Smart beta is the Swiss Army knife of the investment world. Smart-beta products reorganize indexes in a variety of ways to tilt portfolios in multiple directions, trying to exploit volatility or capture value or ride momentum. But new research suggests that many of these vehicles may simply be dressed-up performance chasers. To succeed with smart beta, investors should focus more on what a product is actually doing and less on the popularity of a specific factor.
David Underwood, an assistant chief investment officer at the $33 billion Arizona State Retirement System, has been working on indexing for more than two decades. Like any strategy, it calls for discipline, he says. ASRS, which started doing its own indexing to help with risk management in its equity holdings, expanded that effort into building a broader multifactor smart-beta portfolio.
As a result of this work, the pension fund also seeded three low-cost factor-based exchange-traded funds offered by iShares. Those products, which track momentum, size and value factors, launched in 2013. In the U.S., smart-beta ETF products swelled from $61 billion to $377 billion in assets between 2009 and 2015, the Nasdaq Stock Market reports.
“What we wanted to address initially was risk-factor alignment across the total portfolio,” Underwood says. The multifactor approach allows Underwood to dial up or down certain factor exposures as market conditions change. It’s vital to be clear on what stocks underpin each factor product, he says: “You have to get under the hood with these products, which is a question of resources from the standpoint of the investor and what they are able to spend the time on and understand.”
Just ask Anthony Davidow, alternative-beta and asset allocation strategist at the New York office of Charles Schwab & Co. Davidow operates a think tank inside Schwab, which runs $11 billion in smart-beta strategies, aimed at showing investors how factors work. He too advocates a multifactor approach to smart beta but warns that to get results, investors may have to take steps like boosting their value exposure when everyone else is diving into quality.
“That’s not an easy conversation,” Davidow admits. “We often tell our clients that what seems like a bad idea on the surface is probably what you want to do. If you follow the crowd every time, it’s more likely you’ll be disappointed.”
In a February paper called “How Can ‘Smart Beta’ Go Horribly Wrong?” investment strategy firm and indexing pioneer Research Affiliates demonstrated that as more investors pile into smart-beta strategies, some products gain on investor interest alone, a bubbly behavior that could set up individual portfolios for headaches when popular factors revert to the mean. The authors compared the performance of such factors with that of the broader market. Their findings suggest that investors gain the most when a factor starts to trend upward with a market rally. If investors wait until a factor outperforms and buy into it late in the cycle, however, they’ll pay more and may get in just as performance starts to level off.
“What we find is that factor returns are achievable consistently, but they are lower than what investors may expect from some of these products,” says Rob Arnott, co-founder, chairman and CEO of Newport Beach, California–based Research Affiliates and one of the paper’s authors. Most smart-beta strategies are based on weighting an index of investments toward a set of proven factors, instead of a traditional market cap–weighted index in which the largest companies predominate.
Arnott illustrates it this way: Individual investment factors tilt portfolios in specific directions. Let’s say you’re channeling Warren Buffett, and you want a value tilt; there are smart-beta products with that overlay. Instead of just buying a Standard & Poor’s 500 index fund, smart-beta products reweight the index based on factors like value, momentum or quality. So, whereas Internet titan Alphabet might lead the pack among companies in the traditional index, Allstate Insurance Co. might be at the top of a smart-beta product targeting quality. Sounds pretty straightforward, right? Unfortunately, creating bubbles is also pretty straightforward — especially when investor fads are driving decisions.
“We’ve seen, for example, investors piling into the quality factor because it was doing well on a fundamental basis, which then raises the price of those stocks so they look better on paper from a relative-value perspective, and more people dive in,” Arnott says. “But when you strip away the bubble or the bubble reverts to the mean, the returns look a lot different.”
In their paper, Arnott and his co-authors, Research Affiliates analysts Noah Beck, Vitali Kalesnik and John West, argue that as investors go for factor-tilt strategies that ride a wave of relative value or rely heavily on data mining, they could be setting themselves and the market up for a smart-beta crash. Investors would do better to evaluate a product based on historical pricing, not just current popularity, they suggest.
That argument may not be convincing to investors who get drawn in by marketing pitches. Factor investing by itself can be a bit boring, but many new products claim to include proprietary factors or other bells and whistles that may only exist within a data-mining anomaly or when a given factor is popular. The rapid growth of smart-beta products has in some ways removed any clear sense of meaning from the terminology, which can be confusing for investors.
“Data mining is easier than ever, and it’s easy to set up a process that works great on paper, but it is actually a really bad idea in the market,” says Sara Shores, San Francisco–based global head of smart beta for BlackRock, which manages $121 billion in factor-based and smart-beta strategies across equities, fixed income, commodities and alternatives.
“The factors that are battle-tested have demonstrated return premiums all the way back to the 1920s, and are systematic and repeatable at scale,” Shores adds. “When you start getting into proprietary factors, you’re really chasing alpha, which by definition is very narrow and transitory. That’s not necessarily consistent with the philosophy of smart beta.”
