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Regulation adaptive strategy and bank efficiency: A network slacks-based measure with shared resources

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  • Zhao, Linlin
  • Zhu, Qingyuan
  • Zhang, Lin

Abstract

Banks have two primary strategies for adapting to a regulation change in the era of big data which can be characterized as natural disposability and managerial disposability. Natural disposability implies a negative strategy by which a bank attempts to decreases its vector of inputs to decrease undesirable outputs. In contrast, managerial disposability indicates a positive strategy by which a bank considers a regulation change as an opportunity and adapt the regulation change by utilizing big data technology. The operational process of a bank can be decomposed into a productivity stage and a profitability stage. Furthermore, the operation costs, a shared resource, can be used to characterize natural disposability and managerial disposability. Based on natural disposability and managerial disposability, this paper proposes two network models to estimate the efficiencies of banks. To test their practical implications, the proposed models were applied to examine the efficiencies of Chinese commercial banks in the period 2014−2018. Our key findings are as follows. (1) There exist great disparities in the inefficiencies between two adaptive strategies. The inefficiencies are primarily driven by the profitability stage under natural disposability, whereas the inefficiencies are equally attributed to both stages under managerial disposability. (2) The efficiency differences among different types of banks are insignificant under natural disposability but are significant under managerial disposability. (3) Joint-stock commercial banks are more oveall efficient than state-owned commercial banks, city commercial banks and rural commercial banks, while state-owned commercial banks show worst practice for overall efficiency and profitability stage efficiency.

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  • Zhao, Linlin & Zhu, Qingyuan & Zhang, Lin, 2021. "Regulation adaptive strategy and bank efficiency: A network slacks-based measure with shared resources," European Journal of Operational Research, Elsevier, vol. 295(1), pages 348-362.
  • Handle: RePEc:eee:ejores:v:295:y:2021:i:1:p:348-362
    DOI: 10.1016/j.ejor.2021.02.050
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    References listed on IDEAS

    as
    1. Wu, Jie & Zhu, Qingyuan & Ji, Xiang & Chu, Junfei & Liang, Liang, 2016. "Two-stage network processes with shared resources and resources recovered from undesirable outputs," European Journal of Operational Research, Elsevier, vol. 251(1), pages 182-197.
    2. Güray Kara & Ayşe Özmen & Gerhard-Wilhelm Weber, 2019. "Stability advances in robust portfolio optimization under parallelepiped uncertainty," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 27(1), pages 241-261, March.
    3. Hirofumi Fukuyama & William Weber, 2015. "Measuring Japanese bank performance: a dynamic network DEA approach," Journal of Productivity Analysis, Springer, vol. 44(3), pages 249-264, December.
    4. Matthews, Kent, 2013. "Risk management and managerial efficiency in Chinese banks: A network DEA framework," Omega, Elsevier, vol. 41(2), pages 207-215.
    5. Zhou, Xiaoyang & Xu, Zhongwen & Chai, Jian & Yao, Liming & Wang, Shouyang & Lev, Benjamin, 2019. "Efficiency evaluation for banking systems under uncertainty: A multi-period three-stage DEA model," Omega, Elsevier, vol. 85(C), pages 68-82.
    6. Joseph C. Paradi & H. David Sherman & Fai Keung Tam, 2018. "Bank Branch Productivity Applications: Focused Applications to Improve Performance," International Series in Operations Research & Management Science, in: Data Envelopment Analysis in the Financial Services Industry, chapter 0, pages 113-127, Springer.
    7. Chen, Yao & Du, Juan & David Sherman, H. & Zhu, Joe, 2010. "DEA model with shared resources and efficiency decomposition," European Journal of Operational Research, Elsevier, vol. 207(1), pages 339-349, November.
    8. Lozano, Sebastián, 2016. "Slacks-based inefficiency approach for general networks with bad outputs: An application to the banking sector," Omega, Elsevier, vol. 60(C), pages 73-84.
    9. Barbara Casu & Philip Molyneux, 2003. "A comparative study of efficiency in European banking," Applied Economics, Taylor & Francis Journals, vol. 35(17), pages 1865-1876.
    10. Tone, Kaoru & Tsutsui, Miki, 2009. "Network DEA: A slacks-based measure approach," European Journal of Operational Research, Elsevier, vol. 197(1), pages 243-252, August.
    11. Berger, Allen N. & Humphrey, David B., 1997. "Efficiency of financial institutions: International survey and directions for future research," European Journal of Operational Research, Elsevier, vol. 98(2), pages 175-212, April.
    12. Joseph C. Paradi & H. David Sherman & Fai Keung Tam, 2018. "Bank Branch Productivity Applications: Managing Bank Productivity," International Series in Operations Research & Management Science, in: Data Envelopment Analysis in the Financial Services Industry, chapter 0, pages 101-112, Springer.
    13. Linlin Zhao & Lin Zhang & Yong Zha, 2019. "Industrial Efficiency Evaluation in China: A Nonparametric Production-Frontier Approach," Sustainability, MDPI, vol. 11(18), pages 1-23, September.
    14. Emel Savku & Gerhard-Wilhelm Weber, 2018. "A Stochastic Maximum Principle for a Markov Regime-Switching Jump-Diffusion Model with Delay and an Application to Finance," Journal of Optimization Theory and Applications, Springer, vol. 179(2), pages 696-721, November.
    15. Sueyoshi, Toshiyuki & Yuan, Yan & Goto, Mika, 2017. "A literature study for DEA applied to energy and environment," Energy Economics, Elsevier, vol. 62(C), pages 104-124.
    16. Barnabé Walheer, 2019. "Is constant returns-to-scale a restrictive assumption for sector-level empirical macroeconomics? The case of Europe," Applied Economics Letters, Taylor & Francis Journals, vol. 26(3), pages 231-236, February.
    17. Kaoru Tone & Miki Tsutsui, 2014. "Slacks-Based Network DEA," International Series in Operations Research & Management Science, in: Wade D. Cook & Joe Zhu (ed.), Data Envelopment Analysis, edition 127, chapter 0, pages 231-259, Springer.
    18. Fukuyama, Hirofumi & Matousek, Roman, 2017. "Modelling bank performance: A network DEA approach," European Journal of Operational Research, Elsevier, vol. 259(2), pages 721-732.
    19. Feng Li & Qingyuan Zhu & Jun Zhuang, 2018. "Analysis of fire protection efficiency in the United States: a two-stage DEA-based approach," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 40(1), pages 23-68, January.
    20. Drake, Leigh & Hall, Maximilian J.B. & Simper, Richard, 2006. "The impact of macroeconomic and regulatory factors on bank efficiency: A non-parametric analysis of Hong Kong's banking system," Journal of Banking & Finance, Elsevier, vol. 30(5), pages 1443-1466, May.
    21. Kopecky, Kenneth J. & VanHoose, David, 2006. "Capital regulation, heterogeneous monitoring costs, and aggregate loan quality," Journal of Banking & Finance, Elsevier, vol. 30(8), pages 2235-2255, August.
    22. Paradi, Joseph C. & Zhu, Haiyan, 2013. "A survey on bank branch efficiency and performance research with data envelopment analysis," Omega, Elsevier, vol. 41(1), pages 61-79.
    23. Joseph C. Paradi & H. David Sherman & Fai Keung Tam, 2018. "Bank Branch Productivity Applications: Basic Applications – Efficiency Measurement," International Series in Operations Research & Management Science, in: Data Envelopment Analysis in the Financial Services Industry, chapter 0, pages 87-100, Springer.
    24. A S Camanho & R G Dyson, 2005. "Cost efficiency, production and value-added models in the analysis of bank branch performance," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(5), pages 483-494, May.
    25. Chih-Ching Yang, 2014. "An enhanced DEA model for decomposition of technical efficiency in banking," Annals of Operations Research, Springer, vol. 214(1), pages 167-185, March.
    26. Boussemart, Jean-Philippe & Leleu, Hervé & Shen, Zhiyang & Vardanyan, Michael & Zhu, Ning, 2019. "Decomposing banking performance into economic and credit risk efficiencies," European Journal of Operational Research, Elsevier, vol. 277(2), pages 719-726.
    27. Laffont,Jean-Jacques, 2005. "Regulation and Development," Cambridge Books, Cambridge University Press, number 9780521840187, January.
    28. Cook, Wade D. & Hababou, Moez, 2001. "Sales performance measurement in bank branches," Omega, Elsevier, vol. 29(4), pages 299-307, August.
    29. Avkiran, Necmi Kemal, 2015. "An illustration of dynamic network DEA in commercial banking including robustness tests," Omega, Elsevier, vol. 55(C), pages 141-150.
    30. Sueyoshi, Toshiyuki & Goto, Mika, 2015. "DEA environmental assessment in time horizon: Radial approach for Malmquist index measurement on petroleum companies," Energy Economics, Elsevier, vol. 51(C), pages 329-345.
    31. Paradi, Joseph C. & Rouatt, Stephen & Zhu, Haiyan, 2011. "Two-stage evaluation of bank branch efficiency using data envelopment analysis," Omega, Elsevier, vol. 39(1), pages 99-109, January.
    32. Cui, Qiang & Li, Ye, 2018. "Airline dynamic efficiency measures with a Dynamic RAM with unified natural & managerial disposability," Energy Economics, Elsevier, vol. 75(C), pages 534-546.
    33. Sueyoshi, Toshiyuki & Goto, Mika, 2012. "Data envelopment analysis for environmental assessment: Comparison between public and private ownership in petroleum industry," European Journal of Operational Research, Elsevier, vol. 216(3), pages 668-678.
    34. Yang, Chyan & Liu, Hsian-Ming, 2012. "Managerial efficiency in Taiwan bank branches: A network DEA," Economic Modelling, Elsevier, vol. 29(2), pages 450-461.
    35. Lawrence M. Seiford & Joe Zhu, 1999. "Profitability and Marketability of the Top 55 U.S. Commercial Banks," Management Science, INFORMS, vol. 45(9), pages 1270-1288, September.
    36. Zha, Yong & Liang, Nannan & Wu, Maoguo & Bian, Yiwen, 2016. "Efficiency evaluation of banks in China: A dynamic two-stage slacks-based measure approach," Omega, Elsevier, vol. 60(C), pages 60-72.
    37. Wang, Ke & Huang, Wei & Wu, Jie & Liu, Ying-Nan, 2014. "Efficiency measures of the Chinese commercial banking system using an additive two-stage DEA," Omega, Elsevier, vol. 44(C), pages 5-20.
    38. Cevdet Denizer & Mustafa Dinc & Murat Tarimcilar, 2007. "Financial liberalization and banking efficiency: evidence from Turkey," Journal of Productivity Analysis, Springer, vol. 27(3), pages 177-195, June.
    39. Akther, Syed & Fukuyama, Hirofumi & Weber, William L., 2013. "Estimating two-stage network Slacks-based inefficiency: An application to Bangladesh banking," Omega, Elsevier, vol. 41(1), pages 88-96.
    40. Athanassopoulos, Antreas 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, Elsevier, vol. 98(2), pages 300-313, April.
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