Destroying Smart Beta: Part 1

Destroying Smart Beta: Part 1

Series of blogs will examine how to get rid of a dumb term like ‘smart beta.’

Director of Research
Reviewed by: Elisabeth Kashner
Edited by: Elisabeth Kashner

Series of blogs will examine how to get rid of a dumb term like ‘smart beta.’


This blog is the first installment of a series examining smart beta and how to think about the expanding phenomenon more clearly.

The inquiries are coming in faster and harder than ever before. Everyone wants facts and figures about so-called smart beta funds. How many are there? What’s the biggest one? What’s the total number of assets under management in smart beta funds? How many launched in 2013?

As the director of research at, I am like a zookeeper, tending to a menagerie of beautifully varied creatures. There are, figuratively speaking, peacocks platypuses and gorillas, each filling a specific niche of the ETF ecosystem. Every fund has nuances and features that allow it to adapt to its market.

If you knew ETFs like I know ETFs, you would refuse to even try to count smart-beta funds. That’s because the features of headline smart-beta funds end up being common throughout the ETF landscape, popping up as adaptations to regulation and market conditions, without any promise of risk-adjusted outperformance.

I look and I see equal weighting in laddered commodities and bond strategies, S&P’s industry indexes, social justice funds, and hedge-fund copycats as well as in funds like the Guggenheim S&P 500 Equal Weight ETF (RSP | A-75)—which advertises “reduced volatility and improved risk efficiency.”

And if you knew ETFs like I do, you would encourage people to understand each fund’s strategy, and to group funds by common features rather than by appearances. You would work hard to educate people to understand each fund’s structure and properties, and how it might act in various environments.

You would not waste your time counting something you cannot define; namely, total assets under management in smart beta.

Want to try it? What do you think is the biggest smart-beta ETF in the U.S.? For starters, here’s a list of the 10 biggest equity ETFs by assets at the end of the first quarter.

How many of these are smart-beta funds?

Smart Beta Wrecking Ball Top 10 Funds Table

Depending on how well you know these funds, and on how you think about smart beta, your answer could range from zero to six. If you picked VIG, the payout-tilted Vanguard Dividend Appreciation ETF (VIG | A-67) for the No. 1 slot, you’d be in for a surprise.

Many of our favorite “dumb” funds, it turns out, have smart-beta features.


  • The SPDR S&P 500 ETF (SPY | A-98) and the iShares Core S&P 500 (IVV | A-98) both share an index that screens components for profitability, just like the index underlying the FlexShares Quality Dividend ETF (QDF | A-78) does.
  • The PowerShares QQQ (QQQ | A-53) has a tier of equally weighted securities, partially mimicking RSP, thus giving it a smart-beta tilt to small-cap stocks.
  • The iShares Russell 2000 ETF (IWM | A-82) accesses the small-cap premium—that’s factor investing.
  • The iShares Russell 1000 Growth (IWF | A-89) has momentum exposure—that’s also factor investing.

The surprises go the other way, too—“smart funds” have some pedestrian features. VIG, the smartest-seeming of the top 10 above, is cap-weighted. So, what happened to alternative weighting as a hallmark of smart beta?

Marketing Drives Labeling

Defining smart beta is harder than you thought, huh? Actually, it’s impossible.

Here’s why:

The definitions of smart beta that you have seen are working backward. They start with a set of funds that someone wants to call smart, and teases out their characteristics, like nonmarket-cap weighting and factor exposure. Then they generalize. That’s where the trouble begins.

The most common generalizations you’ll hear about smart beta are that it uses nonmarket-cap weightings, and provides factor exposure.

As we saw with VIG, some funds with sophisticated strategies use cap weighting. The Vanguard High Dividend Yield ETF (VYM | A-92), the PowerShares Buyback Achievers (PKW | A-94) and the db X-trackers MSCI Japan Hedged Equity Fund (DBJP | B-58) are all cap-weighted.

Meanwhile, some noncap-weighted funds like the SPDR S&P Retail ETF (XRT | A-40) and the SPDR S&P Regional Banking ETF (KRE | A-42) don’t strike anyone as smart-beta funds.

Factor exposure can be a design feature, as it is in the FlexShares Morningstar U.S. Market Factor Tilt ETF (TILT | A-79) or a knock-on effect, as it is in the Utilities Select Sector SPDR Fund (XLU | A-94).

The ETF menagerie is so full of complex strategies that generalizing gets you in trouble, the kind of trouble that leads you to misunderstand strategies and to miss investment opportunities.

I’ve turned the ETF universe inside out and upside down, but I can’t find a definition for smart beta that makes sense of the jumble of U.S.-listed ETFs. Defining smart beta is a fool’s errand.

Designing The Smart-Beta Wrecking Ball

It will take me some time to demolish any hopes of defining smart beta.

So, I’m going to allow myself a series of five blogs (in addition to this one): Firstly, four blogs to walk you through the illogic of existing smart-beta definitions, and one to get you on board with my radical suggestion that we ditch marketing labels and talk about what funds actually do.

In my final blog, I’ll introduce a tool I have built to help you sort funds by their strategies.

Here is my game plan, blog by blog:

  1. Present common definitions for smart beta and requirements for a successful definition (hint: it has to work for all funds)
  2. Dive deep into what cap-weighting means, and how security selection and weighting are used in the index fund industry
  3. Explore factor investing and its effects across the ETF universe
  4. Test claims regarding long-term risk-adjusted outperformance
  5. Propose a better way to talk about complexity and strategy in ETFs, along with a prototype strategy classification tool from

Stay tuned for the wrecking ball. It’s going to be a blast.

At the time this article was written, the author held long positions in IWM, IVV and IWF. Contact Elisabeth Kashner at [email protected].


Elisabeth Kashner is FactSet's director of ETF research. She is responsible for the methodology powering FactSet's Analytics system, providing leadership in data quality, investment analysis and ETF classification. Kashner also serves as co-head of the San Francisco chapter of Women in ETFs.