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Are Smart Beta strategies suitable for hedge fund portfolios?

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  • Hitaj, Asmerilda
  • Zambruno, Giovanni

Abstract

In the equity context different Smart Beta strategies (such as the equally weighted, global minimum variance, equal risk contribution and maximum diversified ratio) have been proposed as alternatives to the cap-weighted index. These new approaches have attracted the attention of equity managers as different empirical analyses demonstrate the superiority of these strategies with respect to cap-weighted and to strategies that consider only mean and variance. In this paper we focus our attention to hedge fund index portfolios and analyze if the results reported in the equity framework are still valid. We consider hedge fund index and equity portfolios, the approaches used for portfolio selection are the four ‘Smart Beta’ strategies, mean–variance and mean–variance–skewness. In the two latter approaches the Taylor approximation of a CARA expected utility function and the Polynomial Goal Programing (PGP) have been used. The obtained portfolios are analyzed in the in-sample as well as in the out-of-sample perspectives.

Suggested Citation

  • Hitaj, Asmerilda & Zambruno, Giovanni, 2016. "Are Smart Beta strategies suitable for hedge fund portfolios?," Review of Financial Economics, Elsevier, vol. 29(C), pages 37-51.
  • Handle: RePEc:eee:revfin:v:29:y:2016:i:c:p:37-51
    DOI: 10.1016/j.rfe.2016.03.001
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    1. Gustavo Athayde & Renato G. Flores, 2002. "The Portfolio Frontier with Higher Moments: The Undiscovered Country," Computing in Economics and Finance 2002 209, Society for Computational Economics.
    2. Victor DeMiguel & Lorenzo Garlappi & Raman Uppal, 2009. "Optimal Versus Naive Diversification: How Inefficient is the 1-N Portfolio Strategy?," The Review of Financial Studies, Society for Financial Studies, vol. 22(5), pages 1915-1953, May.
    3. repec:dau:papers:123456789/4688 is not listed on IDEAS
    4. Ledoit, Oliver & Wolf, Michael, 2008. "Robust performance hypothesis testing with the Sharpe ratio," Journal of Empirical Finance, Elsevier, vol. 15(5), pages 850-859, December.
    5. Lionel Martellini & Volker Ziemann, 2010. "Improved Estimates of Higher-Order Comoments and Implications for Portfolio Selection," The Review of Financial Studies, Society for Financial Studies, vol. 23(4), pages 1467-1502, April.
    6. M. J. Brennan, 1998. "The Role of Learning in Dynamic Portfolio Decisions," Review of Finance, European Finance Association, vol. 1(3), pages 295-306.
    7. Ledoit, Olivier & Wolf, Michael, 2003. "Improved estimation of the covariance matrix of stock returns with an application to portfolio selection," Journal of Empirical Finance, Elsevier, vol. 10(5), pages 603-621, December.
    8. Jorion, Philippe, 1986. "Bayes-Stein Estimation for Portfolio Analysis," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 21(3), pages 279-292, September.
    9. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    10. Asmerilda Hitaj & Lorenzo Mercuri, 2013. "Hedge Fund Portfolio Allocation with Higher Moments and MVG Models," Palgrave Macmillan Books, in: Jonathan A. Batten & Peter MacKay & Niklas Wagner (ed.), Advances in Financial Risk Management, chapter 14, pages 331-346, Palgrave Macmillan.
    11. Asmerilda Hitaj & Lorenzo Mercuri, 2013. "Portfolio allocation using multivariate variance gamma models," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 27(1), pages 65-99, March.
    12. Elton, Edwin J & Gruber, Martin J, 1973. "Estimating the Dependence Structure of Share Prices-Implications for Portfolio Selection," Journal of Finance, American Finance Association, vol. 28(5), pages 1203-1232, December.
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    Cited by:

    1. Zeynep Cipiloglu Yildiz & Selim Baha Yildiz, 2022. "A portfolio construction framework using LSTM‐based stock markets forecasting," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 2356-2366, April.
    2. Asmerilda Hitaj & Lorenzo Mercuri & Edit Rroji, 2019. "Sensitivity analysis of Mixed Tempered Stable parameters with implications in portfolio optimization," Computational Management Science, Springer, vol. 16(1), pages 71-95, February.
    3. Massimiliano Kaucic & Filippo Piccotto & Gabriele Sbaiz, 2024. "A constrained swarm optimization algorithm for large-scale long-run investments using Sharpe ratio-based performance measures," Computational Management Science, Springer, vol. 21(1), pages 1-29, June.
    4. Gian Paolo Clemente & Rosanna Grassi & Asmerilda Hitaj, 2022. "Smart network based portfolios," Annals of Operations Research, Springer, vol. 316(2), pages 1519-1541, September.
    5. Gian Paolo Clemente & Rosanna Grassi & Asmerilda Hitaj, 2018. "Asset allocation: new evidence through network approaches," Papers 1810.09825, arXiv.org.
    6. Gian Paolo Clemente & Rosanna Grassi & Asmerilda Hitaj, 2019. "Smart network based portfolios," Papers 1907.01274, arXiv.org.
    7. Fabio Vanni & Asmerilda Hitaj & Elisa Mastrogiacomo, 2024. "Enhancing Portfolio Allocation: A Random Matrix Theory Perspective," Mathematics, MDPI, vol. 12(9), pages 1-16, May.
    8. Giorgio Consigli & Asmerilda Hitaj & Elisa Mastrogiacomo, 2019. "Portfolio choice under cumulative prospect theory: sensitivity analysis and an empirical study," Computational Management Science, Springer, vol. 16(1), pages 129-154, February.
    9. Gian Paolo Clemente & Rosanna Grassi & Asmerilda Hitaj, 2021. "Asset allocation: new evidence through network approaches," Annals of Operations Research, Springer, vol. 299(1), pages 61-80, April.

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