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.
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.
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...