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Resampling DEA estimates of investment fund performance

  • Lamb, John D.
  • Tee, Kai-Hong
Registered author(s):

    Data envelopment analysis (DEA) is attractive for comparing investment funds because it handles different characteristics of fund distribution and gives a way to rank funds. There is substantial literature applying DEA to funds, based on the time series of funds’ returns. This article looks at the issue of uncertainty in the resulting DEA efficiency estimates, investigating consistency and bias. It uses the bootstrap to develop stochastic DEA models for funds, derive confidence intervals and develop techniques to compare and rank funds and represent the ranking. It investigates how to deal with autocorrelation in the time series and considers models that deal with correlation in the funds’ returns.

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    File URL: http://www.sciencedirect.com/science/article/pii/S0377221712005371
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    Article provided by Elsevier in its journal European Journal of Operational Research.

    Volume (Year): 223 (2012)
    Issue (Month): 3 ()
    Pages: 834-841

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    Handle: RePEc:eee:ejores:v:223:y:2012:i:3:p:834-841
    Contact details of provider: Web page: http://www.elsevier.com/locate/eor

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    1. Briec, Walter & Kerstens, Kristiaan, 2009. "Multi-horizon Markowitz portfolio performance appraisals: A general approach," Omega, Elsevier, vol. 37(1), pages 50-62, February.
    2. Mickael Lothgren & Magnus Tambour, 1999. "Testing scale efficiency in DEA models: a bootstrapping approach," Applied Economics, Taylor & Francis Journals, vol. 31(10), pages 1231-1237.
    3. Mickael Lothgren & Magnus Tambour, 1999. "Bootstrapping the data envelopment analysis Malmquist productivity index," Applied Economics, Taylor & Francis Journals, vol. 31(4), pages 417-425.
    4. SIMAR, Léopold & WILSON, Paul, 1995. "Sensitivity Analysis to Efficiency Scores : How to Bootstrap in Nonparametric Frontier Models," CORE Discussion Papers 1995043, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    5. Lozano, Sebastián & Gutiérrez, Ester, 2008. "Data envelopment analysis of mutual funds based on second-order stochastic dominance," European Journal of Operational Research, Elsevier, vol. 189(1), pages 230-244, August.
    6. Atsushi Inoue & Lutz Kilian, 2002. "Bootstrapping Smooth Functions of Slope Parameters and Innovation Variances in VAR (∞) Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 43(2), pages 309-332, May.
    7. Murthi, B. P. S. & Choi, Yoon K. & Desai, Preyas, 1997. "Efficiency of mutual funds and portfolio performance measurement: A non-parametric approach," European Journal of Operational Research, Elsevier, vol. 98(2), pages 408-418, April.
    8. Joro, Tarja & Na, Paul, 2006. "Portfolio performance evaluation in a mean-variance-skewness framework," European Journal of Operational Research, Elsevier, vol. 175(1), pages 446-461, November.
    9. Philippe Artzner & Freddy Delbaen & Jean-Marc Eber & David Heath, 1999. "Coherent Measures of Risk," Mathematical Finance, Wiley Blackwell, vol. 9(3), pages 203-228.
    10. Rajiv D. Banker, 1993. "Maximum Likelihood, Consistency and Data Envelopment Analysis: A Statistical Foundation," Management Science, INFORMS, vol. 39(10), pages 1265-1273, October.
    11. X. M. Gonzalez & D. Miles, 2002. "Statistical precision of DEA: a bootstrap application to Spanish public services," Applied Economics Letters, Taylor & Francis Journals, vol. 9(2), pages 127-132.
    12. W. Ogryczak & A. Ruszczynski, 1997. "From Stochastic Dominance to Mean-Risk Models: Semideviations as Risk Measures," Working Papers ir97027, International Institute for Applied Systems Analysis.
    13. Walter Briec & Kristiaan Kerstens, 2009. "Portfolio Selection in Multidimensional General and Partial Moment Space," Working Papers 2009-ECO-08, IESEG School of Management.
    14. Kao, Chiang & Liu, Shiang-Tai, 2009. "Stochastic data envelopment analysis in measuring the efficiency of Taiwan commercial banks," European Journal of Operational Research, Elsevier, vol. 196(1), pages 312-322, July.
    15. Lamb, John D. & Tee, Kai-Hong, 2012. "Data envelopment analysis models of investment funds," European Journal of Operational Research, Elsevier, vol. 216(3), pages 687-696.
    16. Rockafellar, R. Tyrrell & Uryasev, Stanislav, 2002. "Conditional value-at-risk for general loss distributions," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1443-1471, July.
    17. Morey, Matthew R. & Morey, Richard C., 1999. "Mutual fund performance appraisals: a multi-horizon perspective with endogenous benchmarking," Omega, Elsevier, vol. 27(2), pages 241-258, April.
    18. Kosowski, Robert & Naik, Narayan Y. & Teo, Melvyn, 2007. "Do hedge funds deliver alpha? A Bayesian and bootstrap analysis," Journal of Financial Economics, Elsevier, vol. 84(1), pages 229-264, April.
    19. Cuthbertson, Keith & Nitzsche, Dirk & O'Sullivan, Niall, 2008. "UK mutual fund performance: Skill or luck?," Journal of Empirical Finance, Elsevier, vol. 15(4), pages 613-634, September.
    20. G Souza & M Souza & E Gomes, 2011. "Computing confidence intervals for output-oriented DEA models: an application to agricultural research in Brazil," Journal of the Operational Research Society, Palgrave Macmillan, vol. 62(10), pages 1844-1850, October.
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