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Simple and cross efficiency of CTAs using data envelopment analysis

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  • Greg Gregoriou
  • Fabrice Rouah
  • Stephen Satchell
  • Fernando Diz
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    Abstract

    Data envelopment analysis (DEA) is applied, and basic and cross-efficiency models are used to evaluate the performance of CTA classifications. With the ever-increasing number of CTAs, there is an urgent requirement to provide money managers, pension funds, and high-net-worth individuals with a trustworthy appraisal method in ranking their efficiency. DEA can achieve this, and one important benefit of this measure is that benchmarks are not required, thereby alleviating the problem of using traditional benchmarks to examine non-normal returns. This article aims to investigate CTAs and to identify the ones that have achieved superior performance or, in other words, have an efficiency score of 100 in a risk/return setting.

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    Bibliographic Info

    Article provided by Taylor & Francis Journals in its journal The European Journal of Finance.

    Volume (Year): 11 (2005)
    Issue (Month): 5 ()
    Pages: 393-409

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    Handle: RePEc:taf:eurjfi:v:11:y:2005:i:5:p:393-409

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    Related research

    Keywords: Data envelopment analysis; commodity trading advisors; efficiency; benchmark;

    References

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    1. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, INFORMS, vol. 30(9), pages 1078-1092, September.
    2. A. Charnes & W. W. Cooper & E. Rhodes, 1981. "Evaluating Program and Managerial Efficiency: An Application of Data Envelopment Analysis to Program Follow Through," Management Science, INFORMS, INFORMS, vol. 27(6), pages 668-697, June.
    3. 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.
    4. Basso, Antonella & Funari, Stefania, 2001. "A data envelopment analysis approach to measure the mutual fund performance," European Journal of Operational Research, Elsevier, Elsevier, vol. 135(3), pages 477-492, December.
    5. Richard S. BARR & Lawrence M. SEIFORD & Thomas F. SIEMS, 1994. "Forecasting Bank Failure : A Non-Parametric Frontier Estimation Approach," Discussion Papers (REL - Recherches Economiques de Louvain) 1994041, Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES).
    6. Muhittin Oral & Ossama Kettani & Pascal Lang, 1991. "A Methodology for Collective Evaluation and Selection of Industrial R&D Projects," Management Science, INFORMS, INFORMS, vol. 37(7), pages 871-885, July.
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    Cited by:
    1. Jacques Pézier, 2011. "Rationalization of Investment Preference Criteria," ICMA Centre Discussion Papers in Finance, Henley Business School, Reading University icma-dp2011-12, Henley Business School, Reading University.
    2. Lamb, John D. & Tee, Kai-Hong, 2012. "Data envelopment analysis models of investment funds," European Journal of Operational Research, Elsevier, Elsevier, vol. 216(3), pages 687-696.
    3. Glawischnig, Markus & Sommersguter-Reichmann, Margit, 2010. "Assessing the performance of alternative investments using non-parametric efficiency measurement approaches: Is it convincing?," Journal of Banking & Finance, Elsevier, vol. 34(2), pages 295-303, February.

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