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Limiting out-of-sample performance of optimal unconstrained portfolios

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  • Chavez-Bedoya, Luis
  • Birge, John R.

Abstract

This paper studies the out-of-sample Sharpe ratio of an unconstrained portfolio that combines the global minimum-variance with a hedge portfolio. Furthermore, we investigate how this ratio behaves as the number of risky assets and observations approaches infinity while maintaining a constant ratio. Under these conditions, it becomes possible to simultaneously account for estimation risk and achieve analytical tractability when optimizing the out-of-sample Sharpe ratio. This analysis also provides valuable insights to enhance out-of-sample performance in the finite case by introducing additional deterministic factors to the portfolio components.

Suggested Citation

  • Chavez-Bedoya, Luis & Birge, John R., 2024. "Limiting out-of-sample performance of optimal unconstrained portfolios," Finance Research Letters, Elsevier, vol. 67(PB).
    • Handle: RePEc:eee:finlet:v:67:y:2024:i:pb:s1544612324009164
    DOI: 10.1016/j.frl.2024.105886
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    References listed on IDEAS

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    6. Bryan T. Kelly & Semyon Malamud & Kangying Zhou, 2021. "The Virtue of Complexity in Machine Learning Portfolios," Swiss Finance Institute Research Paper Series 21-90, Swiss Finance Institute.
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    More about this item

    Keywords

    Portfolio optimization; Estimation risk; Out-of-sample Sharpe ratio; Global minimum-variance portfolio; Hedge portfolio;
    All these keywords.

    JEL classification:

    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors

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