Integration and application of rough sets and data envelopment analysis for assessments of the investment trusts industry

  • Wen-Min Lu
  • Qian Long KwehEmail author
  • Chung-Wei Wang
S.I. : MOPGP 2017


Approximately 40% of pension funds for military officials, civil servants, and educators in Taiwan are entrusted to international and domestic management funds. However, the average return rate for domestically outsourced funds is not greater than the earnings of self-managed funds. This study establishes a selection mechanism for pension fund outsourcing that conforms to current outsourcing management policies and accounts for both safety and profitability. This study conducts a network data envelopment analysis with considerations of dynamism to gauge the internal management efficiency and investment performance of 37 investment trust companies in Taiwan, thereby accurately measuring the links between internal economic activities and improving overlooked internal productivity activities. The results of this study indicate the internal and external corporate learning benchmarks, which are sequenced to assist the optimal outsourcing measures by applying rough set theory concepts. This study also provides suggestions for the Public Service Pension Fund Management Board in Taiwan in terms of future operations in domestic investment trust companies.


Rough sets Data envelopment analysis Performance evaluation Investment trust companies 



  1. An, Q., Chen, H., Wu, J., & Liang, L. (2015). Measuring slacks-based efficiency for commercial banks in China by using a two-stage DEA model with undesirable output. Annals of Operations Research, 235(1), 13–35.Google Scholar
  2. Bai, C., Fahimnia, B., & Sarkis, J. (2017). Sustainable transport fleet appraisal using a hybrid multi-objective decision making approach. Annals of Operations Research, 250(2), 309–340.Google Scholar
  3. 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.Google Scholar
  4. Basso, A., & Funari, S. (2003). Measuring the performance of ethical mutual funds: A DEA approach. Journal of the Operational Research Society, 54(5), 521–531.Google Scholar
  5. Berg, S. A., Førsund, F. R., Hjalmarsson, L., & Suominen, M. (1993). Banking efficiency in the Nordic countries. Journal of Banking and Finance, 17(2), 371–388.Google Scholar
  6. Berg, S. A., Førsund, F. R., & Jansen, E. S. (1992). Malmquist indices of productivity growth during the deregulation of Norwegian banking, 1980–1989. The Scandinavian Journal of Economics, S211–S228.Google Scholar
  7. Brandouy, O., Kerstens, K., & Van de Woestyne, I. (2015). Frontier-based vs. traditional mutual fund ratings: A first backtesting analysis. European Journal of Operational Research, 242(1), 332–342.Google Scholar
  8. Charnes, A., & Cooper, W. W. (1984). Preface to topics in data envelopment analysis. Annals of Operations Research, 2(1), 59–94.Google Scholar
  9. Chen, Y., Cook, W. D., & Zhu, J. (2010a). Deriving the DEA frontier for two-stage processes. European Journal of Operational Research, 202(1), 138–142.Google Scholar
  10. Chen, Y., Du, J., Sherman, H. D., & Zhu, J. (2010b). DEA model with shared resources and efficiency decomposition. European Journal of Operational Research, 207(1), 339–349.Google Scholar
  11. Cook, W. D., Liang, L., & Zhu, J. (2010). Measuring performance of two-stage network structures by DEA: A review and future perspective. Omega, 38(6), 423–430.Google Scholar
  12. Cooper, W. W., Seiford, L. M., & Tone, K. (2006). Introduction to data envelopment analysis and its uses: With DEA-solver software and references. USA: Springer Science.Google Scholar
  13. Du, K., Worthington, A. C., & Zelenyuk, V. (2018). Data envelopment analysis, truncated regression and double-bootstrap for panel data with application to Chinese banking. European Journal of Operational Research, 265(2), 748–764.Google Scholar
  14. Elyasiani, E., & Mehdian, S. M. (1990). A nonparametric approach to measurement of efficiency and technological change: The case of large US commercial banks. Journal of Financial Services Research, 4(2), 157–168.Google Scholar
  15. Färe, R., Grosskopf, S., Norris, M., & Zhang, Z. (1994). Productivity growth, technical progress, and efficiency change in industrialized countries. American Economic Review, 84(1), 66–83.Google Scholar
  16. Favero, C. A., & Papi, L. (1995). Technical efficiency and scale efficiency in the Italian banking sector: A non-parametric approach. Applied Economics, 27(4), 385–395.Google Scholar
  17. Ferreira, M. A., Keswani, A., Miguel, A. F., & Ramos, S. B. (2013). The determinants of mutual fund performance: A cross-country study. Review of Finance, 17(2), 483–525.Google Scholar
  18. Galagedera, D. U. A., Roshdi, I., Fukuyama, H., & Zhu, J. (2018). A new network DEA model for mutual fund performance appraisal: An application to U.S. equity mutual funds. Omega, 77, 168–179.Google Scholar
  19. Galagedera, D. U., & Silvapulle, P. (2002). Australian mutual fund performance appraisal using data envelopment analysis. Managerial Finance, 28(9), 60–73.Google Scholar
  20. Galagedera, D. U., Watson, J., Premachandra, I., & Chen, Y. (2016). Modeling leakage in two-stage DEA models: An application to US mutual fund families. Omega, 61, 62–77.Google Scholar
  21. Golany, B., & Roll, Y. (1989). An application procedure for DEA. Omega, 17(3), 237–250.Google Scholar
  22. Greco, S., Matarazzo, B., & Slowinski, R. (2001). Rough sets theory for multicriteria decision analysis. European Journal of Operational Research, 129(1), 1–47.Google Scholar
  23. Greco, S., Matarazzo, B., & Słowiński, R. (2010). Dominance-based rough set approach to decision under uncertainty and time preference. Annals of Operations Research, 176(1), 41–75.Google Scholar
  24. Greco, S., Matarazzo, B., Slowinski, R., & Zanakis, S. (2011). Global investing risk: A case study of knowledge assessment via rough sets. Annals of Operations Research, 185(1), 105–138.Google Scholar
  25. Gregoriou, G. N. (2006). Optimisation of the largest US mutual funds using data envelopment analysis. Journal of Asset Management, 6(6), 445–455.Google Scholar
  26. Haslem, J. A., & Scheraga, C. A. (2003). Data envelopment analysis of Morningstar’s large-cap mutual funds. The journal of Investing, 12(4), 41–48.Google Scholar
  27. Haslem, J. A., & Scheraga, C. A. (2006). Data envelopment analysis of Morningstar’s small-cap mutual funds. The journal of Investing, 15(1), 87–92.Google Scholar
  28. Ho, C.-T. B., & Wu, D. D. (2009). Online banking performance evaluation using data envelopment analysis and principal component analysis. Computers and Operations Research, 36(6), 1835–1842.Google Scholar
  29. Hu, J.-L., & Fang, C.-Y. (2010). Do market share and efficiency matter for each other? An application of the zero-sum gains data envelopment analysis. Journal of the Operational Research Society, 61(4), 647–657.Google Scholar
  30. Huang, C.-C., Tseng, T.-L. B., Jiang, F., Fan, Y.-N., & Hsu, C.-H. (2014). Rough set theory: A novel approach for extraction of robust decision rules based on incremental attributes. Annals of Operations Research, 216(1), 163–189.Google Scholar
  31. Iqbal Ali, A., & Lerme, C. S. (1997). Comparative advantage and disadvantage in DEA. Annals of Operations Research, 73, 215–232.Google Scholar
  32. Kantor, J., & Maital, S. (1999). Measuring efficiency by product group: Integrating DEA with activity-based accounting in a large mideast bank. Interfaces, 29(3), 27–36.Google Scholar
  33. Kao, C. (2016). Efficiency decomposition and aggregation in network data envelopment analysis. European Journal of Operational Research, 255(3), 778–786.Google Scholar
  34. Khorana, A., Servaes, H., & Tufano, P. (2005). Explaining the size of the mutual fund industry around the world. Journal of Financial Economics, 78(1), 145–185.Google Scholar
  35. Klopp, G. (1985). The analysis of the efficiency of productive systems with multiple inputs and outputs. Ph.D. thesis, University of Illinois at Chicago.Google Scholar
  36. Li, Y., Liao, X., & Zhao, W. (2009). A rough set approach to knowledge discovery in analyzing competitive advantages of firms. Annals of Operations Research, 168(1), 205–223.Google Scholar
  37. Liu, S.-T. (2011). Performance measurement of Taiwan financial holding companies: An additive efficiency decomposition approach. Expert Systems with Applications, 38(5), 5674–5679.Google Scholar
  38. Liu, J. S., & Lu, W.-M. (2010). DEA and ranking with the network-based approach: A case of R&D performance. Omega, 38(6), 453–464.Google Scholar
  39. Liu, J. S., & Lu, W.-M. (2012). Network-based method for ranking of efficient units in two-stage DEA models. Journal of the Operational Research Society, 63(8), 1153–1164.Google Scholar
  40. Liu, J. S., Lu, W.-M., & Ho, M. H.-C. (2014). National characteristics: Innovation systems from the process efficiency perspective. R&D Management, 45(4), 317–338.Google Scholar
  41. Liu, J. S., Lu, L. Y., & Lu, W.-M. (2016). Research fronts in data envelopment analysis. Omega, 58, 33–45.Google Scholar
  42. Liu, J. S., Lu, L. Y., Lu, W.-M., & Lin, B. J. (2013a). Data envelopment analysis 1978–2010: A citation-based literature survey. Omega, 41(1), 3–15.Google Scholar
  43. Liu, J. S., Lu, L. Y., Lu, W.-M., & Lin, B. J. (2013b). A survey of DEA applications. Omega, 41(5), 893–902.Google Scholar
  44. Liu, J. S., Lu, W.-M., Yang, C., & Chuang, M. (2009). A network-based approach for increasing discrimination in data envelopment analysis. Journal of the Operational Research Society, 60(11), 1502–1510.Google Scholar
  45. Lu, W.-M., Liu, J. S., Kweh, Q. L., & Wang, C.-W. (2016). Exploring the benchmarks of the Taiwanese investment trust corporations: Management and investment efficiency perspectives. European Journal of Operational Research, 248(2), 607–618.Google Scholar
  46. Lu, W.-M., Wang, W.-K., Hung, S.-W., & Lu, E.-T. (2012). The effects of corporate governance on airline performance: Production and marketing efficiency perspectives. Transportation Research Part E: Logistics and Transportation Review, 48(2), 529–544.Google Scholar
  47. Luo, X. (2003). Evaluating the profitability and marketability efficiency of large banks: An application of data envelopment analysis. Journal of Business Research, 56(8), 627–635.Google Scholar
  48. Murthi, B., Choi, Y. K., & Desai, P. (1997). Efficiency of mutual funds and portfolio performance measurement: A non-parametric approach. European Journal of Operational Research, 98(2), 408–418.Google Scholar
  49. Paradi, J. C., & Schaffnit, C. (2004). Commercial branch performance evaluation and results communication in a Canadian bank—A DEA application. European Journal of Operational Research, 156(3), 719–735.Google Scholar
  50. Pawlak, Z. (1982). Rough sets. International Journal of Parallel Programming, 11(5), 341–356.Google Scholar
  51. Pawlak, Z. (2002). Rough sets, decision algorithms and Bayes’ theorem. European Journal of Operational Research, 136(1), 181–189.Google Scholar
  52. Pawlak, Z. (2012). Rough sets: Theoretical aspects of reasoning about data. New York: Springer Science & Business Media.Google Scholar
  53. Pawlak, Z., Grzymala-Busse, J., Slowinski, R., & Ziarko, W. (1995). Rough sets. Communications of the ACM, 38(11), 88–95.Google Scholar
  54. Premachandra, I., Zhu, J., Watson, J., & Galagedera, D. U. (2012). Best-performing US mutual fund families from 1993 to 2008: Evidence from a novel two-stage DEA model for efficiency decomposition. Journal of Banking and Finance, 36(12), 3302–3317.Google Scholar
  55. Ray, S. C., & Das, A. (2010). Distribution of cost and profit efficiency: Evidence from Indian banking. European Journal of Operational Research, 201(1), 297–307.Google Scholar
  56. Seiford, L. M. (1997). A bibliography for data envelopment analysis (1978–1996). Annals of Operations Research, 73, 393–438.Google Scholar
  57. Seiford, L. M., & Zhu, J. (1999). Profitability and marketability of the top 55 US commercial banks. Management Science, 45(9), 1270–1288.Google Scholar
  58. Sherman, H. D., & Gold, F. (1985). Bank branch operating efficiency: Evaluation with data envelopment analysis. Journal of Banking and Finance, 9(2), 297–315.Google Scholar
  59. Shu, P.-G., Yeh, Y.-H., & Yamada, T. (2002). The behavior of Taiwan mutual fund investors—performance and fund flows. Pacific-basin finance journal, 10(5), 583–600.Google Scholar
  60. Sueyoshi, T., Shang, J., & Chiang, W.-C. (2009). A decision support framework for internal audit prioritization in a rental car company: A combined use between DEA and AHP. European Journal of Operational Research, 199(1), 219–231.Google Scholar
  61. Tone, K. (2001). A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research, 130(3), 498–509.Google Scholar
  62. Tone, K., & Tsutsui, M. (2009). Network DEA: A slacks-based measure approach. European Journal of Operational Research, 197(1), 243–252.Google Scholar
  63. Tone, K., & Tsutsui, M. (2014). Dynamic DEA with network structure: A slacks-based measure approach. Omega, 42(1), 124–131.Google Scholar
  64. Vaz, C. B., Camanho, A., & Guimarães, R. (2010). The assessment of retailing efficiency using network data envelopment analysis. Annals of Operations Research, 173(1), 5–24.Google Scholar
  65. Walczak, B., & Massart, D. (1999). Rough sets theory. Chemometrics and Intelligent Laboratory Systems, 47(1), 1–16.Google Scholar
  66. Wang, K., Huang, W., Wu, J., & Liu, Y.-N. (2014). Efficiency measures of the Chinese commercial banking system using an additive two-stage DEA. Omega, 44, 5–20.Google Scholar
  67. Wen, M. (2015). Uncertain data envelopment analysis. Berlin: Springer.Google Scholar
  68. Xu, J., Li, B., & Wu, D. (2009). Rough data envelopment analysis and its application to supply chain performance evaluation. International Journal of Production Economics, 122(2), 628–638.Google Scholar
  69. Zhou, Z., Xiao, H., Jin, Q., & Liu, W. (2017). DEA frontier improvement and portfolio rebalancing: An application of China mutual funds on considering sustainability information disclosure. European Journal of Operational Research, 269(1), 111–131.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Financial ManagementNational Defense UniversityBeitouTaiwan
  2. 2.Benchmarking Research Group, Faculty of AccountingTon Duc Thang UniversityHo Chi Minh CityVietnam

Personalised recommendations