Research on Long-Term Portfolio Selection Model Based on DEA Cross-Efficiency Evaluation

  • Chengchao QiuEmail author
Conference paper


This thesis proposes a method to use Data Analysis Envelopment (DEA) for choosing a value stock which has a long-term advantage. This thesis suggests a new assurance region for a DEA model which is will prove that suitable for stock evaluation. The method is to use cross-efficiency DEA with new assurance region and exam its score and variance in several years for selecting stocks. It is a reasonable way to find a good stock for investment and focus to a durable, strong and good performance stock, not diversifying the portfolio. The Author will discuss in which input and output factors need to use in DEA model to have a result that is suitable to the purpose of long-term investment.


DEA RAM Cross-efficiency Assurance region constraints Portfolio selection Long-term investment 


  1. 1.
    A. Charnes, W.W. Cooper, E. Rhodes, Measuring the efficiency of decision making units. Eur. J. Oper. Res. 2(6), 429–444 (1978)CrossRefGoogle Scholar
  2. 2.
    J. Doyle, R. Green, Efficiency and cross-efficiency in Dea—derivations, meanings and uses. J. Oper. Res. Soc. 45(5), 567–578 (1994)CrossRefGoogle Scholar
  3. 3.
    P. Andersen, N.C. Petersen, A procedure for ranking efficient units in data envelopment analysis. Manage. Sci. 39(10), 1261–1265 (1993)CrossRefGoogle Scholar
  4. 4.
    A. Charnes, W.W. Cooper, Z.M. Huang et al., Polyhedral cone-ratio DEA models with an illustrative application to large commercial banks. J. Econ. 46(1–2), 73–91 (1990)CrossRefGoogle Scholar
  5. 5.
    R.G. Thompson, L.N. Langemeier, C. Lee et al., The role of multiplier bounds in efficiency analysis with application to Kansas farming. J. Econ. 46(1), 93–108 (1990)CrossRefGoogle Scholar
  6. 6.
    M. Oral, O. Kettani, P. Lang, A methodology for collective evaluation and selection of industrial research-and-development projects. Manag. Sci. 37(7), 871–885 (1991)CrossRefGoogle Scholar
  7. 7.
    B. Marcello, P. Alberto, A quality assurance-oriented methodology for handling trade-offs in supplier selection. Int. J. Phys. Distrib. & Logist. Manag. 30(2), 96–112 (2000)CrossRefGoogle Scholar
  8. 8.
    H.B.S.L. Jaehun Park, Multi-criteria ABC inventory classification using the cross-efficiency method in DEA. J. Korean Inst. Ind. Eng. 37(4), 358–366 (2011)Google Scholar
  9. 9.
    L. Liang, H. Wu, W.D. Cook et al., Alternative secondary goals in DEA cross-efficiency evaluation. Int. J. Prod. Econ. 113(2), 1025–1030 (2008)CrossRefGoogle Scholar
  10. 10.
    J. Alcaraz, N. Ramón, J.L. Ruiz et al., Ranking ranges in cross-efficiency evaluations. Eur. J. Oper. Res. 226(3), 516–521 (2013)CrossRefGoogle Scholar
  11. 11.
    F. Yang, S. Ang, Q. Xia et al., Ranking DMUs by using interval DEA cross efficiency matrix with acceptability analysis. Eur. J. Oper. Res. 223(2), 483–488 (2012)CrossRefGoogle Scholar
  12. 12.
    S. Lim, K.W. Oh, J. Zhu, Use of DEA cross-efficiency evaluation in portfolio selection: an application to Korean stock market. Eur. J. Oper. Res. 236(1), 361–368 (2014)CrossRefGoogle Scholar
  13. 13.
    M. Robaina-Alves, V. Moutinho, P. Macedo, A new frontier approach to model the eco-efficiency in European countries, J. Cleaner. prod. 103, 562–573 (2015)CrossRefGoogle Scholar
  14. 14.
    J.T. Pastor, J.L. Ruiz, Variables with negative values in Dea, in Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis, ed. by J. Zhu, W.D. Cook (Springer US, Boston, MA, 2007), pp. 63–84Google Scholar
  15. 15.
    W.W. Cooper, K.S. Park, J.T. Pastor, RAM: a range adjusted measure of inefficiency for use with additive models, and relations to other models and measures in DEA. J. Prod. Anal. 11(1), 5–42 (1999)CrossRefGoogle Scholar
  16. 16.
    R.D. Banker, A. Maindiratta, Nonparametric analysis of technical and allocative efficiencies in production. Econ. 56(6), 1315–1332 (1988)CrossRefGoogle Scholar
  17. 17.
    H. Scheel, Undesirable outputs in efficiency valuations. Eur. J. Oper. Res. 132(2), 400–410 (2001)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Institute for Interdisciplinary Research, Jianghan UniversityWuhanChina

Personalised recommendations