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Smart Beta ETF’s have captured the imaginations of the investing public over the past five years, growing to more than 440 ETF’s and over $450 billion in assets, according to Morningstar. The growth in names and assets suggests that financial advisors and investors find some value in these strategies. The question that needs to be asked is, “By itself, is this a winning strategy?”
What is Smart Beta?
While traditional indexes consist of a list of securities weighted by overall size, some people have concluded that this weighting presents an advantage for large securities and a disadvantage for small securities. Many investors have concluded that this inequity must create exploitable inefficiencies within the market, bringing about the growth of Smart Beta. Smart Beta strategies involve researching and investing in a specific subset of the overall market that exhibits common attributes. The expectation of Smart Beta strategies is that those shared attributes will produce an investment return superior to the traditional weight-based index discussed above.
The concept of Smart Beta strategies is not new to the investment world. Professors Eugene Fama and Kenneth French of the University of Chicago Booth School of Business identified the predictive capacities of certain common factors exhibited within the companies that compose the public markets. They initially ranked companies according to three characteristics: company size, valuation, and price momentum.
Little has changed in the almost 25 years since Fama and French published their first paper on the topic; these three characteristics remain the primary drivers of most Smart Beta strategies. However, one change has been made: Quality was added as a factor to round out the offering.
Good idea run afoul?
Little disagreement exists over the theory that these factors demonstrate an ability to capture excess returns. As mentioned above, the most common explanation of the factors’ ability suggests that the success of traditional asset-weighted indexing has created exploitable inefficiencies.
Research into the returns associated with these factors has shown that their generated alpha is inconsistent over time. Ample evidence suggests any one of the factors could lead the market one year only to underperform swiftly the next. The level of volatility, much like the volatility shown in individual securities, calls into question the sustainability of the inefficiency that firms attempt to capture. The results, as shown over time, are excessive trading and poor timing, both of which lead to large costs and an inability to consistently capture the desired inefficiency.
The single factor return history presented by MSCI (Figure 1) shows the differences in return between Smart Beta strategies and the weight-based index. There are times when a particular strategy will differ so greatly from the index that one can celebrate the results of the bet made. For example, a focus on Momentum in 2011 would have produced a 4% positive absolute return — before fees and implementation costs — versus a loss of over 5% by the index. Volatile Alpha Generation
Approaching the strategy in a different situation, would the bet on Quality in 2008, which resulted in a loss of 38% versus a loss of almost 41% by the index, make you want to crack open a bottle of champagne?
Figure 1 demonstrates that the factors are fairly consistent over the 16 years presented: each factor detracts from performance about one third of the time. Each has outperformed the standard weight-based index. However, the time frame used can provide a false sense of confidence. In the initial historical view of the factors — the past 25 years —Quality demonstrated the best performance. However, in May of 2015, MSCI expanded the timeframe to include the past 40 years. The inclusion of 15 more years in the timeframe caused Quality to fall to the second worst performing factor. Moral of the story: timeframe matters.
The Need for Wisdom
In the world of investing, new “tricks” are frequently marketed as ways to cheat the system and outperform the index. Some may actually work, if one is able to remain consistent over time. However, all too often, the trick fails for a period of time, causing the investment industry to panic, believing their past success is inimitable. This fear appears to reflect the investing majority’s incomprehension of the fact that that these factors can be cyclical and coincide to patterns within the business cycle.
A deeper look into the periods of outperformance and underperformance and the corresponding Smart Beta index composition offers some insights and poses questions.
Is it a coincidence that Value experienced five years of material outperformance during the late stages of the housing and mortgage bubble, when the financial sector represented close to 25% of the value index?
Additionally, is it a coincidence that the Quality factor experienced its winning streak over the past 15 years during the late stages of the commodity cycle when the Energy, Industrial, and Material sectors averaged a third of the weight of the Quality index?
The one factor that appears to have a mind of its own is the Momentum factor. This is evident from the volatility in its holdings: Momentum-based sector weightings having the largest swings of all factors. The factor’s need to “recalibrate” each year to mimic the strong momentum experienced by securities may be the reason for its volatile excess return in the early phases of the new century, when markets were going through a period of reconstitution.
Conclusion
Smart Beta techniques open up a level of abstraction to the investing world, offering investors a more efficient method to capture outsized returns in areas of perceived inefficiency. While this may produce desired results over some time periods, the cyclicality of performance witnessed historically may make the capturing of alpha elusive. So this produces one question: Is the Smart Beta abstraction layer hiding the real inefficiency?