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Improving the naive diversification: An enhanced indexation approach

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  • Li, Helong
  • Huang, Qin
  • Wu, Baiyi

Abstract

This paper employs enhanced indexation to derive an optimization model with an explicit objective to track and outperform the naive diversification (1/N) strategy. The proposed model is data-driven and can start from any number of historical return samples. Simulation shows that the number of samples needed for the new model to outperform the 1/N benchmark is much smaller than the number documented in existing literature for other models. Our out-of-sample tests show that the proposed enhanced indexation model with the 1/N strategy as benchmark can achieve higher expected returns and significantly higher Sharpe ratios in most of the test cases.

Suggested Citation

  • Li, Helong & Huang, Qin & Wu, Baiyi, 2021. "Improving the naive diversification: An enhanced indexation approach," Finance Research Letters, Elsevier, vol. 39(C).
    • Handle: RePEc:eee:finlet:v:39:y:2021:i:c:s1544612320302579
    DOI: 10.1016/j.frl.2020.101661
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    References listed on IDEAS

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    1. Filippi, C. & Guastaroba, G. & Speranza, M.G., 2016. "A heuristic framework for the bi-objective enhanced index tracking problem," Omega, Elsevier, vol. 65(C), pages 122-137.
    2. Richard H. Thaler & Shlomo Benartzi, 2001. "Naive Diversification Strategies in Defined Contribution Saving Plans," American Economic Review, American Economic Association, vol. 91(1), pages 79-98, March.
    3. 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.
    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. Hodges, Stewart D, 1976. "Problems in the application of portfolio selection models," Omega, Elsevier, vol. 4(6), pages 699-709.
    6. Eckhard Platen & Renata Rendek, 2012. "Approximating the numéraire portfolio by naive diversification," Journal of Asset Management, Palgrave Macmillan, vol. 13(1), pages 34-50, February.
    7. Saejoon Kim & Soong Kim, 2020. "Index tracking through deep latent representation learning," Quantitative Finance, Taylor & Francis Journals, vol. 20(4), pages 639-652, April.
    8. Beasley, J. E. & Meade, N. & Chang, T. -J., 2003. "An evolutionary heuristic for the index tracking problem," European Journal of Operational Research, Elsevier, vol. 148(3), pages 621-643, August.
    9. Victor DeMiguel & Lorenzo Garlappi & Francisco J. Nogales & Raman Uppal, 2009. "A Generalized Approach to Portfolio Optimization: Improving Performance by Constraining Portfolio Norms," Management Science, INFORMS, vol. 55(5), pages 798-812, May.
    10. N. Meade & J. E. Beasley, 2011. "Detection of momentum effects using an index out-performance strategy," Quantitative Finance, Taylor & Francis Journals, vol. 11(2), pages 313-326.
    11. Pflug, Georg Ch. & Pichler, Alois & Wozabal, David, 2012. "The 1/N investment strategy is optimal under high model ambiguity," Journal of Banking & Finance, Elsevier, vol. 36(2), pages 410-417.
    12. Gnägi, M. & Strub, O., 2020. "Tracking and outperforming large stock-market indices," Omega, Elsevier, vol. 90(C).
    13. Hiroshi Konno & Annista Wijayanayake, 2001. "Minimal Cost Index Tracking Under Nonlinear Transaction Costs And Minimal Transaction Unit Constraints," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 4(06), pages 939-957.
    14. Guastaroba, G. & Mansini, R. & Ogryczak, W. & Speranza, M.G., 2016. "Linear programming models based on Omega ratio for the Enhanced Index Tracking Problem," European Journal of Operational Research, Elsevier, vol. 251(3), pages 938-956.
    15. Huang, Jinbo & Li, Yong & Yao, Haixiang, 2018. "Index tracking model, downside risk and non-parametric kernel estimation," Journal of Economic Dynamics and Control, Elsevier, vol. 92(C), pages 103-128.
    16. Guastaroba, G. & Speranza, M.G., 2012. "Kernel Search: An application to the index tracking problem," European Journal of Operational Research, Elsevier, vol. 217(1), pages 54-68.
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    More about this item

    Keywords

    Enhanced indexation; Enhanced index tracking; Naive diversification; Portfolio management; Benchmark portfolio;
    All these keywords.

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General

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