. (2006). Financial deregulation and efficiency: An empirical analysis of Indian banks during the post reform period. Review of Financial Economics, 15(3), 193–221.
Dekker, D., & Post, T. (2001). A quasi-concave DEA model with an application for bank branch performance evaluation. European Journal of Operational Research, 132(2), 296–311.
Deng, Q., Wong, W. P., Wooi, H. C., & Xiong, C. M. (2011). An engineering method to measure the bank productivity effect in Malaysia during 2001–2008. Systems Engineering Procedia, 2, 1–11.
Dias, W., & Helmers, G. A. (2001). Agricultural and nonagricultural bank productivity: A DEA approach. Agricultural Finance Review, 61(1), 1–18.
Emrouznejad, A., Amin, G. R., Thanassoulis, E., & Anouze, A. L. (2010a). On the boundedness of the SORM DEA models with negative data. European Journal of Operational Research, 206(1), 265–268.
Emrouznejad, A., Anouze, A. L., & Thanassoulis, E. (2010b). A semi-oriented radial measure for measuring the efficiency of decision making units with negative data, using DEA. European Journal of Operational Research, 200(1), 297–304.
Eskelinen, J. (2017). Comparison of variable selection techniques for data envelopment analysis in a retail bank. European Journal of Operational Research, 259(2), 778–788.
Fang, J., Lau, C.-K., Lu, Z., Tan, Y., & Zhang, H. (2019). Bank performance in China: A perspective from bank efficiency, risk-taking and market competition. Pacific-Basin Finance Journal, 56, 290–309.
Ferrier, G. D., & Lovell, C. K. (1990). Measuring cost efficiency in banking: Econometric and linear programming evidence. Journal of econometrics, 46(1–2), 229–245.
Galariotis, E., Kosmidou, K., Kousenidis, D., Lazaridou, E., & Papapanagiotou, T. (2020). Measuring the effects of M&As on Eurozone bank efficiency: An innovative approach on concentration and credibility impacts. Annals of Operations Research. https://doi.org/10.1007/s10479-020-03586-9
Golany, B., & Roll, Y. (1989). An application procedure for DEA. Omega, 17(3), 237–250.
Golany, B., & Storbeck, J. E. (1999). A data envelopment analysis of the operational efficiency of bank branches. Interfaces, 29(3), 14–26.
Grigorian, D., & Manole, V. (2005). A cross-country nonparametric analysis of Bahrain's banking system.
Gulati, R., & Kumar, S. (2017). Analysing banks’ intermediation and operating efficiencies using the two-stage network DEA model. International Journal of Productivity and Performance Management.
Hadad, M. D., Hall, M. J., Kenjegalieva, K. A., Santoso, W., & Simper, R. (2011). Banking efficiency and stock market performance: An analysis of listed Indonesian banks. Review of Quantitative Finance and Accounting, 37(1), 1–20.
Hadad, M. D., Hall, M. J., Kenjegalieva, K. A., Santoso, W., & Simper, R. (2012). A new approach to dealing with negative numbers in efficiency analysis: An application to the Indonesian banking sector. Expert Systems with Applications, 39(9), 8212–8219.
Hahn, F. R. (2009). A note on management efficiency and international banking. Some empirical panel evidence. Journal of Applied Economics, 12(1), 69–81.
Hartman, T. E., Storbeck, J. E., & Byrnes, P. (2001). Allocative efficiency in branch banking. European Journal of Operational Research, 134(2), 232–242.
Hastie, T., Tibshirani, R., & Friedman, J. (2009). Random forests. In The elements of statistical learning (pp. 587–604). Springer, New York, NY.
Hatami-Marbini, A., Emrouznejad, A., & Agrell, P. J. (2014). Interval data without sign restrictions in DEA. Applied Mathematical Modelling, 38(7–8), 2028–2036.
Henriques, I. C., Sobreiro, V. A., Kimura, H., & Mariano, E. B. (2020). Two-stage DEA in banks: Terminological controversies and future directions. Expert Systems with Applications., 161, 113632. https://doi.org/10.1016/j.eswa.2020.113632
Holod, D., & Lewis, H. F. (2011). Resolving the deposit dilemma: A new DEA bank efficiency model. Journal of Banking & Finance, 35(11), 2801–2810.
Hsiao, B., Chern, C. C., & Chiu, C. R. (2011). Performance evaluation with the entropy-based weighted Russell measure in data envelopment analysis. Expert Systems with Applications, 38, 9965–9972.
Jain, R. K., Natarajan, R., & Ghosh, A. (2016). Decision tree analysis for selection of factors in DEA: An application to banks in India. Global Business Review, 17(5), 1162–1178.
Jenkins, L., & Anderson, M. (2003). A multivariate statistical approach to reducing the number of variables in data envelopment analysis. European Journal of Operational Research, 147(1), 51–61.
Kaffash, S., Matin, R. K., & Tajik, M. (2018). A directional semi-oriented radial DEA measure: An application on financial stability and the efficiency of banks. Annals of Operations Research, 264(1–2), 213–234.
Khan, A., Hassan, M., Maroney, N., Boujlil, R., & Ozkan, B. (2020). Efficiency, diversification, and performance of US banks. International Review of Economics & Finance, 67, 101–117. https://doi.org/10.1016/j.iref.2019.12.010
Khezrimotlagh, D., Cook, W., & Zhu, J. (2019). Number of performance measures versus number of decision making units in DEA. Annals of Operations Research. https://doi.org/10.1007/s10479-019-03411-y
Konara, P., Tan, Y., & Johnes, J. (2019). FDI and Heterogeneity in Bank Efficiency: Evidence from Emerging Markets. Research in International Business and Finance, 49, 100–113.
Kumar, S., & Gulati, R. (2008). Evaluation of technical efficiency and ranking of public sector banks in India. International Journal of Productivity and Performance Management, 57(7), 540–568.
Kutlar, A., Kabasakal, A., & Babacan, A. (2015). Dynamic efficiency of Turkish Banks: A DEA window and Malmquist index analysis for the period of 2003–2012/Türkiye’deki Bankalarin Dinamik Etkinligi: 2003–2012 Dönemi için VZA-Pencere ve Malmquist Endeksi Analizi. Sosyoekonomi, 2, 71.
Lang, G., & Welzel, P. (1996). Efficiency and technical progress in banking Empirical results for a panel of German cooperative banks. Journal of Banking & Finance, 20(6), 1003–1023.
Lee, C. Y., & Cai, J. Y. (2020). LASSO variable selection in data envelopment analysis with small datasets. Omega, 91, 102019.
Li, Y., Shi, X., Yang, M., & Liang, L. (2017). Variable selection in data envelopment analysis via Akaike’s information criteria. Annals of Operations Research, 253, 453–476.
Lim, S. (2008). A decision tree-based method for selection of input–output factors in DEA. In Proceedings of the 2008 international conference on artificial intelligence, ICAI 2008, 14–17 July 2008.
Lin, T. T., Lee, C.-C., & Chiu, T.-F. (2009). Application of DEA in analyzing a bank’s operating performance. Expert Systems with Applications, 36(5), 8883–8891.
Liu, J., & Tone, K. (2008). A multistage method to measure efficiency and its application to Japanese banking industry. Socio-Economic Planning Sciences, 42(2), 75–91.
Lo, S.-F., & Lu, W.-M. (2009). An integrated performance evaluation of financial holding companies in Taiwan. European Journal of Operational Research, 198(1), 341–350.
Lozano-Vivas, A., Pastor, J. T., & Pastor, J. M. (2002). An efficiency comparison of European banking systems operating under different environmental conditions. Journal of Productivity Analysis, 18(1), 59–77.
Lu, T., & Liu, S. T. (2016). Ranking DMUs by Comparing DEA Cross-Efficiency Intervals Using Entropy Measures. Entropy, 18, 452–465. https://doi.org/10.3390/e18120452
Lunetta, K. L., Hayward, L. B., Segal, J., & Van Eerdewegh, P. (2004). Screening large-scale association study data: Exploiting interactions using random forests. BMC Genetics, 5(1), 32.
Luo, Y., Bi, G., & Liang, L. (2012). Input/output indicator selection for DEA efficiency evaluation: An empirical study of Chinese commercial banks. Expert Systems with Applications, 39(1), 1118–2112.
Madhanagopal, R., & Chandrasekaran, R. (2014). Selecting appropriate variables for DEA using genetic algorithm (GA) search procedure. International Journal of Data Envelopment Analysis and Operations Research, 1(2), 28–33.
Mahmoudabadi, M. Z., & Emrouznejad, A. (2019). Comprehensive performance evaluation of banking branches: A three-stage slacks-based measure (SBM) data envelopment analysis. International Review of Economics and Finance, 64, 359–376.
Marie, A., Al-Nasser, A., & Ibrahim, M. (2013). Operational-Profitability-Quality Performance of Dubai’s Banks. Journal of Management Research, 13(1), 25–34.
Matin, R. K., Amin, G. R., & Emrouznejad, A. (2014). A modified semi-oriented radial measure for target setting with negative data. Measurement, 54, 152–158.
Montillo, A. A. (2009). Random forests. Lecture in Statistical Foundations of Data Analysis.
Moradi-Motlagh, A., & Saleh, A. S. (2014). Re-examining the technical efficiency of Australian banks: A Bootstrap DEA Approach. Australian Economic Papers, 53(1–2), 112–128.
Nataraja, N. R., & Johnson, A. L. (2011). Guidelines for using variable selection techniques in data envelopment analysis. European Journal of Operational Research, 215(3), 662–669.
Neralić, L., & Wendell, R. E. (2004). Sensitivity in data envelopment analysis using an approximate inverse matrix. Journal of the Operational Research Society, 55(11), 1187–1193.
Oliveira, C. V., & Tabak, B. M. (2005). An international comparison of banking sectors: A DEA approach. Global Economic Review, 34(3), 291–307.
Osman, I. H., Anouze, A. L., Irani, Z., Al-Ayoubi, B., Lee, H., Balcı, A., et al. (2014). COBRA framework to evaluate e-government services: A citizen-centric perspective. Government information quarterly, 31(2), 243–256.
Osman, I. H., Anouze, A. L., Irani, Z., Lee, H. T., Medeni, D., & Weerakkody, V. (2019). A cognitive analytics management framework for the transformation of electronic government services from users’ perspective to create sustainable shared values. European Journal of Operational Research, 278(2), 514–532.
Osman, I. H., Berbary, L. N., Sidani, Y., Al-Ayoubi, B., & Emrouznejad, A. (2011). A data envelopment analysis model for the appraisal and relative performance evaluation of nurses at an intensive care unit. Journal of Medical Systems, 35(5), 1039–1062.
Osman, I.H., Hitti, A., & Al-Ayoubi, B. (2008). Data envelopment analysis: A tool for monitoring the relative efficiency of Lebanese Banks. In CD-ROM online proceedings of the European and mediterranean on information systems conference (ECMS2008) late breaking papers, LBP7, pp 1–9, May 25–26th, 2008, Al-Bustan Rotana, Dubai, UAE, Editors: Zahir Irani et al. ISBN: 902316–59–8
Osman, I. H., & Zablith, F. (2020). Re-evaluating electronic government development index to monitor the transformation toward achieving sustainable development goals. Journal of Business Research. https://doi.org/10.1016/j.jbusres.2020.10.027
Ouenniche, J., & Carrales, S. (2018). Assessing efficiency profiles of UK commercial banks: A DEA analysis with regression-based feedback. Annals of Operations Research, 266(1–2), 551–587.
Paradi, J. C., & Zhu, H. (2013). A survey on bank branch efficiency and performance research with data envelopment analysis. Omega, 41(1), 61–79.
Park, K. H., & Weber, W. L. (2006). A note on efficiency and productivity growth in the Korean banking industry, 1992–2002. Journal of Banking & Finance, 30(8), 2371–2386.
Pastor, J. T., Ruiz, J. L., & Sirvent, I. (2002). A statistical test for nested radial DEA models. Operations Research, 50(4), 728–735.
Petropoulos, A., Siakoulis, V., Stavroulakis, E., & Vlachogiannakis, N. E. (2020). Predicting bank insolvencies using machine learning techniques. International Journal of Forecasting, 36, 1092–1113.
Peyrachea, A., Rosea, C., & Siciliab, G. (2020). Variable selection in Data Envelopment Analysis. European Journal of Operational Research, 282(2), 644–659.
Qi, X. G., & Guo, B. (2014). Determining common weights in data envelopment analysis with Shannon’s entropy. Entropy, 16, 6394–6414.
Qin, Z., & Song, I. (2014). Joint variable selection for data envelopment analysis via group sparsity. SSRN 2406690.
Rao, K. R. M., & Lakew, T. B. (2012). Cost Efficiency and Ownership Structure of Commercial Banks in Ethiopia: An application of non-parametric approach. European Journal of Business and Management, 4(10), 36–47.
Ray, S. C. (2007). Are some Indian banks too large? An examination of size efficiency in Indian banking. Journal of Productivity Analysis, 27(1), 41–56.
Rezvanian, R., & Mehdian, S. (2002). An examination of cost structure and production performance of commercial banks in Singapore. Journal of Banking & Finance, 26(1), 79–98.
Ruggiero, J. (2005). Impact assessment of input omission on DEA. International Journal of Information Technology & Decision Making, 4(03), 359–368.
Sakar, B. (2006). A study on efficiency and productivity of Turkish banks in Istanbul stock exchange using Malmquist DEA. Journal of American Academy of Business, 8(2), 145–155.
Sealey, C. W., Jr., & Lindley, J. T. (1977). Inputs, outputs, and a theory of production and cost at depository financial institutions. The Journal of Finance, 32(4), 1251–1266.
Seiford, L. M., & Zhu, J. (1998). Stability regions for maintaining efficiency in data envelopment analysis. European Journal of Operational Research, 108(1), 127–139.
Sexton, T. R., Silkman, R. H., & Hogan, A. J. (1986). Data envelopment analysis: Critique and extensions. New Directions for Program Evaluation, 1986(32), 73–105.
Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27(379–656), 1948.
Sharma, M. J., & Yu, S. J. (2015). Stepwise regression data envelopment analysis for variable reduction. Applied Mathematics and Computation, 253, 126–134.
Shokrollahpour, E., Lotfi, F. H., & Zandieh, M. (2016). An integrated data envelopment analysis–artificial neural network approach for benchmarking of bank branches. Journal of Industrial Engineering International, 12(2), 137–143.
Sigala, M., Airey, D., Jones, P., & Lockwood, A. (2004). ICT paradox lost? A stepwise DEA methodology to evaluate technology investments in tourism settings. Journal of Travel Research, 43(2), 180–192.
Simar, L., & Wilson, P. W. (2001). Testing restrictions in nonparametric efficiency models. Communications in Statistics-Simulation and Computation, 30(1), 159–184.
Simar, L., & Wilson, P. (2019). Technical, Allocative and Overall Efficiency: Estimation and Inference. European Journal of Operational Research. https://doi.org/10.1016/j.ejor.2019.10.011
Smith, P. (1997). Model misspecification in data envelopment analysis. Annals of Operations Research, 73, 233–252.
Soleimani-Damaneh, M., & Zarepisheh, M. (2009). Shannon’s entropy for combining the efficiency results of different DEA models: Method and application. Expert Systems with Applications, 36, 5146–5150.
Soteriou, A. C., & Stavrinides, Y. (2000). An internal customer service quality data envelopment analysis model for bank branches. The International Journal of Bank Marketing, 18(5), 246–252.
Spokeviciute, L., Keasey, K., & Vallascas, F. (2019). Do Financial Crises Cleanse the Banking Industry? Evidence from US Commercial Bank Exits. Journal of Banking and Finance, 99, 222–236.
Storto, C. (2016). Ecological Efficiency Based Ranking of Cities: A Combined DEA Cross-Efficiency and Shannon’s Entropy Method. Sustainability, 8, 124.
Strobl, C., & Augustin, T. (2009). Adaptive Selection of Extra Cutpoints—Towards Reconciling Robustness and Interpretability in Classification Trees. Journal of Statistical Theory and Practice, 3(1), 119–135.
Strobl, C., Malley, J., & Tutz, G. (2009). An introduction to recursive partitioning: Rationale, application, and characteristics of classification and regression trees, bagging, and random forests. Psychological methods, 14(4), 323.
Subramanyam, T. (2016). Selection of input-output variables in data envelopment analysis-Indian commercial banks. International Journal of Computer & Mathematical Sciences, 5(6), 2347–8527.
Tanaka, K., Kinkyo, T., & Hamori, S. (2016). Random forests-based early warning system for bank failures. Economics Letters, 148, 118–121.
Thanassoulis, E. (2001). Introduction to the theory and application of data envelopment analysis. Springer.
Tortosa-Ausina, E. (2004). An alternative conditioning scheme to explain efficiency differentials in banking. Economics Letters, 82(2), 147–155.
Tsionas, E. G., & Papadakis, E. N. (2010). A Bayesian approach to statistical inference in stochastic DEA. Omega, 38(5), 309–314.
Tulkens, H., & Eeckaut, P. V. (1995). Non-parametric efficiency, progress and regress measures for panel data: Methodological aspects. European Journal of Operational Research, 80(3), 474–499.
Ueda, T., & Hoshiai, Y. (1997). Application of principal component analysis for parsimonious summarization of DEA inputs and/or outputs. Journal of the Operations Research society of Japan, 40(4), 466–478.
Wagner, J. M., & Shimshak, D. G. (2007). Stepwise selection of variables in data envelopment analysis: Procedures and managerial perspectives. European Journal of Operational Research, 180(1), 57–67.
Wang, Q., Zhao, Z., Zhou, P., & Zhou, D. (2013). Energy efficiency and production technology heterogeneity in China: A meta-frontier DEA approach. Economic Modelling, 35, 283–289.
Wozniewska, G. (2015). Methods of measuring the efficiency of commercial banks: An example of Polish banks. Ekonomika, 85, 81–91.
Wu, D., Yang, Z., & Liang, L. (2006a). Using DEA-neural network approach to evaluate branch efficiency of a large Canadian bank. Expert Systems with Applications, 31(1), 108–115.
Wu, D. D., Yang, Z., & Liang, L. (2006b). Using DEA-neural network approach to evaluate branch efficiency of a large Canadian bank. Expert Systems with Applications, 31(1), 108–115.
Xie, J., Zhu, X., & Liang, L. (2020). A multiplicative method for estimating the potential gains from two-stage production system mergers. Annals of Operations Research. https://doi.org/10.1007/s10479-020-03530-x
Xie, Q., Dai, Q., Li, Y., & Jiang, A. (2014). Increasing the discriminatory power of DEA using Shannon’s entropy. Entropy, 16, 1571–1585.
Yang, Z. (2009). Assessing the performance of Canadian bank branches using data envelopment analysis. Journal of the Operational Research Society, 60(6), 771–780.
Yin, Z., Xie, F., & Xu, Y. (2010). An empirical analyze on the credit risk management efficiency of Chinese commercial banks. Paper presented at the 2010 International Conference on Management and Service Science.
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. https://doi.org/10.1016/j.omega.2019.102145
Zhu, J. (2000). Multi-factor performance measure model with an application to Fortune 500 companies. European Journal of Operational Research, 123(1), 105–124.
Acknowledgements
The authors would like to acknowledge the support of the National Research Center of Lebanon and the University Research Board of the American University of Beirut for funding this research. Furthermore, the authors would like to thank Professor Samuel F. Wamba, co-editors and the three anonymous reviewers for their insightful and constructive comments, and suggestions to improve the quality of the paper.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendix: DEA models used to evaluate the performance efficiency of banks
Appendix: DEA models used to evaluate the performance efficiency of banks
Authors | Year | Input variables | Output variables |
|---|---|---|---|
Chen (1998) | 1998 | Staff, Assets, Interest expense | Loans, investments, Interest income, Non-interest income |
Chen and Yeh (1998) | 1998 | Staff, Assets, number of branches, Operating costs, Deposits, Interest expenses | Loans, investments, Interest income, Non-interest income |
Golany and Storbeck (1999) | 1999 | Operating expenses, Size of branch in square feet | Loans, Deposits, Number of accounts per customer |
Chen and Yeh (2000) | 2000 | Staff, Assets, Deposits | Loans, investments, Non-interest income |
Soteriou and Stavrinides (2000) | 2000 | Operating costs, Size (M2), No of accounts | Service Quality Level |
Dekker and Post (2001) | 2001 | Front office Personnel, Facilitating Personnel | Total Revenues |
Dias and Helmers (2001) | 2001 | Total capital, Ratio of interest expense to total expenses, Ratio of employee Salary and benefit expenses to total expenses | Ratio of demand deposits to total deposits, Total loans |
Hartman et al. (2001) | 2001 | Staff, No of computer terminals, Square meters of premises | Deposits, Loans, Amount of house mortgages, Number of customers |
Chen (2001) | 2001 | Staff, No of bank branches, deposits, fixed assets | Loans, Investments, Non-interest revenues |
Chen (2002) | 2002 | Labor, Assets, Deposits | Loans, Investments, Non-interest revenues |
Lozano-Vivas et al. (2002) | 2002 | Personnel expenses, Non-interest expenses | Loans, Deposits, Other earning asset |
Rezvanian and Mehdian (2002) | 2002 | Borrowed funds, Other inputs | Loans, Securities, Other earning assets |
Casu and Molyneux (2003) | 2003 | Total costs, Total customers, and total deposits | Loans and other earning assets |
Canhoto and Dermine (2003) | 2003 | Number of employees, Physical capital | Loans, Deposits, Securities, Interbank assets/liabilities, Number of branches |
Brown and Gardener (2004) | 2004 | Labor, Property, other capital assets, and Financial capital | Net interest income and Non-interest income |
Chen (2004) | 2004 | Labor, Assets, Deposits, Interest expense, Branches | Loans, Investments, Non-interest revenue, Interest Revenue |
Tortosa-Ausina (2004) | 2004 | Labor, Funding, Capital | Loans, Securities, non-interest income |
Grigorian and Manole (2005) | 2005 | Operating expenses, Fixed assets, Branch network, Equipment, Interest expenditures | Revenues, Loans, and Liquid assets |
Casu and Girardone (2005) | 2005 | Labor, interest expenses, cost of capital | Total Loans and Securities |
Oliveira and Tabak (2005) | 2005 | The market risk | Stocks Profitability of banks of each country |
Park and Weber (2006) | 2006 | Labor, Capital, Deposits, Interest expense, Non-interest expense, Equity | Loans, Securities, Non-performing loans, Demand deposits, Interest income, Non-interest income |
Sakar (2006) | 2006 | Number of branches, number of personnel per branch, assets, loans, deposits | ROA, ROE, Net interest income/total assets, Net interest income/total operating income, Non-interest income/total assets |
Ray (2007) | 2007 | Borrowed funds, Labor, Physical capital, Equity | Credit, Investments, Other income |
Liu and Tone (2008) | 2008 | Interest expense, Credit cost, General and administrative expenses | Interest-accruing loans, Lending revenue |
Kumar and Gulati (2008) | 2008 | Physical capital, Labor, Loanable funds | Net-interest income and non-interest income |
Hahn (2009) | 2009 | Total costs and deposits | Loans, deposits, Other earning assets |
Lo and Lu (2009) | 2009 | Assets, Stockholders’ equity, Employees, Revenues, Profits | Revenues, Profits, Market value, Earnings per share (EPS) |
Lin et al. (2009) | 2009 | Staff, Interest expense, Deposits | Loan operating amount, Interest revenue, Operating revenue, Earnings |
Yang (2009) | 2009 | Total deposits, Personnel expense, Fixed assets | Loans, Portfolio investment, Non-interest revenue |
Tsionas and Papadakis (2010) | 2010 | Labor, Physical capital and deposits | Loans, Investments, and Liquid assets |
Barros et al. (2010) | 2010 | Fixed assets, Variable cost | Loans, Securities, Off balance sheet items |
Yin et al. (2010) | 2010 | Staff, Operating expenses, Depreciation charges | Non-performing loan rate |
Hadad et al. (2011) | 2011 | Deposits, operating expenses, Total fixed assets | Loans, other earning assets, Net interest revenue, other income, Net commission, net fee net trading income, Deposits, and Loan loss provisions |
Deng et al. (2011) | 2011 | Branches, Staff, Deposits | Loans and Advances, Profit |
Hadad et al. (2012) | 2012 | Deposits, operating expenses, non-operating expenses, Total loan loss provisions | Total loans, Total other earning assets Net total off balance-sheet income |
Arjomandi et al. (2012) | 2012 | Labor, Physical capital, Purchased funds | Total demand deposits, State-owned sector loans, Non-state-owned loans |
Marie et al. (2013) | 2013 | Customers’ accounts, operating expenses, fees and commission income, and net interest income | ROA, ROE, Overall Customer Satisfaction |
Moradi-Motlagh and Saleh (2014) | 2014 | Interest expense and Non-interest Expense | Interest income and non-interest income |
Chang et al. (2015) | 2015 | Fixed assets, deposits, and staff expenses | Loans, securities investments, Non-interest income |
Kutlar et al. (2015) | 2015 | Net Assets, Deposits, Interest Expenses, Fees and Commissions, Other Operations, Salaries, number of Personnel | Loans, Operational Income, Interest Income, Fees & Commissions, Other Operational Income |
Chen et al. (2015) | 2015 | Total deposits, number of staff, fixed assets, NPL ratio | Total loans, Total investment, Non-interest income |
Shokrollahpour et al. (2016) | 2016 | Income, fees, loan granted account, main, current, and other deposits | Deposit’s paid profit and expenses |
Jain et al. (2016) | 2016 | Interest expenses and operating expenses | Interest Income and Operating Income |
Bou-Hamad et al. (2017) | 2017 | Fixed assets, Deposits, Equity, Interest expense and Personnel expenses | Loans, Net income, Off-balance sheet items and Liquid assets |
Kaffash et al. (2018) | 2018 | Total non-interest expenses, Other operating expenses, Fixed assets, Deposits & short term funding and Equity | Gross interest and dividend income, Total non-interest operating income, Loans and Net income |
Ouenniche and Carrales (2018) | 2018 | Personnel expenses, Fixed assets, Equity, Total interest expense and Total expenses not including personnel expense | Gross loans, Total customer deposits, Gross interest and dividend income and Total income |
Anouze and Bou-Hamad (2019) | 2019 | Fixed assets, Deposits, Equity, Interest expense and Personnel expenses | Loans, Net income, Off-balance sheet and Liquid assets |
Yu et al. (2019) | 2019 | Labor, Physical Capital, Deposit and Operating Expense | Loan, Securities Investment, Deposit and Non-interest Income |
Simar and Wilson (2019) | 2019 | Labor, Capital and Loanable funds | Real estate loans, Commercial and industrial loans, Consumer loans, All other loans and Demand deposits |
Konara et al. (2019) | 2019 | Personnel expenses, Total non-interest expenses and Total interest expenses | Total customer deposit, Loans and Other earning assets |
Fang et al. (2019) | 2019 | Interest expenses and Non-interest expenses | Deposits, Total loans, Securities and Non-interest income |
Spokeviciute et al. (2019) | 2019 | Total deposits, Premises and Fixed assets, and Number of employees | Total securities and Total gross loans and leases |
Khan et al. (2020) | 2020 | Labor (staff costs), Fixed capital (costs of premises and fixed assets) and Customer and short-term funding funds | Total loans, Other earning assets (directed and specialized loans, treasury and other securities and Off-balance sheet items |
Belasri et al. (2020) | 2020 | In the first stage, Staff costs, Fixed assets and Equity. In the second stage deposits | In the first stage, deposits. In the second stage, loans and securities |
Galariotis et al. (2020) | 2020 | Fixed assets, Deposits and short-term funding, Number of employees and loan loss provisions | Loans and Other earning assets |
Xie et al. (2020) | 2020 | Fixed assets and Personnel expenses | Interest income and Non-interest income |
Rights and permissions
About this article
Cite this article
Bou-Hamad, I., Anouze, A.L. & Osman, I.H. A cognitive analytics management framework to select input and output variables for data envelopment analysis modeling of performance efficiency of banks using random forest and entropy of information. Ann Oper Res 308, 63–92 (2022). https://doi.org/10.1007/s10479-021-04024-0
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10479-021-04024-0