Is Smart Beta Getting Smarter?

Paul Murdock of Indexology recently sat down with Vinit Srivastava to trace the origins of smart beta indexing.

S&P Dow Jones Indices - Author

Apr. 20 2018, Updated 9:58 a.m. ET

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Paul Murdock, S&P Dow Jones Indices Indexology Magazine Editor, recently sat down with Vinit Srivastava, Managing Director Strategy & ESG Indices at S&P Dow Jones Indices, to trace the origins of smart beta indexing. From early days, before it even had a name, to recent developments in multi-factor indexing, Vinit shares his perspectives on each step of the journey and weighs in on where smart beta may be headed.

PAUL: What were the first factors tracked by indices?

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VINIT: Before the recent wave of single- and multi-factor indices (over the last five to seven years), dividend, value, and equal-weight indices had already been widely adopted. These indices and the funds tracking them sought to provide exposure to yield, value, and size—all well-established factors that have been known to have a risk premium. One of the reasons for the success of these early strategies was their simplicity, both in their construction and their implementation. In fact, many of these factor strategies were adopted by active managers long before passive solutions were available.

PAUL: How has the factor index landscape evolved since then?

VINIT: Over the last decade, we’ve seen the creation of single-factor indices, which seek to provide exposure to well-known risk factors like low volatility, quality, size, momentum, value, and yield. These have allowed market participants to take views on factors and combine them strategically or tactically. As the market has evolved, multi-factor strategies that provide exposure to multiple risk factors in a single package have become more common.

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One of the main drivers of this was asset owners’ need for costeffective strategies that meet their objectives in an era when long-term return expectations from most asset classes are lower than they have been historically. In this kind of environment, the right risk/return characteristics are essential to meeting long-term liabilities.

PAUL: How has the spectrum of multi-factor indices expanded over the years, and how do these indices differ from each other?

VINIT: Many multi-factor indices have been launched over the last five years. They differ from each other along two dimensions: the factors they provide exposure to and their construction.

Factor exposures are often chosen based on which ones have offered attractive diversification benefits according to historical correlations, as well as investors’ outlook on the excess returns associated with those factors. For example, in most markets, popular two-factor combinations include quality and value, value and momentum, and low volatility and value. A three-factor combination that has also caught on, for similar reasons, is quality, value, and momentum.

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Portfolio construction also differentiates multi-factor indices from each other. Providers try to offer efficient exposure to the chosen factors, while attempting to stay within stated investment objectives regarding liquidity and tradability, capacity, and tracking error to the cap-weighted benchmark. Different indices use different methods to try to accomplish their particular goals.

The choices in both of those areas—factor exposure and portfolio construction—affect the indices’ risk/return profiles, and market participants have to look into those in detail to figure out what works best for them. Some investors also choose to implement custom multifactor strategies when off-the-shelf products don’t mesh with their requirements.

PAUL: What would you say is driving the surge in smart beta fund flows?

VINIT: The shift to smart beta has been largely about capturing the efficient risk/return characteristics that market participants are seeking without paying a high price. These approaches were historically available through active strategies, which provided excess returns (or lower risk) by systematically allocating to certain factors. Now, these strategies are available in a passive form, providing similar “active” exposure at a fraction of that cost.

PAUL: Are you seeing more growth in multi- or single-factor strategies? Why?

VINIT: A recent survey, corroborated by our own outreach, showed that multi-factor strategies are the most commonly evaluated and adopted smart beta strategies among asset owners today.1

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There are several reasons investors might consider multi-factor strategies. First, and most obvious, is that individual factors may go through sustained periods when they don’t outperform the benchmark— so a diversified allocation to a set of relatively less correlated factors may make sense. An example of this approach is our S&P 500 Quality, Value &; Momentum Multi-Factor Index, which provides an equal active exposure to those three factors. From Dec. 31, 1994 to Jan. 31, 2017, it outperformed the S&P 500 80%, 96%, and 100% of the time over rolling 3-, 5-, and 10-year periods, respectively.2 Of the individual factors the index allocates to, only quality showed similar outperformance over the same span. And, as our example shows, market participants realize that strategically choosing multiple (relatively uncorrelated) factors may allow them to outperform the benchmark over medium- to long-term periods. In other words, some investors are attracted to the potential for receiving excess returns over the benchmark without taking a view on specific factors.

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In terms of single factors, value and low volatility are among the most popular choices. The prevalence of traditional style investing has made value a part of many investment frameworks for quite some time, while low volatility is relatively new but still another well-understood risk factor. Market participants have embraced low volatility for its use as a traditional risk-control factor and also in newer risk-budgeting approaches.

PAUL: Some have speculated that smart beta is just another fad and others worry that factors won’t be viable sources of risk premia once too many people catch on. How would you respond to these concerns?

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VINIT: You’re talking about what is commonly referred to as the “crowding” effect. As assets in factor-based strategies approach the USD 500 billion mark, these questions often come up. However, experts generally disagree that these strategies are crowded,3,4 and we agree with this assessment for a few reasons. First, the risk premia are not new—they have been established in academia for a long time and have been used by active managers to tilt their portfolios for years. As discussed earlier, assets in factorbased strategies are moving away from active strategies for fee and transparency reasons. Given the diversity of approaches in portfolio construction and the range of factors used in these strategies, crowding doesn’t seem to be an issue at this point. On the issue of whether the premia associated with the risk factors would persist if there were crowding, there are wide-ranging views on that subject.

PAUL: What’s the next frontier in smart beta?

VINIT: Factor-based strategies in equities have come a long way in the past decade. The industry is now sorting through implementation and scaling issues, like capacity and liquidity, which are quite challenging. We see the next frontier as the development of unique smart beta ideas within fixed income. In 2017, we launched the S&P U.S. High Yield Low Volatility Corporate Bond Index,5 which illustrated that the low volatility effect can be captured in the high-yield space using duration times spread as the volatility measure.

There is a lot more to come in this space as we look beyond equities to fixed income, ESG, and multi-asset strategies.

1. FTSE Russell 2017 Smart Beta Survey, June 29, 2017,

2. Data from Dec. 31, 1994 to Jan. 31, 2017. We constructed as many rolling 3, 5, 10, and 15 year windows within that period and checked the percentage of times the S&P 500 QVM Multi-factor index outperformed the S&P 500 among those rolling periods.

3. AQR, “How Can a Strategy Still Work If Everyone Knows About It,” (2015),

4. GSAM, “Crowding in Smart Beta Strategies,” (2017),



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