Improved common weight DEA-based decision approach for economic and financial performance assessment
Abstract
Economic and financial performance assessment possesses an important role for efficient usage of available resources. In this study, a novel common weight multiple criteria decision making (MCDM) approach based on data envelopment analysis (DEA) is presented to identify the best performing decision making unit (DMU) accounting for multiple inputs as well as multiple outputs. The robustness of the developed model, which provides a rank-order with enhanced discriminatory characteristics and improved weight dispersion, is illustrated by two case studies that aim to provide economic and financial performance assessment. The first study presents an evaluation of Morgan Stanley Capital International emerging markets, whereas the second case study ranks the Turkish deposit banks using the proposed methodology as well as providing a comparative evaluation with several other approaches addressed in earlier works. The results indicate that the introduced approach guarantees to identify the best performing DMU without including a discriminating parameter requiring an arbitrary step size value in model formulation while also achieving an improved weight dispersion for inputs and outputs.
Keyword : common weight DEA-based models, discriminating power, decision analysis, performance evaluation, MSCI emerging markets, Turkish banking sector
How to Cite
Karsak, E. E., & Goker, N. (2020). Improved common weight DEA-based decision approach for economic and financial performance assessment. Technological and Economic Development of Economy, 26(2), 430-448. https://doi.org/10.3846/tede.2020.11870
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Puri, J., & Yadav, S. P. (2016). A fully fuzzy DEA approach for cost and revenue efficiency measurements in the presence of undesirable outputs and its application to the banking sector in India. International Journal of Fuzzy Systems, 18(2), 212–226. https://doi.org/10.1007/s40815-015-0031-6
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Toloo, M. (2013). The most efficient unit without explicit inputs: An extended MILP-DEA model. Measurement, 46(9), 3628–3634. https://doi.org/10.1016/j.measurement.2013.06.030
Titko, J., Stankeviciene, J., & Lace, N. (2014). Measuring bank efficiency: DEA application. Technological and Economic Development of Economy, 20(4), 739–757. https://doi.org/10.3846/20294913.2014.984255
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Yang, B., Zhang, Y., Zhang, H., Zhang, R., & Xu, B. (2016). Factor-specific Malmquist productivity index based on common weights DEA. Operational Research, 16(1), 51–70. https://doi.org/10.1007/s12351-015-0185-x
Amirteimoori, A., & Emrouznejad, A. (2012). Optimal input/output reduction in production processes. Decision Support Systems, 52(3), 742–747. https://doi.org/10.1016/j.dss.2011.11.020
Andersen, P., & Petersen, N. C. (1993). A procedure for ranking efficient units in data envelopment analysis. Management Science, 39(10), 1261–1264. https://doi.org/10.1287/mnsc.39.10.1261
Carillo, M., & Jorge, J. M. (2016). A multiobjective DEA approach to ranking alternatives. Expert Systems with Applications, 50, 130–139. https://doi.org/10.1016/j.eswa.2015.12.022
Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision-making units. European Journal of Operational Research, 2(6), 429–444. https://doi.org/10.1016/0377-2217(78)90138-8
Chodakowska, E., & Nazarko, J. (2017), Environmental DEA method for assessing productivity of European countries, Technological and Economic Development of Economy, 23(4), 589–607. https://doi.org/10.3846/20294913.2016.1272069
Degl’Innocenti, M., Kourtzidis, S. A., Sevic, Z., & Tzeremes, N. G. (2017). Bank productivity growth and convergence in the European Union during the financial crisis. Journal of Banking & Finance, 75, 184–199. https://doi.org/10.1016/j.jbankfin.2016.11.016
Elhadef, S., Ogun, F., & Topcu, L. (2016). Turkish banking: Current state and the way forward. Global Banking & Financial Policy Review 2015/2016. Retrieved February 21, 2017, from http://www.ey.com/Publication/vwLUAssets/Global_banking_and_financial_policy_review_2016_Turkey/
Feng, C., Wang, M., Liu, G. C., & Huang, J. B. (2017). Green development performance and its influencing factors: A global perspective. Journal of Cleaner Production, 144, 323–333. https://doi.org/10.1016/j.jclepro.2017.01.005
Foroughi, A. A. (2012). A modified common weight model for maximum discrimination in technology selection. International Journal of Production Research, 50(14), 3841–3846. https://doi.org/10.1080/00207543.2011.593201
Giambona, F., & Vassallo, E. (2014). Composite indicator of social inclusion for European countries. Social Indicators Research, 116(1), 269–293. https://doi.org/10.1007/s11205-013-0274-2
GCM Forex. (n.d.). Retrieved June 5, 2019, from www.gcmforex.com/
Grigoroudis, E., Tsitsiridi, E., & Zopounidis, C. (2013). Linking customer satisfaction, employee appraisal, and business performance: An evaluation methodology in the banking sector. Annals of Operations Research, 205(1), 5–27. https://doi.org/10.1007/s10479-012-1206-2
Hajiagha, S. H. R., Mahdiraji, H. A., Tavana, M., & Hashemi, S. S. (2018). A novel common set of weights method for multi-period efficiency measurement using mean-variance criteria. Measurement, 129, 569–581. https://doi.org/10.1016/j.measurement.2018.07.061
Hsu, Y. C., & Lee, C. C. (2014). Performance measurement in public spending: evidence from a nonparametric approach. Romanian Journal of Economic Forecasting, 3, 136–159.
Jahan, A., Edwards, K., & Bahraminasab, M. (2016). Multi-criteria decision analysis for supporting the selection of engineering materials in product design (2nd ed.). Butterworth-Heinemann.
Karsak, E. E., & Ahiska, S. S. (2005). Practical common weight multi-criteria decision-making approach with an improved discriminating power for technology selection. International Journal of Production Research, 43(8), 1537–1554. https://doi.org/10.1080/13528160412331326478
Karsak, E. E., & Ahiska, S. S. (2007). A common-weight MCDM framework for decision problems with multiple inputs and outputs. Lecture Notes in Computer Science, 1, 779–790. https://doi.org/10.1007/978-3-540-74472-6_64
Karsak, E. E., & Ahiska, S. S. (2008). Improved common weight MCDM model for technology selection. International Journal of Production Research, 46(24), 6933–6944. https://doi.org/10.1080/00207540701419364
Lozano-Vivas, A., & Pastor, J. T. (2006). Relating macro-economic efficiency to financial efficiency: A comparison of fifteen OECD countries over an eighteen year period. Journal of Productivity Analysis, 25(1–2), 67–78. https://doi.org/10.1007/s11123-006-7129-7
Moradi-Motlagh, A., & Babacan, A. (2015). The impact of the global financial crisis on the efficiency of Australian banks. Economic Modelling, 46, 397–406. https://doi.org/10.1016/j.econmod.2014.12.044
Morgan Stanley Capital International. (n.d.). Retrieved July 1, 2017, from https://www.msci.com/
Omrani, H. (2013). Common weights data envelopment analysis with uncertain data: A robust optimization approach. Computers and Industrial Engineering, 66(4), 1163–1170. https://doi.org/10.1016/j.cie.2013.07.023
Puri, J., & Yadav, S. P. (2016). A fully fuzzy DEA approach for cost and revenue efficiency measurements in the presence of undesirable outputs and its application to the banking sector in India. International Journal of Fuzzy Systems, 18(2), 212–226. https://doi.org/10.1007/s40815-015-0031-6
Ray, S. (2016). Cost efficiency in an Indian bank branch network: A centralized resources allocation model. Omega, 65, 69–81. https://doi.org/10.1016/j.omega.2015.12.009
Salahi, M., Torabi, N., & Amiri, A. (2016). An optimistic robust optimization approach to common set of weights in DEA. Measurement, 93, 67–73. https://doi.org/10.1016/j.measurement.2016.06.049
Sathye, M. (2003). Efficiency of banks in a developing economy: The case of India. European Journal of Operational Research, 148(3), 662–671. https://doi.org/10.1016/S0377-2217(02)00471-X
Sherman, H. D., & Zhu, J. (2006). Benchmarking with quality-adjusted DEA (Q-DEA) to seek lowercost high-quality service: Evidence from a U.S. bank application. Annals of Operations Research, 145(1), 301–319. https://doi.org/10.1007/s10479-006-0037-4
Sun, J., Wu, J., & Guo, D. (2013). Performance ranking of units considering ideal and anti-ideal DMU with common weights. Applied Mathematical Modelling, 37(9), 6301–6310. https://doi.org/10.1016/j.apm.2013.01.010
Toloo, M. (2013). The most efficient unit without explicit inputs: An extended MILP-DEA model. Measurement, 46(9), 3628–3634. https://doi.org/10.1016/j.measurement.2013.06.030
Titko, J., Stankeviciene, J., & Lace, N. (2014). Measuring bank efficiency: DEA application. Technological and Economic Development of Economy, 20(4), 739–757. https://doi.org/10.3846/20294913.2014.984255
Trading Economics. (n.d.). Retrieved July 8, 2017, from https://tradingeconomics.com/
Türkiye Bankalar Birliği. (n.d.). Retrieved May 7, 2019, from www.tbb.org.tr/
World Bank. (n.d.). World Bank Open Data. Retrieved July 8, 2017, from http://data.worldbank.org
Yang, B., Zhang, Y., Zhang, H., Zhang, R., & Xu, B. (2016). Factor-specific Malmquist productivity index based on common weights DEA. Operational Research, 16(1), 51–70. https://doi.org/10.1007/s12351-015-0185-x