Destroying Smart Beta 2: Ground Rules

Destroying Smart Beta 2: Ground Rules

How is defining smart beta tricky? Let us count the ways.

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

How is defining smart beta tricky? Let us count the ways.


This blog is the second installment of a series transforming our ideas about smart beta. Part I set the stage, arguing that defining smart beta in an ETF context is essentially impossible.


In my previous blog, “Destroying Smart Beta: Part I,” I challenged readers’ perceptions about what defines smart-beta funds.

By taking a broad look at the way indexers solve problems and adapt to their universe, I was able to show that many of the features of smart-beta funds show up widely, while some smart funds don’t fit preconceptions.

I promised to demolish your hopes of defining smart beta.

Today the work starts in earnest, beginning with the ground rules. We need to understand what a successful definition of smart beta would look like, and what the possible candidates are. We can even test a few of the definition candidates.

Like any type of ETF classification, a successful definition of smart-beta ETFs must satisfy basic ground rules. It must:

  1. Apply to all funds consistently
  2. Classify funds according to how they are constructed, rather than by their names or marketing: Many investors incorrectly believe that a fund’s name tells the whole story. They fail to understand that a fund like the IQ Global Resources ETF (GRES | D-44) focuses on momentum in selecting its constituents, or that the Market Vectors Africa ETF (AFK | D-28) selects and weights securities based on country GDP rather than market cap.
  3. Make meaningful groupings: Criteria that create overly broad groups, perhaps including 95 percent or more of US ETFs, will not help us separate out the funds we want to discuss.
  4. Produce results that are widely acceptable to the ETF community: The S&P 1500 is a curated index shaped by profitability screens and an selection committee. Its smallest security, AutoNation, ranked No. 954 in the Russell 1000 as of April 7, 2014. In the U.S., for all but a few index nerds, the S&P 500 Index represents the stock market. In other words, any definition of smart beta must make sense to the people most likely to use it.

These ground rules will allow us to test smart-beta definitions. Any criterion that can satisfy all four ground rules is a winner—a bona fide working definition of smart beta.

Conversely, if a criterion fails any of them, it’s not workable.

I am not alone in my pursuit to define smart beta. Fund sponsors, consultants, journalists and marketers have staked out plenty of criteria they claim define smart beta.

At the end of this blog, I’ve included links of smart-beta definitions from the Financial Times, Cogent, Towers Watson, Russell Investments, Morningstar and Vanguard.

All these experts converge on the following seven smart-beta criteria:

  1. Transparency
  2. Rules-based/quantitative
  3. Thematic/specific segments or objectives
  4. Noncap-weighting
  5. Captures risk premia/factor exposure
  6. Superior risk-adjusted returns
  7. Improves portfolio diversification

I’ll examine these criteria one by one.

Today I’ll take on criteria 1-3, and next time I’ll examine “noncap-weighting” as a criterion for defining smart beta. That’s where folks tend to take dangerous short cuts, so I’ll need an entire blog to set the record straight.

From there, I’ll turn my attention to “risk premia/factor exposure”—also in a stand-alone blog. Then I’ll address the final two criteria, “risk-adjusted returns” and “diversification,” together in the fourth blog of this series.



So, to return to today’s task, those first three criteria—“transparency,” “rules-based” and “themes”—will all fail because they break ground rule No. 3. That rule, again, is that a successful definition of smart-beta ETFs must “make for meaningful groupings.”

So, those first three smart-beta criteria are just too broad. They do a great job of differentiating active management and smart beta, but they are useless within the ETF universe, because they encompass at least 95 percent of all ETFs.

They’re basically meaningless. Let me explain further:

  1. Transparency: At this point, all ETFs are required by law to post their holdings daily. Until and unless the Securities and Exchange Commission permits nontransparent active ETFs, the first criterion includes every single U.S. listed-ETF. Transparency is useless for parsing the ETF landscape. One hundred percent of anything is the whole shebang, not a group.
  2. Rules-based, or quantitative: This criterion distinguishes between passive and active strategies. It may be useful in the overall investment marketplace where active managers dominate, but it’s insufficient in the ETF universe. Ninety-five percent (by count) and 99 percent (by assets) of ETFs are rules-based. Let’s agree to exclude active funds from any discussions of smart-beta definitions, and move on.
  3. Thematic or specific exposure: The ETF market is a salad bar full of slicing and dicing. Except for 10 global, broad-based equity and commodity funds, every ETF offers a narrowed-down view of the market. Again, 99 percent of funds is not a meaningful group

I know I’m being repetitive, but these three broad criteria are of no use to us for defining smart beta in the ETF context. They’re more like descriptions of ETFs, excluding active funds.

Preview Of Coming Attractions

The final four criteria—noncap weighting; captures risk premia/factor exposure; superior risk-adjusted returns; improved portfolio diversification—are each, as I said above, complex topics worthy of their own blogs. So tune in for more on those.

Be sure to come with scuba gear, because in my next blog, on alternative weighting, we’ll take a deep dive into index history, tax law, illiquidity and inaccessible markets.

Until then, I’ll let you in on my open secret: These four criteria aren’t going to pass the ground rules test either. That will help prepare us for my main point: We really do need to think of smart beta in a smarter way.


Links to smart-beta definitions:

Financial Times


Towers Watson






At the time this article was written, the author held no positions in the securities mentioned. Contact Elisabeth Kashner, CFA, 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.