Harry Markowitz: An Equal-Weight Investor?

////Harry Markowitz: An Equal-Weight Investor?

Harry Markowitz: An Equal-Weight Investor?

By |2018-08-27T08:38:42+00:00October 17th, 2014|Research Insights, Tactical Asset Allocation Research|

Jason Zweig’s book, “Your Money and Your Brain” highlights an interesting conversation with Harry Markowitz. Dr. Markowitz is a Nobel Prize winner and his work on mean-variance-analysis laid the foundation for all of modern portfolio theory.

Not too shabby for a financial economist.

We’ll come back to the quote in a moment, but first let’s review some general observations on Markowitz’s mathematically sophisticated approach to asset allocation.

Although Markowitz did win a Nobel Prize, and this was partly based on his elegant mathematical solution to identifying mean-variance efficient portfolios, a funny thing happened when his ideas were applied in the real world: mean-variance performed poorly.

The fact  that a Nobel-Prize winning idea translated into a no-value-add-situation for investors is something to keep in mind when considering any optimization method for asset allocation.

The cautionary tale regarding mean-variance-based model performance heavily influenced the lecture I gave a few weeks ago at the Morningstar ETF conference where I presented the following slides.

My key takeaway from the chat was that COMPLEXITY DOES NOT EQUAL VALUE.

I supported this statement by highlighting that a variety of complex tactical asset allocation frameworks can’t stand toe-to-toe with the simple 1/n, or equal-weight asset allocation model.

Why Do Complex Models Fail?

Estimating the covariance matrix is notoriously unstable, so therefore, the “optimized” weights spit out from a model influenced by an unstable covariance matrix would also end up being unstable and unreliable. (For a detailed discussion of this issue, you can review the “Complexity” section in this post from about a month ago)

The proof is in the pudding: equal-weight allocations seem to reliably beat complicated allocations.

Not soon after the Morningstar event, one of my partners–Jack Vogel–ran across a quote by Harry Markowitz that was fairly amusing:

I should have computed the historical covariance of the asset classes and drawn an efficient frontier…I split my contributions 50/50 between bonds and equities.

In this context, Markowitz’s discussion is meant to highlight the power of behavior over reason. Markowitz pokes fun at himself: he knew he should have followed his own elegant model, but instead he ignored it. There’s an irony here: in light of a few more decades of out-of-sample evidence, it turns out his behaviorally-driven decision (i.e., equal-weight simplicity) probably really was the correct approach after all.

Your Money and Your Brain_ How the New Science of Neuroeconomics Can Help ... - _2014-10-09_22-24-46

So the founder of modern portfolio theory uses an equal-weight allocation. And one of the central assumptions underlying mean-variance optimization is that investors care about risk and return trade-offs. Yet, as Markowitz highlights, his decision-making framework has little to do with risk and return trade-offs. In the year 2014, now that we have a long enough data trail, we can show that Markowitz’s model doesn’t outperform a simple equal-weight allocation. The reason for this underperformance is a not critique on the model, which is clearly an incredible intellectual achievement, but has everything to do with the practical realities of accurately estimating a covariance matrix. So Markowitz’s 1/N approach was right, but for the wrong reasons. He was right that a simple 1/n allocation strategy was appropriate, but his reason – that he wanted to minimize his future regret – was the wrong one. The right answer is that good models don’t necessary translate into good practical ideas.

Holy cow. Someone should write a financial economic soap opera on this story…

  • The views and opinions expressed herein are those of the author and do not necessarily reflect the views of Alpha Architect, its affiliates or its employees. Our full disclosures are available here. Definitions of common statistics used in our analysis are available here (towards the bottom).
  • Join thousands of other readers and subscribe to our blog.
  • This site provides NO information on our value ETFs or our momentum ETFs. Please refer to this site.

About the Author:

Wesley Gray, PhD
Wes Gray has published multiple academic papers and four books, including Quantitative Value (Wiley, 2012), DIY Financial Advisor (Wiley, 2015), and Quantitative Momentum (Wiley, 2016).After serving as a Captain in the United States Marine Corps, Dr. Gray earned an MBA and a PhD in finance from the University of Chicago where he studied under Nobel Prize Winner Eugene Fama. Next, Wes took an academic job in his wife’s hometown of Philadelphia and worked as a finance professor at Drexel University. Dr. Gray’s interest in bridging the research gap between academia and industry led him to found Alpha Architect, an asset management firm that delivers affordable active exposures for tax-sensitive investors. He is a contributor to multiple industry publications and regularly speaks to professional investor groups across the country. Wes currently resides in the suburbs of Philadelphia with his wife and three children.
Yes No
This website uses cookies and third party services. Settings Ok


We use “cookies” on this site. A cookie is a piece of data stored on a site visitor’s hard drive to help us improve your access to our site and identify repeat visitors to our site. For instance, when we use a cookie to identify you, you would not have to log in a password more than once, thereby saving time while on our site. Cookies can also enable us to track and target the interests of our users to enhance the experience on our site. Usage of a cookie is in no way linked to any personally identifiable information on our site. Some of our business partners may use cookies on our site (for example, advertisers). However, we have no access to or control over these cookies.

Embedded Content

Articles on this Site may include embedded content (e.g. videos, images, articles, etc.). Embedded content from other websites behaves in the exact same way as if the visitor has visited the other website.These websites may collect data about you, use cookies, embed additional third-party tracking, and monitor your interaction with that embedded content, including tracking your interaction with the embedded content if you have an account and are logged in to that website.