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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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    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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