Problems with Mean Variance Optimization

Whenever there is a major bear market, investors begin to question everything. In particular, following the Financial Crisis of 2007–2008, many families decided that mean variance optimization (MVO), the main tool for designing investment portfolios for decades, simply didn't work.

Certainly MVO was overhyped by advisors, who should have been using MVO techniques simply as “what if” modeling exercises, not to design final, real-world portfolios. But now that the crisis is several years behind us and the markets have largely recovered, let's take a calmer look at the problems presented by MVO.

Computational Power

This process is known as mean variance optimization, or MVO, and so far, so good. But there are serious problems here. The first problem is that there are many, many possible asset combinations to be looked at. If, for example, we wish to consider including 10 asset classes in our portfolios, our optimizer will have to search through almost 33 million possible portfolios even before it begins to think about changing the percentages of each asset.6 Even as recently as the early 1970s it required a computer the size of a large room, two days of computational time, and tens of thousands of dollars to run one mean variance optimization. In other words, the delay in adopting Markowitz's ideas was not entirely due to intransigence on the part of real-world investors.

Oddly, however, the unavailability of cheap, massive computational power ...

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