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Large scale mean-variance strategies in the U.S. stock market

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  • Pezzo, Luca
  • Wang, Lei
  • Zirek, Duygu

Abstract

We provide an extensive analysis of the profitability of large-scale Mean-Variance (MV) strategies in the US stock market. Implementing MV strategies has never been so rewarding as recently. MV strategies work best in periods where their parameters are more accurately estimated, making strategies more stable and able to adapt to changes in the investment opportunity set. Minimizing over costs is better than going for the classical approach, especially for strategies that target higher returns. This is because cost optimization puts a stabilizing economic bound on the weights, lowering downside risk and enabling better scaling, while driving execution costs toward zero.

Suggested Citation

  • Pezzo, Luca & Wang, Lei & Zirek, Duygu, 2023. "Large scale mean-variance strategies in the U.S. stock market," Research in International Business and Finance, Elsevier, vol. 66(C).
    • Handle: RePEc:eee:riibaf:v:66:y:2023:i:c:s0275531923001885
    DOI: 10.1016/j.ribaf.2023.102062
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    References listed on IDEAS

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    1. 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.
    2. Olivier Ledoit & Michael Wolf, 2017. "Nonlinear Shrinkage of the Covariance Matrix for Portfolio Selection: Markowitz Meets Goldilocks," The Review of Financial Studies, Society for Financial Studies, vol. 30(12), pages 4349-4388.
    3. Chordia, Tarun & Subrahmanyam, Avanidhar & Tong, Qing, 2014. "Have capital market anomalies attenuated in the recent era of high liquidity and trading activity?," Journal of Accounting and Economics, Elsevier, vol. 58(1), pages 41-58.
    4. Robert Novy-Marx & Mihail Velikov, 2016. "A Taxonomy of Anomalies and Their Trading Costs," The Review of Financial Studies, Society for Financial Studies, vol. 29(1), pages 104-147.
    5. James J. Angel & Lawrence E. Harris & Chester S. Spatt, 2015. "Equity Trading in the 21st Century: An Update," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 5(01), pages 1-39.
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    Cited by:

    1. Filippou, Ilias & Maurer, Thomas A. & Pezzo, Luca & Taylor, Mark P., 2024. "Importance of transaction costs for asset allocation in foreign exchange markets," Journal of Financial Economics, Elsevier, vol. 159(C).

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    More about this item

    Keywords

    Mean-Variance; Market-timing; Estimation error; Transaction costs; Profitability;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D23 - Microeconomics - - Production and Organizations - - - Organizational Behavior; Transaction Costs; Property Rights
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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