A Tactical Asset Allocation Researcher You Should Know

A Tactical Asset Allocation Researcher You Should Know

January 18, 2017 Research Insights, Tactical Asset Allocation Research
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(Last Updated On: January 22, 2017)

I’m a huge fan of hard-core academics that produce incredible research, and yet, very few are familiar with their research. I call these folks, “undiscovered gems.”

One might ask why undiscovered gems exist. On one hand, if a researcher produces incredible research, they should be widely recognized. However, this logical construct relies on an assumption: good researchers are good at sharing their work beyond their niche peer group in the ivory tower.

Bad assumption.

At Alpha Architect we try and fix this situation. We consistently read research and share it with our large community, which extends well beyond the readership of esoteric top-tier academic finance journals.

In this post we highlight research from one of my favorite researchers, Victor DeMiguel.

Source: http://faculty.london.edu/avmiguel/
Source: http://faculty.london.edu/avmiguel/

Professor DeMiguel, is relatively unknown among the practitioner community, and even among finance departments.(1) In fact, I had never heard his name until I stumbled across his website 5 or 6 years ago. Turns out Victor isn’t a finance professor — he’s a management science and operations professor!

Of course, asset allocation is really less of a finance problem and more of an optimization problem. So it makes sense that someone outside of pure finance would write some really interesting papers.

Anyway, I am indebted to Prof. DeMiguel and I have learned an incredible amount on tactical asset allocation from reading his work. We have discussed his incredible paper (alongside his co-authors Lorenzo Garlappi and Raman Uppal who are great researchers as well!) that highlights the incredible robustness associated with dead simple equal-weight, “1/N,” portfolios.

Perhaps his greatest learning device isn’t a paper, but a presentation he has on the history of asset allocation research and the avenues for the research vein in the future.

Here is the link to the presentation, which starts with a great introduction slide (pictured below)

Source: ppt deck
Source: ppt deck

I highly recommend all readers spend some time on the presentation. Particularly towards the end. There is a discussion of various issues in portfolio optimization and the quest to beat the infamous “1/N” portfolio:

  1. Estimate a better covariance matrix
  2. Estimate a better expected returns
  3. Identify better constraints

beat 1_n

So far all the whiz bang research doesn’t seem to provide crystal clear evidence that 1/N can be beat. In fact, we still recommend that investors beware of geeks bearing formulas and focus on simple trend-following rules if they choose to extend beyond 1/N. One idea that might be promising is robust estimation — see here.

Go forth and learn new things!

Note, Victor’s coauthors are also awesome. For example, here is a similar presentation from Raman Uppal on asset allocation.


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Definitions of common statistics used in our analysis are available here (towards the bottom)

References   [ + ]

1. A good friend of mine who is tenured at a top 10 business school did not know of Victor DeMiguel.

About the Author

Wesley R. Gray, Ph.D.

After serving as a Captain in the United States Marine Corps, Dr. Gray earned a PhD, 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 that delivers affordable active exposures for tax-sensitive investors. Dr. Gray has published four books and a number of academic articles. Wes is a regular contributor to multiple industry outlets, to include the following: Wall Street Journal, Forbes, ETF.com, and the CFA Institute. Dr. Gray earned an MBA and a PhD in finance from the University of Chicago and graduated magna cum laude with a BS from The Wharton School of the University of Pennsylvania.

  • I imagine the performance of the 1/N weighting system would depend on how one defines the asset classes. For example, does one lump all equities into a single asset class (“Global Equity”), or break out U.S., International, EM, etc.? What about going further and dividing U.S. into large and small cap? The increase in granularity would shift more and more weight towards equity.

    Nick de Peyster

  • Hey Nick,
    I think the takeaway from the research is that simple static diversification schemes seem more robust than dynamic optimization-based approaches — at least out of sample. So if you had 2 assets and you are an extremely aggressive investor — micro-cap distressed equity and US Treasury bills — 1/N may not be the best approach. But it doesn’t sound like risk parity, min var, tangency portfolio, etc. are either. So the user might implement a 1/N type concept and do 80% equity, 20% tbill. Rebal annual.

  • Thom_Jefferson

    Very interesting post! In my experience, the benefits of 1/N weighting over mean-variance optimization apply not just to allocations between asset classes, but also between trading strategies. I’ve learned painfully over the years that any strategy can “blow up” and deliver large drawdowns with poor returns, regardless of what its backtested or historical performance might have been. So while allocating a bit more to a low vol approach is OK, close to even allocations between strategies is the way to go (assuming of course, that all your strategies are generally sound).

  • That has been our experience as well. But nobody likes to admit this fact…