Deconstructing ‘Smart Beta’

August 21, 2014


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?

If you knew ETFs like I know ETFs, you would refuse to count smart-beta funds, because you would see “smart” features in all manner of funds, even those not deliberately designed for risk-adjusted outperformance. You would see factor exposure in small-cap funds and utilities, equal weighting in industry funds, social justice funds and hedge-fund copycats. Every fund has nuances and features that allow it to adapt to its market.

Instead of wasting your time counting something you cannot define, you would encourage people to understand each fund’s strategy, and to group funds by common features rather than by marketing claims. You would work hard to educate people to understand each fund’s structure and properties, and how it might act in various environments.

A quick quiz will introduce the difficulty of defining smart beta.

Figure 1 provides a list of the 10 biggest equity ETFs by assets at the end of the March 31, 2014.


How many of the funds in Figure 1 are smart-beta funds?

Depending on how well you know these funds, and on how you think about smart beta, your answer could range from zero to six. Many of our favorite “dumb” funds, it turns out, have smart-beta features.



The surprises go the other way, too—“smart funds” have some pedestrian features. If you picked VIG, the payout-tilted Vanguard Dividend Appreciation ETF (VIG | A-67) for the No. 1 slot among the smart-beta ETFs, you’d be in for a surprise. VIG, the smartest-seeming of the top 10 above, is cap weighted.

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 tease out their characteristics, like nonmarket-cap weighting and factor exposure. Then they generalize. That’s where the trouble begins.

Taking A Wrecking Ball To Smart Beta
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. Instead, I propose we address the illogic of existing smart-beta definitions, and then pursue my radical suggestion that we ditch marketing labels and talk about what funds actually do.

A Common Definition?
First, we need to establish the ground rules. 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
  3. Make meaningful groupings
  4. Produce results that are widely acceptable to the ETF community

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 of defining smart beta. Fund sponsors, consultants, journalists and marketers have staked out plenty of criteria they claim define smart beta. The literature converges on the following seven smart-beta criteria:

  • Transparency
  • Rules-based/quantitative
  • Thematic/specific segments or objectives
  • Noncap-weighting
  • Captures risk premia/factor exposure
  • Superior risk-adjusted returns
  • Improves portfolio diversification

100% Is Not A Meaningful Group
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.” These rules produce groups that contain all—or nearly all—U.S.-listed ETFs.



Current regulations require all ETFs to post their holdings daily. Until and unless the Securities and Exchange Commission permits nontransparent active ETFs, the transparency 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.

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 of ETFs and 99 percent of ETF assets are tied to rules-based indexes. I will exclude active funds from any discussions of smart-beta definitions.

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.

Other smart-beta criteria are a bit more complex and deserve much more in-depth examination. Noncap weighting is probably worth a research paper all on its own.

Noncap Weighting
A Broad Category
“Anything but cap-weighted” is the simplest definition of smart beta. But it falls apart if you rigorously use it to sort ETFs.

Indeed, many funds, such as the PowerShares FTSE RAFI US 1000 Portfolio (PRF | A-88) and the iShares Select Dividend ETF (DVY | A-68), stake their “smartness” on their fundamental- and dividend-weighting schemes, respectively, and thereby imply that weighting is the key to smart beta.

It’s tempting to think that alternative weighting defines smart beta. But that’s far too simplistic, as we shall see.

The Weight Of Non-selected Securities Equals Zero
It’s not clear what the phrase “non-cap-weighted” means in a smart-beta context.

As of April 9, 2014, a total of 912 of the 1,573 U.S.-listed ETFs, or 58 percent of them, are cap weighted. But not all of the cap-weighted ETFs mimic the broad market. VIG is a perfect example.

Cap-weighted VIG builds a portfolio that is very different from the U.S. total market, by security selection alone. VIG restricts its portfolio to U.S. companies that have 10 or more years of annual dividend increases. VIG’s 145 constituent portfolio (as of April 1, 2014) is smaller-cap than the U.S. total market, with lower price-earnings multiples and higher yields. Indeed, many consider VIG to be a smart-beta fund, despite its cap-weighting. Security selection can tilt a fund’s portfolio well away from the broad market.

We need to get a little smarter about how we sort funds if we want to get to what most folks mean when they say “cap weighted.” I suggest we take a closer look at “plain vanilla” funds.



Plain Vanilla
“Plain vanilla” means that an index fully represents the long side of the opportunity set of the market it targets. Critically, plain-vanilla indexes are market-cap-oriented in two ways: security selection; and weighting. Note: We make allowances for S&P’s committee-based selection process, which de-selects many securities, but results in index series that retain virtually all properties of the overall market, and for adaptations needed in the futures markets, where the net exposure is zero.

As of April 9, 2014, 810 U.S. ETFs were plain vanilla—that’s 51 percent.

The other 49 percent is like the parking lot crowd outside a Grateful Dead show: You can find just about anything there—the smart, the stupid, the jugglers, the clowns and the wastrels. Yes, you’ll find the smart-seeming funds you’re looking for, but you’ll find many others, too. That’s how we know alternative weighting fails as a smart-beta definition.

More formally, alternative weighting fails as a smart-beta criterion because, when it’s applied to all funds, it produces groups that aren’t widely accepted by the ETF community.

Test Cases
The seven test cases in Figure 2 show the folly of relying on selection and weighting schemes to define smart beta. I’ll use these as examples to remind you how indexers use selection and weighting to create investable products in challenging markets.


Their solutions are usually smart, but the indexes they build are nothing like the fundamental, dividend or low-volatility schemes we call to mind when we think of smart beta.

Every one of these funds’ indexes solves a problem: from hedging duration exposure, to maximizing tradability in an illiquid market, to providing compliant exposure to narrow industries or creating thematic exposure.

All of them are adaptive, though some have not aged well. They are all edge cases, testing the usefulness of a plain-vanilla smart-beta definition.

Hedged Exposure In The 51 Percent
There are two problems with looking for smart beta by flavor (plain vanilla versus everything else). There are some pretty exotic funds in the vanilla group, while some plain-Jane ETFs make it to the not-vanilla bunch.

Let’s start with the easier of the two problems, the one type of by-the-book plain-vanilla fund that seems smart.

The ProShares’ High Yield - Interest Rate Hedged ETF (HYHG | C) is cap weighted and cap selected, but has an overlay that grossly alters its pattern of returns. HYHG shorts U.S. Treasury futures to manage interest-rate risk.

So, HYHG’s returns will be different from the overall high-yield market any time interest rates move. Hedging exposures is a clever, complex strategy, but HYHG’s cap weighting and selection process leaves it stuck in the vanilla bucket.



Really, though, this isn’t the end of the world. I’m sure that if the hedged funds were the only problem with using alternative weighting as a smart-beta definition, we would deal with it as a minor exception. But the mishmash of strategies in the alternatively weighted group—the 49 percent—is the bigger challenge.

Flotsam In The 49 Percent
There’s a group of legacy funds in the 49 percent. These funds solve the problems of yesteryear, but live on in the ETF landscape. Our test cases Nos. 2 through 4 show all too well how some not-exactly-smart funds live alongside the hot new factor funds in the alternatively weighted bucket.

Idiosyncratic funds like the Nasdaq 100 ETF—the PowerShares QQQ (QQQ | A-52)—and the SPDR Dow Jones Industrial Average Trust (DIA | A-71) are not plain vanilla, so they land in the 49 percent.

QQQ restricts its selection universe to the Nasdaq. QQQ has its oddities—it excludes financials, limits the weight of top holdings, rebalances unpredictably and sandwiches an equally weighted tier between two cap-weighted ones.

QQQ tries to be smart, but not in the way you might think. Nasdaq, advancing its status as an exchange by sponsoring an index, needed to make sure its index was investable. High concentrations in listings like Microsoft or Apple pushed Nasdaq to implement position limits and a redistribution scheme. Bottom line: QQQ ain’t smart beta.

DIA tracks the Dow Jones industrial average, the granddaddy of all indexes. There’s no question that measuring the stock market was a huge innovation in the 19th century, but the Dow hasn’t aged well.

The Dow is selected by a committee and holds one share (split-adjusted) of each of its 30 constituents. Today it’s clear that the Dow is about as dumb an index as could be. Nobody wants to put DIA on a smart-beta list.

The 49 percent has other legacy funds too, some from much more recent vintages. For example, the BLDRS Emerging Markets 50 ADR (ADRE | B-35) was launched in 2002, when access to emerging markets was difficult and the iShares MSCI Emerging Markets ETF (EEM | B-100) was highly optimized. ADRE is focused on large-caps and only includes ADRs, creating a tradable and hedge-able basket.

ADRE clocks in at 93 percent large-caps, and overweights Brazil, telecoms and energy, as of April 1, 2014. It’s not vanilla, but it’s not smart beta, either.

Tax-Penalty Avoidance Puts Equal Weighting In The 49 Percent
Our fifth test case, the SPDR S&P Bank ETF (KBE | A-54), tracks an equal-weighted index, as do all of the SPDR S&P U.S. industry funds. I would guess that the ETF community would be reluctant to give S&P’s industry suite a smart-beta label, for a number of reasons.

Internal Revenue Service rules for registered investment companies require diversified funds to hold no more than 25 percent in any single security and no more than 50 percent cumulatively in positions above 5 percent. In narrow industries like banking, this matters.

As of April 1, 2014, the top five U.S. banks comprised 71 percent of the industry, according to Thomson Reuters. No. 1, Wells Fargo, clocks in at 19.4 percent, while No. 5, U.S. Bancorp, takes up 6.4 percent of the weight. No diversified fund registered under the Investment Company Act of 1940 could track a vanilla banking index.

MSCI solves this problem by capping the weight of the biggest positions; others create tiers. S&P’s equal weighting is somewhat of a drastic solution, but it’s better than the double taxation that curses funds that fail to comply with RIC regulations. Bottom line? KBE is equal weighted, but SSgA promotes it as representing the banking industry, not as smart beta.

Oddballs And Hippies In The 49 Percent
Our final two test cases show how indexers use selection and weighting to craft another world, where everyone can act like an accredited investor and where good deeds are rewarded and wrongdoers punished.

To capture specialized exposures such as private equity, social media, initial public offerings or up-and-coming Nashville, indexers turn to nonmarket-cap-related rule sets to isolate an economic theme.



The PowerShares Global Listed Private Equity Portfolio (PSP | F-58) faces a hard problem: delivering access to private equity returns. Private equity by definition is not publically listed, but some general partners like business development companies and MLPs are exchange-traded. The Red Rocks Global Listed Private Equity Index tracks these publically listed firms.

The index attempts to mimic what Red Rocks describes as a typical institutional private equity portfolio by differentiating between early, mid- and late-stage firms. Their weighting looks through to the listed firms’ portfolios, targeting 65 percent late-stage, 25 percent midstage and 10 percent early-stage exposure. The weight of each tier is fixed; and within the tiers, holdings are cap-weighted.

This is an inventive solution, perhaps even an advance for democracy. But I suspect it’s not the sort of indexing revolution that smart-beta believers envision.

The iShares MSCI KLD 400 Social ETF (DSI | B-90) is a cap-weighted fund that deliberately selects companies that safeguard the environment, communities, human rights and other sensitive areas. MSCI’s socially aware methodology helps some investors feel like they can align their investments with their beliefs. DSI’s portfolio isn’t vanilla, but it’s not really smart beta either.

Leave Your Cap On
The simplicity of noncap-weighting is its downfall as a smart-beta definition. Indexers have been skillful, adaptive and inventive in creating investable products that access quirky parts of the market, using a rich set of selection and weighting schemes.

These adaptations are smart, and surprisingly common in the ETF landscape. Not every alternatively weighted fund fits our presumptions of what smart beta is.

As I mentioned when I introduced the smart-beta definition ground rules, the groupings that result from definitions of smart beta have to make sense to the people who will use it. Alternatively weighted funds fail this test.

Factor exposure does no better, and for the same reasons.

Factor Investing
There is a strong movement to conflate smart beta with factor investing. For lots of folks, factor investing is the “smart” part of smart beta. Sorting ETFs by factor exposure produces groupings that could cause some rifts in the ETF community, and therefore break our ground rule regarding widespread acceptability.

Factor investing targets specific drivers of returns. The most common factors are size, value, yield, momentum, volatility and quality. These factors show up in plain-vanilla indexes, of course, but can also be targeted and amplified, especially by index providers with huge databases and crafty analysts.

The past decade has brought us factor funds like the FlexShares Morningstar’s US Market Factor Tilt ETF (TILT) and the PowerShares S&P 500 Low Volatility Portfolio (SPLV | A-45). Older “smart” funds target factors too: value in PowerShares’ FTSE RAFI US 1000 (PRF | A-88), size in Guggenheim’s S&P 500 Equal Weight (RSP | A-75) and yield in iShares’ Select Dividend (DVY | A-67).

Designer funds promote the smart-beta label. However, branding all funds with factor exposure as smart beta will start arguments. In terms of our ground rules, sorting ETFs by factor exposure produces results that are not widely acceptable to the ETF community.

If factor exposure defines smart beta, then all funds with factor exposure must be smart beta. As you will soon see, most funds have some kind of factor exposure.



In particular, three groups of funds that have factor exposure but don’t seem like smart beta will prove that factor exposure doesn’t work as a smart-beta criterion:

  1. Plain-vanilla size and sector funds often have small-cap or value exposure.
  2. Cap-weighted value funds deliberately access the value premium.
  3. Equal weighting adds size exposure wherever it is used, even in seemingly vanilla funds.

Size And Sector Funds
When the press calls me asking, “What’s the biggest smart-beta fund?” I can’t say, “IWM” (the iShares Russell 2000 ETF (IWM | A-84)), because reporters aren’t looking for a fuddy-duddy, plain-vanilla fund. Yet IWM deliberately accesses the size factor.

As a matter of fact, many plain-vanilla funds carry factor tilts. Sector funds are great examples.

The Utilities Select Sector SPDR Fund’s (XLU | A-94) weighted average market cap of $25 billion (as of April 1, 2014) is one-fifth that of its parent S&P 500 Index of $118 billion. Meanwhile, SSgA’s Energy Select Sector SPDR (XLE | A-96) is more of a value play than the world’s biggest value ETF. XLE’s P/E ratio was 16.7 as of April 1, 2014, versus 17.11 for the iShares Russell 1000 Value ETF (IWD | A-86).

Does anyone think IWM, XLU and XLE are “smart beta” funds?

Until folks cast aside the “anything but cap weighting” definition of smart beta, using factor exposure to define smart beta will create surprising, unacceptable groupings.

XLU’s and XLE’s size or value tilts are knock-on effects, reflective of their sectors. By contrast, FirstTrust and PowerShares have deliberately crafted sector suites, with products like the First Trust Energy AlphaDex (FXN | B-56), which is designed to capture the value and growth factors, in which the tiered-weighting scheme introduces a distinct small-cap tilt. That still doesn’t lessen XLU and XLE’s factor exposure.

Growth And Value Funds
The cap-weighted Russell 1000 Value Index and its counterparts from S&P, MSCI, CRSP and FTSE challenge the conflation of smart beta and factor exposure.

Because these indexes are cap-weighted (sometimes with complex buffering and treatment-of-borderline-security rules), they might not seem like smart beta, especially in contrast to a value-weighted index. But often the results are the same.

IWD and iShares’ MSCI USA Value Factor ETF (VLUE | B-86) each target the value effect, one via security selection, and the other via weighting. VLUE’s and IWD’s portfolios and returns are remarkably similar.

VLUE and IWD share a parent universe of large- and midcap U.S. stocks, represented in Figure 3 by the iShares Russell 1000 ETF (IWB | A-92) and the iShares MSCI USA ETF (EUSA | B-96). VLUE includes all stocks in the MSCI USA Index, but weights them by factors like earnings and book value per share. IWD culls the value stocks from the Russell 1000, then cap-weights them.




Look carefully at Figure 3. Which better captures the value premium: VLUE or IWD?

IWD beats VLUE with the lower P/B—the classic value metric. IWD also has the higher dividend yield, but VLUE sports the lower P/E ratio. Yet these are minor differences. As of April 10, 2014, VLUE’s correlation with IWD is 0.98, with a 0.99 beta. The two are largely indistinguishable, but if I had to pick a value fund, I’d go with IWD.

Security selection can produce powerful portfolio tilts, just as weighting can, because any security not in a port­folio has a weight of zero percent.

If VLUE is a factor fund, then IWD is a factor fund.

Will the ETF community accept cap-weighted growth and value funds like IWD as smart beta? Changing perceptions takes time; I’m not sure the ETF community is ready to put IWD in the same bucket as VLUE. And even if the community were ready to include all the style funds as smart beta, I think there would still be pushback about the equal-weighted industry funds.

Equal-Weighted Funds
With no vestige of cap weighting, and with deliberate index design that promotes the small-cap effect, equal-weighted funds check every smart-beta preconception box.

But some equal-weighted funds don’t seem like smart beta.

Compare SPDR S&P Retail (XRT | A-45) with Guggenheim’s S&P 500 Equal Weight ETF (RSP | A-75). RSP tops nearly everyone’s smart-beta list. XRT, meanwhile, seems like a “plain Jane” retail fund even though XRT—like RSP—is equal weighted. Guggenheim and SSgA have vastly different reasons for equal-weighting their funds, and it shows in the way they market their funds, and, in turn, in the way investors think about these funds.

Guggenheim’s claim that equal weighting improves risk efficiency helps differentiate RSP among the 66 U.S. large-cap funds. RSP’s asset base was about 1/20th the size of SPY’s as of April 2, 2014.

SSgA claims that XRT represents the U.S. retailing industry. Yet XRT tilts much smaller than the overall retail industry. As of April 1, 2014, XRT’s weighted average market cap is about 15 percent of that of all cap-weighted U.S. retailers, according to Thomson Reuters. As long as SSgA says XRT represents U.S. retailers, hardly anyone will think XRT is a smart-beta fund.

XRT and RSP are equally equal weighted. If RSP is smart beta, then XRT must also be smart beta. If XRT is not smart beta, then neither is RSP.

The Vagaries Of Factor Exposure
Factor exposure is not a reliable smart-beta indicator. Many funds have some kind of factor exposure, whether by happenstance (XLU), by design (IWM and IWD) or as a defense against the taxman (XRT). Unless the ETF community is ready to accept these old-line funds in the smart-beta rolls, factor exposure won’t work as a smart-beta criterion—neither in its pure form, nor adapted to account for deliberateness, nor expanded to include alternative weighting.

Neither the anything-but-vanilla approach nor the factor-exposure approach to defining smart beta works across the ETF universe. When you use these criteria to sort U.S. ETFs, you get all kinds of unexpected—and, for many, unwelcome—results.

Perhaps the most reliable smart-beta flag lives in the marketing material, where issuers claim, subtly or boldly, their rules-based designer strategy will outperform a plain-vanilla index on a risk-adjusted basis.

Burying Smart Beta
So far, I’ve laid to rest five of the seven definitions: transparency, rules-following and thematic exposure; alternative weighting; and factor exposure. The last two are superior risk-adjusted returns and improved diversification.

No. 7, diversification, is a mere trifle. The real heart of the matter is testing for excess risk-adjusted returns. Why else would anyone go to the trouble and expense of rearranging a plain-vanilla index?



I will look at the returns of some flagship smart-beta funds, to test whether smart-beta funds deliver risk-adjusted outperformance.

Complex strategies often debut in the crowded U.S. large-cap space; by now, plenty of smarty-pants U.S. large-cap funds have five years of returns history. The U.S. large-cap segment provides plenty of data to test claims that smart-beta funds generate risk-adjusted excess returns.

I used’s database to test 11 widely held U.S. large-cap ETFs with complex strategies, whose marketing material suggests these funds will outperform on a risk-adjusted basis.

Six Feet Under
Whether you look at one-, three- or five-year performance, these 11 U.S. large-cap smart-beta funds have produced returns in line with their risks. No more, no less. Whether you look for statistically significant alpha or Sharpe ratios, there’s virtually no risk-adjusted outperformance to back up the marketing claims.

It’s best to compare apples to apples, so I’ll focus most of my tests on a single equity segment, but I’ll branch out to look at all (nongeared) equity funds.

We’ll measure the risks these funds have taken and the returns they’ve earned relative to a plain-vanilla benchmark through March 31, 2014. I’ll use the MSCI USA Large Cap Index.

I tested one-, three- and, when extant, five-year fund total-return net asset values against gross total index returns. My results are shown in Figures 4a-4c.






The funds appear in order of their total annualized return, from high to low. The MSCI USA Large Cap benchmark data appears in bold.

These funds fit our benchmark well, with all but three of our 28 tests producing a goodness-of-fit over 0.90, and with three-fourths of our test funds hitting 95 percent co-movement. The MSCI USA Large Cap Index is a fair and well-fitting benchmark for these 11 funds.

When goodness-of-fit is this high, multiplying the benchmark’s returns by the fund’s beta gives a reliable predicted return. By extension, the regression’s alpha and its significance should rightly measure each fund’s excess returns.

The 28 tests of one-, three- and five-year alpha significance from our 11 funds produced only two alphas (DLN and SPHB) that are significant at the 95 percent level. One of the two is strongly negative. Except for these two, the headline smart-beta funds had no statistically significant risk-adjusted outperformance on a one-, three- or five-year basis.

Even at the most generous threshold of 90 percent significance, only three funds (one of which did so twice) generated meaningful alpha. The WisdomTree Large Cap Dividend Fund’s (DLN | A-95) produced 2.8 percent alpha over the three-year period and 2.5 percent alpha over the five-year period, and the PowerShares S&P High Quality (SPHQ | A-78) generated 3 percent per year of excess returns over the three-year period. Note that I couldn’t test SPHQ for the five-year period because it changed its underlying index in 2010. The current version of SPHQ was not quite four years old in March 2014.



It Gets Worse
Sharpe ratios, though aligned with alphas, tell an even more drastic story.

Not a single U.S. large-cap headline smart-beta fund produced a Sharpe ratio that’s statistically different from the benchmark—not in one year, three years or five years. That’s zero risk-adjusted outperformance for 11 headline smart-beta funds over the past five years, according to Sharpe ratios.

I wondered if things might be different outside of the U.S. large-cap segment, so I did a quick experiment.

Using’s Fund Finder and Analytics tool, I searched for equity funds with names that contain words associated with self-proclaimed smart-beta strategies: alpha, achievers, beta, dividend, dynamic, earnings, equal, factor, fundamental (or RAFI), income, momentum, quality, revenue, volatility and yield.

I checked how many had statistically significant alpha against a segment-appropriate benchmark, at the 95 percent confidence level.

The answer: fewer than would be expected by chance (see Figure 5).


While claims of risk-adjusted outperformance are probably the most reliable indicator of smart-beta funds, actual fund performance has been in line with risks taken—and for the past five years.

One small note:’s Analytics system benchmarks high-yield dividend funds against MSCI’s High Dividend Yield indexes. This comparison yielded three of the four significant negative three-year alphas, and one of the three negative five-year alphas in Figure 5.

If actual risk-adjusted outperformance defines smart beta, then the smart-beta club will be exclusive indeed, with the vast majority of clever-sounding strategies barred at the gates.

Issuers who are applying smart-beta labels to their fund suites would surely object if they found those funds excluded from a smart-beta list. Therefore, risk-adjusted outperformance as a smart-beta criterion creates groups that are not acceptable to the ETF community.

In other words, risk-adjusted outperformance fails as a smart-beta criterion. Since risk-adjusted outperformance is what investors actually care about, it’s pretty much “game over” for the term “smart beta.” Time to say your goodbyes, because the end will be quick.



Portfolio Concentration
The final criterion, “improves portfolio diversification,” will fail in the same way as alternative weighting, factor exposure and risk-adjusted outperformance.

To measure the extent to which adding a fund to a portfolio increases diversity, we need to know what’s in the portfolio. In the absence of a specific portfolio, the best we can do is to measure concentration within self-proclaimed “smart beta” portfolios. If these funds are less concentrated than their cap-weighted benchmarks, then this criterion will define them well. It turns out that requiring smart-beta funds to have a portfolio that is more diversified than a vanilla benchmark will create fund groups that also are not acceptable to the ETF community.’s Analytics tool measures portfolio concentration using the Herfindahl ratio, which is the sum of the squared weights of each constituent. The Federal Trade Commission uses the Herfindahl index to judge if mergers or acquisitions would produce monopolistic conditions within an industry. uses it to measure portfolio concentration. The higher the Herfindahl ratio, the more concentrated, and the less diversified the portfolio.

Look at the Herfindahl ratios for all the popular U.S. large-cap funds (assets > $50 million) with designer strategies (Figure 6). I’ve ranked funds from high to low concentration.




Of the 17 funds in the sample, all but six are more concentrated than the MSCI USA Large Cap benchmark. The equal-weighted funds are the most diversified, while the dividend funds are surprisingly concentrated.

The PowerShares S&P High Dividend Portfolio (SPHD | A-39), in particular, has serious single-security risk, with allocations of more than 3 percent to at least three different holdings as of April 30, 2014. With this level of concentration, there’s no good evidence that smart-beta funds increase diversification. Put another way, if you required better-than-benchmark diversification as a criterion for sorting smart-beta funds, you would get a quite-restricted list of smart-beta funds—one that cuts out almost all the dividend funds.

Smart-beta criterion No. 7 has fallen, along with its six compatriots. They all failed either because they didn’t make meaningful groupings, or because they made groups that are too controversial to gain acceptance in the ETF community.

Below is a recap of the seven smart-beta definitions and their shortcomings.

  1. Transparency—too broad, fails to make meaningful groups
  2. Rules based/quantitative—too broad, fails to make meaningful groups
  3. Thematic/specific segments or objectives—too broad, fails to make meaningful groups
  4. Noncap weighting—results are unacceptable to the ETF community, because too many oddball funds are included
  5. Captures risk premia/factor exposure—results are unacceptable to the ETF community because factor exposure appears in too many funds
  6. Superior risk-adjusted returns—results are unacceptable to the ETF community because only a tiny fraction of designer funds produce statistically significant excess returns
  7. Improves portfolio diversification—results are unacceptable to the ETF community because many designer funds are highly concentrated

Solving The Problem
Here’s my oh-so-radical solution: Let’s leave behind marketing labels and talk about what funds actually do.

Instead of talking about smart beta, with all the baggage of errant factor exposure and missing excess risk-adjusted returns, let’s talk about the exposures the fund targets, and the process by which they get there. Let dividend funds be dividend funds; let value be value; let momentum be momentum; and low volatility be low volatility.

Let’s talk instead about a fund’s strategy.

Vanilla is a strategy. So are equal weighting, fundamental analysis, duration hedging and commodity futures optimization. Even price weighting is a strategy, albeit an antiquated one.

Strategy gets at the heart of how a fund is built, because we base it on index construction methodology. Strategy cuts away the marketing hype and describes what a fund or an index actually does. has developed a strategy tag for every U.S.-listed equity, fixed-income, commodity and currency ETF, including levered and inverse funds. Strategy assignments are completely rules-based, in keeping with our ETF classification methodology. Want to count the dividend-focused funds, or the buy-writes, momentum-seekers or plain-vanilla funds? No problem.

You can find our list of strategies and their definitions in the appendix of this article.

Which Strategy Is Smart For You?
Once we stop fighting about what’s “smart” and what’s “dumb” and stop looking for risk-adjusted excess returns, we can get down to the business of finding the strategy we want.



Think about dividend funds (Figure 7). There are lots of great reasons to seek dividend-paying securities. Retirees like the cash flows, as do yield-seekers weary of the bond market. I reckon it will be useful to see all the dividend funds in a single list. Figure 7 shows all the dividend funds in the U.S. total market universe.


Not included in Figure 7: Schwab’s US Dividend Equity ETF (SCHD | A-86). SCHD’s underlying index, the Dow Jones U.S. Dividend 100 Index, is more complicated than you might think. It looks for high yields, a history of dividend payment and, critically, “strong relative strength based on select financial ratios.”

Financial ratios are the stuff of fundamental analysis; their inclusion in the DJ US Dividend 100 Index methodology catapults SCHD out of the dividend strategy group and into fundamentals.

Also not on this list: FlexShares’ Quality Dividend Fund (QDF | B-76). QDF belongs not in dividends or fundamentals, but in multifactor, because its index optimizes for management efficiency, profitability, cash flow, dividend yield and beta, with sector, industry, region and style factor constraints.

Compare SCHD’s and QDF’s year-to-date returns with’s dividend funds that select from the total U.S. market. With the exception of cap-weighted VIG, all the dividend funds outperformed SCHD and QDF this year to date (Figure 8).




If you think yield will outperform other fundamental or technical factors, or if income is your top priority, you’ll want to load up on plain-old dividend funds. If you prefer to spread out your bets, a fundamental or multifactor fund like SCHD or QDF could be perfect for your portfolio. It’s all about what’s smart for you.

Labeling and talking about funds by strategy allows us to hone in on the funds we want. It’s much more sophisticated and useful than lumping funds into an ill-defined smart-beta group, and much more reliable than hoping for guidance from a fund’s name.

Comparing Strategies’s strategy label lets us compare strategy results across sectors, countries or time periods. We can dig into a segment and describe its competitive landscape. And we can understand its performance under a variety of market conditions. If a strategy happens to produce significant risk-adjusted alpha, we can talk about why, or why some implementations of the strategy might be working better than others.

Take the U.S. large- and midcap low-volatility funds, for example.

As of May 12, 2014, the oddly named iShares MSCI USA Size Factor ETF (SIZE | B-78), which weights its 600-plus large- and midcap securities to favor low volatility, returned 16.85 percent over the past year. That compares with an 11.07 percent return for the PowerShares S&P 500 Low Volatility Portfolio (SPLV | A-49), which both selects and weights its constituents by low volatility. The SPDR Russell 1000 Low Volatility ETF (LGLV | B-66), which uses an optimizer to select and weight for low volatility, while reducing momentum, beta and turnover, split the difference of the other two ETFs, with returns of 14.77 percent.

Meanwhile, the SPDR S&P 500 ETF (SPY | A-97) bested them all, with a return of 18.40 percent. For the past year, the less-intensely low volatility, the better. Interesting, right?

The buy-write ETFs also tell an interesting tale. Two ETFs write calls on the S&P 500: the PowerShares S&P 500 BuyWrite Portfolio (PBP | C-59) and the Horizons S&P 500 Covered Call (HSPX | C-82). PBP writes at-the-money calls on the S&P index, trading premium income for each month’s potential S&P 500 appreciation. HSPX writes out-of-the-money calls on single stocks, earning less premium income than PBP, but preserving more of the S&P 500 Index’s potential upside.

In 2014’s sideways equity markets, selling premium has paid off. PBP’s year-to-date 4.01 percent return is nearly twice HSPX’s 2.15 percent, or the S&P 500’s 2.40 percent, as of May 16, 2014. If we get a sudden rally, the situation should reverse.

Strategy matters. Smart investors should focus on fund construction, not on marketing claims.’s new Strategy label is a great starting place.

For more information on’s Strategy field and the methodology for classifying funds by that metric, please contact Elisabeth Kashner at [email protected].



For a larger view, please click on the image above.


Find your next ETF

Reset All