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The Art of Investing in Hedge Funds: Fund Selection and Optimal Allocations

Author

Listed:
  • Carol Alexander

    (ICMA Centre, University of Reading)

  • Anca Dimitriu

    (ICMA Centre, University of Reading)

Abstract

With institutional investors increasingly involved in alternative investments, portfolio optimisation within a large universe of hedge funds has become a key area for research. This paper develops a portfolio construction model that is specifically designed for funds of hedge funds, incorporating specific controls for operational limitations, data biases and incompleteness. Absolute performance is targeted by selecting funds according to their relative abnormal return, alpha. Whilst different factor models provide quite different estimates of a hedge fund's alpha, we find that ranking funds according to their alpha is an efficient selection process. In an extensive out-of-sample historical analysis, funds of funds that are selected in this way and then allocated using constrained minimum variance optimisation are shown to perform much better than the equally weighted portfolio of all funds, or minimum variance portfolios of randomly selected funds. This is true even when hedge funds are selected according to their alphas produced by the simplest factor model. Of the four factor models considered in this analysis the best out-of-sample performance is obtained using the statistical factor model.

Suggested Citation

  • Carol Alexander & Anca Dimitriu, 2004. "The Art of Investing in Hedge Funds: Fund Selection and Optimal Allocations," ICMA Centre Discussion Papers in Finance icma-dp2004-01, Henley Business School, University of Reading.
    • Handle: RePEc:rdg:icmadp:icma-dp2004-01
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    File URL: http://www.icmacentre.ac.uk/pdf/discussion/DP2004-02.pdf
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    Citations

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    Cited by:

    1. Panopoulou, Ekaterini & Vrontos, Spyridon, 2015. "Hedge fund return predictability; To combine forecasts or combine information?," Journal of Banking & Finance, Elsevier, vol. 56(C), pages 103-122.
    2. Jawadi, Fredj & Khanniche, Sabrina, 2012. "Modeling hedge fund exposure to risk factors," Economic Modelling, Elsevier, vol. 29(4), pages 1003-1018.
    3. Juliane Proelss & Denis Schweizer, 2014. "Polynomial goal programming and the implicit higher moment preferences of US institutional investors in hedge funds," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 28(1), pages 1-28, February.
    4. Vrontos, Spyridon D. & Vrontos, Ioannis D. & Giamouridis, Daniel, 2008. "Hedge fund pricing and model uncertainty," Journal of Banking & Finance, Elsevier, vol. 32(5), pages 741-753, May.
    5. Li, Sheng & Linton, Oliver, 2007. "Evaluating hedge fund performance: a stochastic dominance approach," LSE Research Online Documents on Economics 24486, London School of Economics and Political Science, LSE Library.
    6. Lee, Tae Kyun & Sohn, So Young, 2023. "Alpha-factor integrated risk parity portfolio strategy in global equity fund of funds," International Review of Financial Analysis, Elsevier, vol. 88(C).
    7. Giamouridis, Daniel & Vrontos, Ioannis D., 2007. "Hedge fund portfolio construction: A comparison of static and dynamic approaches," Journal of Banking & Finance, Elsevier, vol. 31(1), pages 199-217, January.
    8. Spyridon D Vrontos & Ioannis D Vrontos & Loukia Meligkotsidou, 2013. "Asset-liability management for pension funds in a time-varying volatility environment," Journal of Asset Management, Palgrave Macmillan, vol. 14(5), pages 306-333, October.
    9. El Kalak, Izidin & Azevedo, Alcino & Hudson, Robert, 2016. "Reviewing the hedge funds literature I: Hedge funds and hedge funds' managerial characteristics," International Review of Financial Analysis, Elsevier, vol. 48(C), pages 85-97.
    10. Harris, Richard D.F. & Mazibas, Murat, 2010. "Dynamic hedge fund portfolio construction," International Review of Financial Analysis, Elsevier, vol. 19(5), pages 351-357, December.

    More about this item

    Keywords

    Hedge fund; risk adjusted performance; mean-variance; constrained optimisation;
    All these keywords.

    JEL classification:

    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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