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Optimality of Naive Investment Strategies in Dynamic MeanVariance Optimization Problems with Multiple Priors

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  • Yuki Shigeta

Abstract

We study dynamic mean-variance optimization problems with multiple priors. We introduce two types of multiple priors, the priors for expected returns and the priors for covariances. Our framework suggests that the global minimumvariance portfolio is optimal when the investor strongly doubts the correctness of the estimated expected returns, and the equally weighted portfolio is optimal when the investor strongly doubts the correctness of the estimated covariances. From the back tests, we find that for some data sets, the strategy that invests in the global minimum-variance portfolio or the equally weighted portfolio considering the market condition is more efficient than the other mean-variance efficient portfolios.

Suggested Citation

  • Yuki Shigeta, 2016. "Optimality of Naive Investment Strategies in Dynamic MeanVariance Optimization Problems with Multiple Priors," Discussion papers e-16-004, Graduate School of Economics , Kyoto University.
    • Handle: RePEc:kue:epaper:e-16-004
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    Cited by:

    1. Platanakis, Emmanouil & Sutcliffe, Charles & Ye, Xiaoxia, 2021. "Horses for courses: Mean-variance for asset allocation and 1/N for stock selection," European Journal of Operational Research, Elsevier, vol. 288(1), pages 302-317.

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    More about this item

    Keywords

    Robust mean-variance optimization; dynamic portfolio selections; naive diversification; global minimum-variance portfolio; mean-variance efficiency.;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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