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The Sharpe ratio of estimated efficient portfolios

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  • Kourtis, Apostolos

Abstract

Investors often adopt mean–variance efficient portfolios for achieving superior risk-adjusted returns. However, such portfolios are sensitive to estimation errors, which affect portfolio performance. To understand the impact of estimation errors, I develop simple and intuitive formulas of the squared Sharpe ratio that investors should expect from estimated efficient portfolios. The new formulas show that the expected squared Sharpe ratio is a function of the length of the available data, the number of assets and the maximum attainable Sharpe ratio. My results enable the portfolio manager to assess the value of efficient portfolios as investment vehicles, given the investment environment.

Suggested Citation

  • Kourtis, Apostolos, 2016. "The Sharpe ratio of estimated efficient portfolios," Finance Research Letters, Elsevier, vol. 17(C), pages 72-78.
    • Handle: RePEc:eee:finlet:v:17:y:2016:i:c:p:72-78
    DOI: 10.1016/j.frl.2016.01.009
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    1. Tu, Jun & Zhou, Guofu, 2011. "Markowitz meets Talmud: A combination of sophisticated and naive diversification strategies," Journal of Financial Economics, Elsevier, vol. 99(1), pages 204-215, January.
    2. Noël Amenc & Lionel Martellini & Mathieu Vaissié, 2003. "Benefits and risks of alternative investment strategies," Journal of Asset Management, Palgrave Macmillan, vol. 4(2), pages 96-118, August.
    3. Victor DeMiguel & Lorenzo Garlappi & Raman Uppal, 2009. "Optimal Versus Naive Diversification: How Inefficient is the 1-N Portfolio Strategy?," Review of Financial Studies, Society for Financial Studies, vol. 22(5), pages 1915-1953, May.
    4. Kan, Raymond & Zhou, Guofu, 2007. "Optimal Portfolio Choice with Parameter Uncertainty," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 42(3), pages 621-656, September.
    5. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    6. Paolella, Marc S., 2003. "Computing moments of ratios of quadratic forms in normal variables," Computational Statistics & Data Analysis, Elsevier, vol. 42(3), pages 313-331, March.
    7. Treynor, Jack L & Black, Fischer, 1973. "How to Use Security Analysis to Improve Portfolio Selection," The Journal of Business, University of Chicago Press, vol. 46(1), pages 66-86, January.
    8. Miller, Robert E. & Gehr, Adam K., 1978. "Sample Size Bias and Sharpe's Performance Measure: A Note," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 13(5), pages 943-946, December.
    9. Merton, Robert C., 1980. "On estimating the expected return on the market : An exploratory investigation," Journal of Financial Economics, Elsevier, vol. 8(4), pages 323-361, December.
    10. repec:dau:papers:123456789/1378 is not listed on IDEAS
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    Cited by:

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    2. Vukovic, Darko & Lapshina, Kseniya A. & Maiti, Moinak, 2019. "European Monetary Union bond market dynamics: Pre & post crisis," Research in International Business and Finance, Elsevier, vol. 50(C), pages 369-380.
    3. Blankenberg, Ann-Kathrin & Gottschalk, Jonas F. A., 2018. "Is socially responsible investing (SRI) in stocks a competitive capital investment? A comparative analysis based on the performance of sustainable stocks," University of Göttingen Working Papers in Economics 349, University of Goettingen, Department of Economics.
    4. Lu, Jin-Ray & Li, Xiu-Yan, 2021. "Identifying the fair value of Sharpe ratio by an option valuation approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 82(C), pages 63-70.
    5. José Claudio Isaias & Pedro Paulo Balestrassi & Guilherme Augusto Barucke Marcondes & Wesley Vieira da Silva & Carlos Henrique Pereira Mello & Claudimar Pereira da Veiga, 2021. "Project Portfolio Selection of Solar Energy by Photovoltaic Generation Using Gini-CAPM Multi-Criteria and Considering ROI Covariations," Energies, MDPI, vol. 14(24), pages 1-21, December.
    6. Lesly Lisset Ortiz-Cerezo & Alin Andrei Carsteanu & Julio Bernardo Clempner, 2022. "Sharpe-Ratio Portfolio in Controllable Markov Chains: Analytic and Algorithmic Approach for Second Order Cone Programming," Mathematics, MDPI, vol. 10(18), pages 1-13, September.

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    More about this item

    Keywords

    Portfolio performance; Mean–variance analysis; Estimation errors;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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