pp 1–20 | Cite as

Market share and performance in Taiwanese banks: min/max SBM DEA

  • Ying Li
  • Yung-ho ChiuEmail author
  • Tai-Yu Lin
  • Yun Yuan Huang
Original Paper


This research used a min/max slacks-based measure data envelopment analysis (SBM DEA) to explore the operational efficiency of 37 banks in Taiwan from 2012 to 2016, with the main research goals being to analyze bank efficiency, compare the differences in the average efficiency values of two models, and identify the need for improvements in the inputs and outputs, from which it was found: (1) the average efficiency of the SBM-max model for each year was higher than the SBM-min model. (2) The best input performance was “deposits”, and the worst input was “the number of employees.” The best output performance was “loans”, and the worse output was “investments”. (3) The average efficiency of the finance holding company subsidiary banks was higher than the non-finance holding banks, the average efficiency of the large-scale banks was higher than the small-scale banks, and the average efficiency of banks with high deposit and loan market share was higher than banks with low deposits and loan market share.


Commercial bank Data envelopment analysis Efficiency Evaluation SBM 



This study was supported by the National Natural Science Foundation of China (No. 71773082);


  1. Akeem UO, Moses F (2014) An empirical analysis of allocative efficiency of Nigerian commercial banks: a DEA approach. Int J Econ Fin 4(3):465–475Google Scholar
  2. Akther S, Fukuyama H, Weber WL (2013) Estimating two-stage network slacks based inefficiency: an application to Bangladesh banks. Omega 41:88–96Google Scholar
  3. Bahrini R (2017) Efficiency analysis of Islamic banks in the Middle East and North Africa region: a Bootstrap DEA approach. Int J Fin Stud 5(1–10):1–12Google Scholar
  4. Baten A, Kasim MM, Rahman M (2015) Efficiency and productivity change of selected online banks in Bangladesh: a non-parametric Malmquist approach. J Internet Bank Commerce 20(3):1–16Google Scholar
  5. Bell F, Murphy N (1976) Costs in commercial banking: a quantitative analysis of bank behavior and its relation to bank regulation. Research report, 41. Federal Research Bank, BostonGoogle Scholar
  6. Benli YK, Degirmen S (2013) The application of data envelopment analysis based Malmquist total factor productivity index: empirical evidence in Turkish banking sector. Panoeconomicus 60(2):139–159Google Scholar
  7. Bhattacharyya A, Lovell CAK, Sahary P (1997) The impact of liberalization on the productive efficiency of Indian commercial banks. Eur J Oper Res 98(2):332–345Google Scholar
  8. Cava PB, Junior APS, de Branco AMF (2016) Evaluation of bank efficiency in Brazil: a DEA approach. Revista de Administração Mackenzie 17(4):62–84Google Scholar
  9. Chen TY, Yeh TL (2000) A measurement of bank efficiency, ownership and productivity change. Serv Ind J 20(1):95–109Google Scholar
  10. Chen MJ, Chiu YH, Jan CL, Chen YC, Liu HH (2015) Efficiency and risk in commercial banks—hybrid DEA estimation. Glob Econ Rev 44(3):335–352Google Scholar
  11. Chiu C, Chiu Y, Chen Y, Fang C (2016) Exploring the source of metafrontier inefficiency for various bank types in the two-stage network system with undesirable output. Pacific-Basin Finance J 36:1–13Google Scholar
  12. de Branco AMF, Junior APS, Cava PB, Carneiro M (2016) Efficiency of the Brazilian banking system: an assessment using DEA under three approaches. J Appl Fin Bank 6(4):27–42Google Scholar
  13. Erasmus C, Makina D (2014) An empirical study of bank efficiency in South Africa using the standard and alternative approaches to data envelopment analysis (DEA). J Econ Behav Stud 6(4):310–317Google Scholar
  14. Ferrier GD, Lovell CAK (1990) Measuring cost efficiency in banking: econometric and linear programming evidence. J Econometr 46:229–245Google Scholar
  15. Fukuyama H, Matousek R (2016) Modelling bank performance: a network DEA approach. Eur J Oper Res 259:721–732Google Scholar
  16. Fukuyama H, Weber WL (2015) Measuring Japanese bank performance: a dynamic network DEA approach. J Prod Anal 44(3):249–264Google Scholar
  17. Fukuyama H, Guerra R, Weber WL (1999) Efficiency and ownership: evidence from japanese credit cooperatives. J Econ Bus 51:473–487Google Scholar
  18. Grigoroudis E, Tsitsiridi E, Zopounidis C (2013) Linking customer satisfaction, employee appraisal, and business performance: an evaluation methodology in the banking sector. Ann Oper Res 205:5–27Google Scholar
  19. Hassan H, Jreisat A (2016) Does bank efficiency matter? A case of Egypt. Int J Econ Fin Issues 6(2):473–478Google Scholar
  20. Hu JL, Wang SC (2006) Total-factor energy efficiency of regions in China. Energy Policy 34(17):3206–3217Google Scholar
  21. Kao C, Liu ST (2014) Multi-period efficiency measurement in data envelopment analysis: the case of Taiwanese commercial banks. Omega 47:90–98Google Scholar
  22. Kumar S, Gulati R (2008) Evaluation of technical efficiency and ranking of public sector banks in India. Int J Prod Perform Manag 57(7):540–568Google Scholar
  23. Lee CW, Peng CJ, Fu WC (2015) Study on efficiency sustainability of Taiwan’s bank performance under a dynamic framework. J Appl Fin Bank 5(2):1–17Google Scholar
  24. Liang L, Cook WD, Zhu J (2008) DEA models for two-stage processes: game approach and efficiency decomposition. Naval Res Log 55:643–653Google Scholar
  25. Moradi-Motlagh A, Valadkhani A, Saleh AS (2015) Rising efficiency and cost saving in australian banks: a bootstrap approach. Appl Econ Lett 22(1–3):189–194Google Scholar
  26. Radojicic M, Savic G, Radovanovic S, Jeremic V (2015) A novel bootstrap DBA-DEA approach in evaluating efficiency of banks. Sci Bull Mircea cel Batran Naval Acad 18(2):375–384Google Scholar
  27. Sanjeev GM (2007) Does banks’ size matter in India? J Serv Res 6(2):135–144Google Scholar
  28. Schaffnit C, Rosen D, Paradi JC (1997) Best practice analysis of bank branches: an application of DEA in a large Canadian bank. Eur J Oper Res 98(2):269–289Google Scholar
  29. Seiford LM, Zhu J (1999) Profitability and marketability of the top 55 US commercial banks. Manag Sci 45(9):1270–1288Google Scholar
  30. Shafiee M, Sangi M, Ghaderi M (2013) Bank performance evaluation using dynamic DEA: a slacks-based measure approach. J Data Envel Anal Decis Sci 2013:1–12Google Scholar
  31. Singh RI, Kaur S (2016) Efficiency and profitability of public and private sector banks in India: data envelopment analysis approach. IUP J Bank Manag 15(1):50–68Google Scholar
  32. Sueyoshi T (1999) DEA-discriminate analysis in the view of goal programming. Eur J Oper Res 115:564–582Google Scholar
  33. Sufian F, Noor MA (2009) The determinants of Islamic banks’ efficiency changes. Int J Islamic Middle East Fin Manag 2(2):120–138Google Scholar
  34. Tone K (2001) A slacks-based measure of efficiency in data envelopment analysis. Eur J Oper Res 130(3):498–509Google Scholar
  35. Tone K (2016) Data envelopment analysis as a Kaizen tool: SBM variations revisited. Bull Math Sci Appl 16:49–61Google Scholar
  36. Tsolas IE (2011) Bank branch-level DEA to assess overall efficiency. EuroMed J Bus 6(3):359–377Google Scholar
  37. Tuskan B, Stojanovic A (2016) Measurement of cost efficiency in the European banking industry. Croat Oper Res Rev 7(1):47–66Google Scholar
  38. Wanke P, Barros C (2013) Two-stage DEA: an application to major Brazilian banks. Expert Syst Appl Int J 41:2337–2344Google Scholar
  39. Wild J (2016) Efficiency and risk convergence of Eurozone financial markets. Res Int Bus Fin 36:196–211Google Scholar
  40. Wong WP, Deng Q (2016) Efficiency analysis of banks in ASEAN countries. Benchmarking 23(7):1798–1817Google Scholar

Copyright information

© Sociedad de Estadística e Investigación Operativa 2019

Authors and Affiliations

  • Ying Li
    • 1
  • Yung-ho Chiu
    • 2
    Email author
  • Tai-Yu Lin
    • 2
  • Yun Yuan Huang
    • 2
  1. 1.Business SchoolSichuan UniversityChengduPeople’s Republic of China
  2. 2.Department of EconomicsSoochow UniversityTaipeiTaiwan, ROC

Personalised recommendations