How One Firm Picks Smart Beta ETFs

How One Firm Picks Smart Beta ETFs

Envestnet now offers a quantitative way of boiling down 800-plus smart-beta funds to a recommended list of 33 ETFs.

Reviewed by: Cinthia Murphy
Edited by: Cinthia Murphy

Envestnet has long had an ETF ranking system that analyzes passive ETFs and offers advisors guidance on which ones they see as best in class. The firm has now expanded that evaluation process to the smart-beta ETF space, boiling down a universe of about 800 ETFs into an initial list of 33 recommended funds from 10 different issuers. (The issuers included in the list are Alps, FlexShares, First Trust, iShares, PowerShares, Schwab, State Street, VanEck, Vanguard and WisdomTree.)

That list of funds—not disclosed to the public—reaches 50,000-plus advisors that use Envestnet’s platform or that have access to the firm’s research. According to Envestnet’s co-founder and Chief Investment Officer Brandon Thomas, the impetus of this new effort is to digest a quickly growing number of smart-beta ETFs, and to help advisors navigate this “very complex” and “still evolving” area of the ETF market. There are some 800 smart-beta ETFs on the market today. They’re hugely diverse. How do you go about evaluating these funds?

Brandon Thomas: We want to identify products that have good characteristics in three areas. First, we want to make sure they’re low cost. Second, we want to make sure the ETFs are liquid. And third, we want to make sure the performance of the ETF is line with what we’d expect. And here’s where we diverge a little bit from our traditional ETF ranking. In our traditional ETF ranking process, we look primarily at tracking error for performance.

But with strategic beta, it’s tracking error plus two additional components: we measure risk-adjusted return— or the alpha of the ETF—to see if the ETF is generating excess return; and we measure the factor exposure.

Our performance dimension includes tracking error, alpha and exposure to the risk factor, such momentum or quality.

From a product screening perspective, we need to screen out products, and for that we use several screens:

  • We look at the legal structure of the ETFs. Is it an open-end investment company? Is it an uncollateralized debt obligation? Is it a limited partnership? ETNs are then excluded from the mix.
  • We screen out actively managed ETFs.
  • We require 24 months of data history to conduct our regressions, so ETFs that don’t have that data history, or are missing one month in a 24-month period, are screened out.
  • We screen out ETFs with less than $100 million in assets under management (AUM).
  • We look for at least four products in a given peer group to evaluate that peer group. So you need at least four competing ETFs—say, four momentum ETFs—to recommend one fund to advisors?

Thomas: That’s right. From your three main criteria—cost, liquidity, performance—is there one that impacts an ETF’s final ranking more than others?

Thomas: Each of these components is multidimensional. Cost, for example, consists of a couple of different components like expense ratio but also tax impact. Liquidity includes more than one metric, and performance includes tracking error, alpha, and factor exposure.

Each of the dimensions is equally weighted.

We then rank the ETFs, and take the top 30% on that combined ranking. Those become our approved strategic-beta ETFs in our platform. We do this on a quarterly basis—our traditional ETF ranking is done annually, but in strategic beta, we find that performance differences call for a quarterly look. The evaluation process only looks at five factors right now?

Thomas: Yes. The factors that we use in our regression are traditional factors that are very well-known and widely researched: value, momentum, quality, which includes profitability and low volatility, size and the market factor. Those are the factors we use in the risk model.

There’s also strategic data attributes, and these attributes are not new categories but additional descriptions of ETFs. For example, you could have a multifactor, strategic-beta ETF that is dividend-weighted, but it could have momentum and quality.

So we have several different attributes in one ETF. The strategic-beta attributes we evaluate include dividend-screened or -weighted, fundamentally weighted, momentum, quality, low volatility and low or high beta. We don’t rank equal-weighted. But the ones that we do rank represent about 90% of all the assets under management in the space. Why are equal-weighted ETF completely excluded from your smart-beta evaluation process?

Thomas: At this point, yes, they are. We could start ranking them in the future, but there just weren’t enough funds in any of the categories that would allow us to form a peer group. If more to come to market, we’d probably start ranking them. What about active ETFs? Like equal-weighted, they’re also excluded.

Thomas: We recently got asked by one of the fund sponsors about actively managed ETFs. We currently exclude those from our strategic-beta rankings. We don’t include them in our traditional ETF rankings either. Actively managed ETFs at this point fall through the seam.

We’re in the process of thinking about how we want to evaluate those, whether it’s from a quantitative perspective or whether we want to have our team of analysts pick up coverage on those and identify the approved products from their perspective. There are a lot of ways this methodology can and will evolve over time. Do you include every issuer in your universe? Your initial recommended lineup of smart-beta ETFs seems to include only 10 fund families.

Thomas: That’s one of the initial screens, or filters, that we have—a firm-level AUM filter. We’re trying to capture the products that will have staying power. We want to capture the firms that’ll be there for the long run. If the market drops, they’re not going to start closing ETFs because of low adoption of their ETFs. It’s about having high confidence in these firms in times of stress.

If you look at all issuers, there’s a sort of cutoff at $10 billion and up. Then there’s a fairly significant drop-off from that to the next level. We evaluate funds from those with $10 billion or more. Do you worry that this firm AUM cutoff excludes key rising players like Goldman Sachs and J.P. Morgan, which have some of the most popular multifactor ETFs on the market today?

Thomas: That’s always a concern, but we need to place a cutoff at some point. J.P. Morgan and Goldman Sachs are relative newcomers and they will—assuming that they gain traction—eventually meet our threshold. It’s the same criteria we established for our traditional ETF ranking methodology. Of all the smart-beta ETFs, your initial recommended list to advisors in your platform includes only 33 funds?

Thomas: Yes. We could recommend a lot more, but we want to make sure we’re giving advisors the ones that satisfy our metrics in the best way possible. They’re quantitatively approved, and what that means is that we stand behind those ETFs as a fiduciary.

We have some firms that use our platform that will say, “We only want to make approved products available to our advisors.” Other RIA firms will say, “We don’t really care about the status. We’ll do the evaluation ourselves.” So what this does is just boils down the list to a manageable number for advisors to use in their portfolio and not generate a lot of confusion. Is there a goal number? Do you try to keep this list of approved funds to, say, under 50 or 100 ETFs? Or it’s however many fall into that top 30 percentile?

Thomas: If we continue to see growth in the number of ETFs, that number is almost certain to grow. But in the approved list, you get not only the top 30% but the funds that remain in the top 50% for a grace period.

 Once they’re on the approved list, and then fall to the 40th percentile or whatever, they’ll still remain on the approved list for some time. So you’re constantly adding new funds that rise to the top 30% but you’re not dropping off the ones that fall below 30 until they reach the 50th percentile. So we’re going to see the numbers grow.

This methodology, as with all of our methodologies, evolves over time. If we see that we’re getting high demand from advisors to say, “Look, why don’t you include this Goldman ETF,” there are things that we can do to include those types of products. We’ve made exceptions in certain cases. And we’ve adjusted our methodology in response to advisor needs. I could see somebody arguing that these brush strokes are too broad to really capture all the nuances of smart-beta ETFs. I could also see someone saying that it’s too narrow because they exclude a big list of good ETFs. What’s your take?

Thomas: I think if we’re not satisfying everybody, that’s probably a good thing. We’re on the right path, and we can evolve it over time. Our role really is to be a fiduciary to the advisors. We want to make sure we come up with a rational and reasonable methodology that gives them some guidance as to the primary strategic-beta attributes of funds. And then evaluate the funds according to some well-thought-out metrics.

It’s a very complex area that, even from an industry perspective, is still evolving. We’re trying to put some objective, quantitative understanding around it to filter out some of the funds we think are the highlights of each of the categories.

Contact Cinthia Murphy at [email protected]


Cinthia Murphy is head of digital experience, advocating for the user in all that does. She previously served as managing editor and writer for, specializing in ETF content and multimedia. Cinthia’s experience includes time at Dow Jones and former BridgeNews, covering commodity futures markets in Chicago and Brazil equities in Sao Paulo. She has a bachelor’s degree in journalism from the University of Missouri-Columbia.