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Practical Improvements to Mean-Variance Optimization for Multi-Asset Class Portfolios

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  • Marin Lolic

    (Independent Researcher, Baltimore, MD 21210, USA)

Abstract

In the more than 70 years since Markowitz introduced mean-variance optimization for portfolio construction, academics and practitioners have documented numerous weaknesses in the approach. In this paper, we propose two easily understandable improvements to mean-variance optimization in the context of multi-asset class portfolios, each of which provides less extreme and more stable portfolio weights. The first method sacrifices a small amount of expected optimality for reduced weight concentration, while the second method randomly resamples the available assets. Additionally, we develop a process for testing the performance of portfolio construction approaches on simulated data assuming variable degrees of forecasting skill. Finally, we show that the improved methods achieve better out-of-sample risk-adjusted returns than standard mean-variance optimization for realistic investor skill levels.

Suggested Citation

  • Marin Lolic, 2024. "Practical Improvements to Mean-Variance Optimization for Multi-Asset Class Portfolios," JRFM, MDPI, vol. 17(5), pages 1-11, April.
    • Handle: RePEc:gam:jjrfmx:v:17:y:2024:i:5:p:183-:d:1385628
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    References listed on IDEAS

    as
    1. Veronica Rappoport & Enrichetta Ravina & Daniel Paravisini, 2010. "Risk Aversion and Wealth: Evidence from Person-to-Person Lending Portfolios," 2010 Meeting Papers 664, Society for Economic Dynamics.
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    4. Gah-Yi Ban & Noureddine El Karoui & Andrew E. B. Lim, 2018. "Machine Learning and Portfolio Optimization," Management Science, INFORMS, vol. 64(3), pages 1136-1154, March.
    5. Jorion, Philippe, 1986. "Bayes-Stein Estimation for Portfolio Analysis," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 21(3), pages 279-292, September.
    Full references (including those not matched with items on IDEAS)

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