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

Marin Lolic ()
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Marin Lolic: Independent Researcher, Baltimore, MD 21210, USA

JRFM, 2024, vol. 17, issue 5, 1-11

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.

Keywords: portfolio optimization; mean-variance optimization; portfolio theory; asset allocation (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2024
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