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Markowitz versus Michaud: portfolio optimization strategies reconsidered

Author

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  • Franziska Becker
  • Marc Gürtler
  • Martin Hibbeln

Abstract

Several attempts have been made to reduce the impact of estimation errors on the optimal portfolio composition. On the one hand, improved estimators of the necessary moments have been developed, and on the other hand, heuristic methods have been generated to enhance the portfolio performance, for instance, the 'resampled efficiency' of Michaud [1998. Efficient Asset Management: A Practical Guide to Stock Portfolio Optimization and Asset Allocation. Boston: Harvard Business School Press]. We compare the out-of-sample performance of traditional mean-variance optimization by Markowitz [1952. "Portfolio Selection." Journal of Finance 7 (1): 77-91] with Michaud's resampled efficiency in a comprehensive simulation study for a large number of relevant estimators appearing in the literature. In addition, we perform an empirical study to confirm the simulation results. Within the framework of the analyses we consider different estimation periods as well as unconstrained and constrained portfolio optimization problems. The main findings are that Markowitz outperforms Michaud on average but the impact of different estimators and constraints is significantly larger. Precisely, in most situations, the estimator of Frost and Savarino [1988. "For Better Performance: Constrain Portfolio Weights." Journal of Portfolio Management 15 (1): 29-34] proves to work excellent. However, if the variance of estimators is large, for example, for short observation periods or large samples, it is recommendable to additionally implement constraints or to use the estimator of Ledoit and Wolf [2003. "Improved Estimation of the Covariance Matrix of Stock Returns with an Application to Portfolio Selection." Journal of Empirical Finance 10 (5): 603-622].

Suggested Citation

  • Franziska Becker & Marc Gürtler & Martin Hibbeln, 2015. "Markowitz versus Michaud: portfolio optimization strategies reconsidered," The European Journal of Finance, Taylor & Francis Journals, vol. 21(4), pages 269-291, March.
    • Handle: RePEc:taf:eurjfi:v:21:y:2015:i:4:p:269-291
    DOI: 10.1080/1351847X.2013.830138
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    Cited by:

    1. Alexandrino Tavares Barreto, 2018. "The Contribution of African Capital Markets in the Diversification of European Investment Portfolios," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 10(3), pages 1-29, March.
    2. I-Chen Lu & Kai-Hong Tee & Baibing Li, 2019. "Asset allocation with multiple analysts’ views: a robust approach," Journal of Asset Management, Palgrave Macmillan, vol. 20(3), pages 215-228, May.
    3. Philipp J. Kremer & Andreea Talmaciu & Sandra Paterlini, 2018. "Risk minimization in multi-factor portfolios: What is the best strategy?," Annals of Operations Research, Springer, vol. 266(1), pages 255-291, July.
    4. Paweł Sakowski & Anna Turovtseva, 2020. "Verification of Investment Opportunities on the Cryptocurrency Market within the Markowitz Framework," Working Papers 2020-41, Faculty of Economic Sciences, University of Warsaw.
    5. Paolella, Marc S. & Polak, Paweł & Walker, Patrick S., 2021. "A non-elliptical orthogonal GARCH model for portfolio selection under transaction costs," Journal of Banking & Finance, Elsevier, vol. 125(C).
    6. Oikonomou, Ioannis & Platanakis, Emmanouil & Sutcliffe, Charles, 2018. "Socially responsible investment portfolios: Does the optimization process matter?," The British Accounting Review, Elsevier, vol. 50(4), pages 379-401.
    7. László PáL, 2022. "Asset Allocation Strategies Using Covariance Matrix Estimators," Acta Universitatis Sapientiae, Economics and Business, Sciendo, vol. 10(1), pages 133-144, September.
    8. Anja Vinzelberg & Benjamin R. Auer, 2022. "Unprofitability of food market investments," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 43(7), pages 2887-2910, October.

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