Menu Smart Beta: Structural vs. Situational Risk Premia Search
Diligence, Institutional Investing

Smart Beta: Structural vs. Situational Risk Premia

Faryan Amir-Ghassemi Director of Analytics

In this follow-up to our Smart Beta paper, we examine additional concerns around asset flows into these products by institutional investors.

The smart beta market had an interesting year. We saw a breathtaking rise in assets across the smart beta ETF space in the first six months. Strategies like low beta and low-volatility equity constructs drew mass attention. These were billed as an antidote to jitters around rising index valuations, and their performance was robust in the first half of 2016. We broadly addressed this trend here, focusing on one such product (The iShares EDGE MSCI Min Vol USA ETF). While that piece focused on analzying a smart beta product as part of an institutional investor’s broader portfolio, we alluded to an alarming trend in the product itself, noting:

What’s perhaps the most interesting aspect of the factor betas over time [for USMV] is the increase in momentum beta since 2014, which may coincide with the rapid increase in the NAV of the product.

To expand upon that, our value and momentum factors (set to a Carhart-4 factor 252 day lookback) show the change in their respective betas for USMV over time:

smart beta

Most interesting is viewing these after super-imposing asset flows into the product:

smart beta

We appear to be off the trail on Momentum, whereas Value is the real interesting factor. Per the Prospectus of the product, USMV seeks to “measure the performance of equity securities in the top 85% by market capitalization of equity securities listed on stock exchanges in the United States that, in the aggregate, have lower volatility relative to the broader U.S. equity market.” By virtue of their definition of volatility (a variance metric), momentum will be somewhat parametized since it’s also a price movement metric. However: Fama-French value is calculated on fundamentals (price-to-book) rather than price. This is how a low-variance product changes its stripes.

Research Affiliates sounded a similar alarm here, in a piece that drew intense discussion. To summarize their point, investors must understand structural and situational value-add for these products. As per above, performance may be driven by situational factors like fund-flows.

To unpack this, a smart beta product construct can become self-referential if the parameters it uses in its construct are sensitive to fund-flows (or as RA puts it, valuations). This is a common refrain against most market-capitalization weighted indices, in that they engender a certain momentum effect by virtue of constituent performance that leads to a larger index weighting. Similarly, USMV’s inclusion criteria (it’s “smart”) can be influenced by fund-flows (and flows to mirroring products in construct and selection criteria), as increased demand will ultimately drive the very correlations it measures to make security selections. While you’d need huge asset flows to move the needle on liquid, large-capitalization securities, low-volatility securities saw torrential inflows in 2016. At this scale, correlation changes cause unintended factor bias to creep into the portfolio.

smart beta

This calendar chart by J.D. Gardner shows how market cycles impact the favorability of various smart betas. You see vertical movement for factors such as high beta, momentum, and low volatility. These changes (e.g., High Beta’s -12.6% return in 2015 followed by its 25.4% return through November month-end) stress the impact of timing and asset allocation on ultimate returns, and (as we’ve discussed in the past) timing is not generally a skill that institutional investors demonstrate en masse.

Since Brexit, for example, which smart beta strategy has been the highest performing? High beta—as demonstrated by the Powershares S&P 500 High Beta Portfolio (NYSEARCA:SPHB):

smart beta

SPHB vs USMV – post Brexit (chart courtesy of Reuters Eikon)

Institutional investors are better served ensuring stable factors in order to focus less on tactically shifting exposures and more on balancing or harvest risk premia. Active managers must be aware of these tectonic shifts in asset allocation models and how they could sway their underlying holdings. Diligencing products carefully is vital in today’s ever-expanding smart-beta landscape.

No Comments