- Last week, I complained that mathematical inertia induced by market-capitalization weighting is messing up the SPDR S&P 500 ETF (SPY) through exorbitant exposure to FANMAG
- I have nothing against FANMAG companies per se, but overemphasis is not what passive indexing is supposed to be about
- Smart Beta, which now means pretty much what anybody wants it to mean, may hold the key to a solution — if we return to its (Rob Arnott’s) original definition
- Instead of owning “the market,” with can be bashed by investors’ whims just as easily as it has lately been boosted, let’s instead own “shareholder wealth”
Last week, I took issue with the way mathematical inertia induced by market-capitalization weighting is causing the SPDR S&P 500 ETF (SPY) (ETF Home) to have an extreme concentration in well-known names, mainly those that comprise the popularly-named FANG or FANMAG groupings: Facebook (FB), Amazon (AMZN), Netflix (NFLX), Microsoft (MSFT), Apple (AAPL) and Alphabet a/k/a Google (GOOGL). I see this as a troublesome source of risk. But I can understand where some might argue that these companies are a disproportionate portion of the modern business world and should be exceptionally weighted. The idea is fine, but the implementation is not. If you want an investment case related to business stature, there’s a smarter way to pursue it, namely “smart beta,” an intriguing idea that is often misunderstood and misapplied.
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Smart Beta Defined
Investopedia defines Smart Beta as “a set of investing strategies that emphasize the use of alternative index construction rules to traditional market capitalization-based indices . . . . such as volatility, liquidity, quality, value, size and momentum . . . . linked to a desire for portfolio risk management and diversification along factor dimensions, as well as seeking to enhance risk-adjusted returns above cap-weighted indices.”
The legitimacy of that definition is based not so much on factual accuracy but, rather that the original definition has been so widely and so often misused, that Smart Beta pioneer Rob Arnott (founder of Research Affiliates) waved the rhetorical white flag several years ago, having acknowledged to CNBC that the phrase has “been stretched to encompass just about everything formulaic, with the result that a lot of dumb ideas are being called smart beta. It now spans just about everything, so the term effectively means nothing.”
The classic definition is, actually, quite focused. Smart beta, at least according to its original definition, refers “to valuation-indifferent strategies that break the link between the price of an asset and its weight in the portfolio while retaining most of the positive attributes of passive indexing.” The key phrase is “valuation-indifferent.” Put another way, the portfolio-weighting protocol must not be dependent on stock price movements. That eliminates market capitalization, value, volatility, and momentum weighting from consideration.
While, as Arnott candidly acknowledged, the integrity of the phrase has been hopelessly watered down (so much so, that even Research Affiliates offers a sizable suite of factor strategies under the banner “smart beta” that don’t fit the original definition), what we’re seeing in conventional mega-sized supposedly-passive ETFs like SPY suggests the old definition remains very useful — perhaps especially useful right now given portfolio concentration risks I discussed last week with respect to FANMAG, SPY and SPY-like ETFs.
I did, however, acknowledge last week, that the best argument in favor of the concentration about which I complained would be that the FANMAG and FANMAG-like stocks deserve the weightings they now have because they have become the business word of today. That argument can be quickly defeated by Table 1, which compares the percentage of portfolio assets invested in FANMAG stocks with the percentage of revenue (relative to all other portfolio-owned companies) they account for in SPY and in the broader iShares Russell 3000 ETF (IWV) (ETF Home):
Table 1
FANMAG concentration was more reasonable five tears ago, at least in the context of IWV. But today, with the biggest stocks hogging insanely large portions of important portfolios because of the shadow-momentum built into market capitalization weighting, the investment community really needs to stop taking market cap for granted as the singular standard. The sort of concentration we see in IWV and SPY could be perfectly fine if you are an active momentum-oriented investor. But if the goal is to avoid active decision-making or factor bets and choose instead, passive exposure to the market, ETFs like those are not delivering.
And by the way, if you are inclined to assume FANMAG-stocks will grow into their bloated weighting in the years ahead, recognize that this is indeed, highly active and debatable stance and treat it as such. Don’t lock in on the FANMAG stocks based on inertia. Consider arguments made by people like Catherine Wood, founder of ARK Invest who argues in favor of such areas as artificial intelligence, gene sequencing, robotics, energy storage and blockchain technology. You need to consider her points and really think about whether the FANMAG group will be as out-front five years from now as it is today. Or, perhaps, the market may be showering love on a completely different group of companies. Bottom line: If you really want to be passive, then be passive; FANMAG-centric ETFs are not the way to do that.
Varieties of Classic (Valuation-Indifferent) Smart Beta
The most obvious form of non-market-capitalization weighting is equal weighting, such as what is used for the Invesco S&P 500 Equal Weight ETF (RSP) (ETF Home). This is appropriate if there is no reason to feel more strongly about one stock, as opposed to any other, once it passed whatever hurdles it takes to get into the portfolio. If the stock is worth owning (however that question may be answered), it’s in; otherwise, it’s out. For RSP, there’s one hurdle: being an S&P 500 constituent. For other ETFs the hurdles may relate to screening filters and/or factor models.
Strictly speaking, I suppose we can call this Smart Beta since weighting is valuation indifferent. But I prefer to regard this as honorary smart beta. Genuine smart beta does not deny the usefulness of different weights for different securities. Instead, it aims for a better approach to allocating that is not tied to bona fide fundamentals rather than the whims of the stock market, which, although kind to big-name stocks lately, tends to be fickle.
Without presuming to fully catalog every possible form of even valuation-indifferent smart beta, here are four varieties that have already found their way into the market and are fully investable through ETFs:
- Revenue Weighting: This is very straightforward. Instead of weighting securities on the basis of market capitalization, the portfolio weights on the basis of revenues. So, for example, the revenues for Company A equal 4% of the sum of all the revenues for all the securities owned by the ETF, then A will have a target weight of 4%. This will be so whether A’s market cap is 1% of the total, 4% of the total, 25% of the total, etc. This provides the most economically clean version of what defenders of FANMAG-heavy portfolios think they’re getting. Revenue is the best proxy for a firm’s contribution to GDP since both metrics arguably put a dollar value on production of goods and services.
- Earnings Weighting: As equity investors know, revenue alone does not tells us everything we need or want to know about a company’s fundamental stature. Investors ultimately aim at shareholder wealth and there are many ways to measure it. Earnings is a well-accepted alternative. The logic makes sense — economic value should be measured after allowing for expenses. The challenge, though, is that not all kinds of expenses are reasonably persistent so there can be some volatility. And earning-based measure also brings accounting practices into play.
- Fundamental Weighting: Ultimately, equity investors aim at shareholder wealth, and there are many ways to measure it. The RAFI approach (from Research Affiliates) looks to a combination of such measures as Revenues, Operating Profits, Cash Flow, Cash delivered to shareholders (dividend payments plus amounts spent to repurchase stock) and Book Value, which is not quite as stale a metric as some, even one as notable as Warren Buffet, supposes.
- Dividend Weighting: This approach, popularized by Wisdom Tree, shares with Revenue weighting the virtue of simplicity. But like Fundamental Weighting, it does not wish to go all in on the top line of the income statement. It actually goes to the other extreme, even below the so-called “bottom line” (net income) and looks only at the portion of net income that gets distributed to shareholders as dividends.
Like market cap weighting, smart beta is premised on the notion that bigger is better. But with market cap, the definition of bigger is soft — it has much and sometimes everything to do with Wall Street sentiment and at times a strained relationship to economic fact.
Performance Caveat
We all love to beat the market and there’s a natural tendency to gravitate toward managers, advisers, ETFs, etc. that show a strong track record of having done so in the past. And in the quant world, the holy grail is “robustness,” statistically significant evidence that an approach has been successful in a large variety of contexts (time periods, regions, etc.). Ideal and reality, however, tend to differ. Often, what starts out looking robust turns out to have been little more than the good fortune to have done all measuring within the context of a longer-than-realized market “regime” (a period of time when some worthy investment factors pay off and others don’t).
In recent years, outperformance has required affinity for the momentum implications of cap-weighing. Hence we’ve come through a very long regime in which the market heavily rewarded the very thing classic smart beta seeks to avoid.
So don’t buy or own a smart beta ETF today because you’re impressed with what it has accomplished in the past. It’s not likely to look special. Own smart beta, if at all, because you believe in the merits of economically-sensible weighting and are willing to presume that Mr. Market’s present ambivalence in this regard won’t persist indefinitely.
Four Smart Beta Ideas
Not all size categories have ETFs available representing all varieties of smart beta. Ideally, true economic passivity would require the broadest possible selection basket. But ETF product lines evolve over time and at present, the large-cap segment is the one that offers us the best opportunity to find a variety of smart beta offerings. So here are four large-cap ETFs, one for each of the smart beta protocols mentioned above.
Wisdom Tree U.S. LargeCap ETF (EPS) (ETF Home)
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As you might have guessed from the ticker, this is an earnings-weighted ETF. Below is a top-10 table that shows the ten largest holdings for EPS and for comparative purposes, SPY and RSP (comparisons with the latter two will be shown for the other ETFs to be presented below.)
As with SPY, the leading positions here are MSFT and AAPL. The combined weights are a little lower (8.25% here and 10.74% for SPY). There’s also little difference in the top-heaviness of the portfolios; the share of assets invested in the largest holdings.
There are, however, noteworthy differences below the top two, and across the board in how the holdings got to be as large as they are, and also in the potential future stability of the positions we see. These companies dominate in terms of earnings, which depend on the sale of goods and services, not market sentiment. This is not to say that earnings can’t bounce around. But at least such fluctuations aren’t compounded by changes in sentiment that lead to changes in valuation ratios.
Invesco S&P 500 Revenue ETF (RWL) (ETF Home)
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When we turn to RWL, the revenue-weighted ETF, we see an interesting difference. The top position here is Walmart (WMT) which is definitely an anti-FANMAG. Numbers two and three however, are from FANMAG. But even focusing on WMT and AMZN, which combine for a weighting of 8.51%, we’re starting to see something that makes sense if we focus not so much on stock-market phenomenon but on aggregate business activity — a big role for product distribution to consumers and growing roles for service distribution. Going down the list, we see the core-distribution theme expanding as we factor in United HealthGroup (UNH) and Berkshire Hathaway (BRK.B), which besides being a celebrity vehicle, is a large insurer. As we look at the entire top 10, and compare with SPY, we see, for RWL, not representation of emphasis on Wall Street themes, but the outlines of representation of the business world itself, as manifested through money, not rhetoric.
Notice, by the way, that we can draw no conclusions at all about RSP expect that the top 10 positions are those that got there through decimal rounding and between-rebalance-dates market movements. Equal weighting doesn’t depict a theme. It is the theme.
Invesco RAFI Strategic US ETF (IUS) (ETF Home)
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This is an example of multi-pronged RAFI (Research Affiliates) fundamental weighting. Specifically for this ETF, each company gets :
- A business-size score based on the equally-weighted average of sales, operating cash flow, return of capital and book value over the prior five years or life of the security (with some adjustments for real estate firms that have different relevant metrics), and
- A quality score that is the equally-weighted average of the prior year’s ratio of sales-to-assets, and the five-year percent change in the sales-to-assets ratio
The selection process then weeds out the bottom 10% of the starting universe in terms of business-size score. The next step is to eliminate from the business-score-eligible stocks those that fall into the bottom 20% in terms of quality score.
The remaining stocks, those that will go into the ETF portfolio and be weighted on the basis of business score.
Given that this is a more detailed algorithm, it’s hard the characterize the top 10 here, which at first glance looks a bit like SPY. But I can’t really call it an SPY clone, or even a near-SPY clone. I might instead describe this as a substantive shareholder wealth-based portfolio. With no single metric being a perfect measure of corporate wealth creation, a good case can be made for the multiple factors used here.
Wisdom Tree U.S. LargeCap Dividend ETF (DLN) (ETF Home)
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Although Wisdom Tree is an early smart-beta leader, this ETF really presents as something very different. To build this portfolio, Wisdom Tree screens for the 300 largest dividend-paying stocks and then weights by dividends; not yield, mind you but indicated annual dividend per share multiplied by the number of shares outstanding. This is not overtly designed as a yield-oriented ETF, but it does turn out that way. This ETF, has a yield of 2.87% at present (the second highest yield among those discussed here today, RWL, is 2.24%) and is part of our Dividend Yield ETF Group, as opposed to Large Cap Blend
Given that investors are not accustomed to thinking in terms of dividend paid, it’s hard to characterize the largest DLN holdings.
Let’s say its an alternative version of a shareholder wealth portfolio, a more conservative one since it uses a singular and particularly strict measure of wealth generation.
Conclusion
All of these ETF have Neutral Power Ranks and decent but unexceptional Group ranks. That’s fine. We’re not aiming to be world beaters here. These are offered as passive no-factor-bet, no-style-bet etc. ways to own the U.S. equity market. So for this purpose, these rank profiles seem right.
Here are some core comparison, from PortfolioWise, of the smart beta ETFs discussed here.
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IUS hasn’t been around long enough to definitively characterize its track record relative to this smart-beta peer group (I may not care about smart beta relative to SPY, but it’s is interesting to see how these ETFs compare to one another). But given my lengthy career doing stock-specific fundamental analysis, I’m most comfortable with its balanced multi-prong measure of shareholder wealth. So that’s my top pick today. I also have a conservative-income oriented streak and like DLN too.
Long DLN, IUS (with funds raised by selling out of SPY)
Marc Gerstein, Director of Research at Chaikin Analytics, is an oddball sort of quant. He has long specialized in rules/factor-based equity investing strategies and has been addicted to stock screening since the days when the program was loaded into a pc on a 5 1/4” floppy disc that went into Drive A and the disc holding data went into Drive B. But he hates fancy math has no use for by-now stale “factor” worship. He favors theoretically sound quant approaches, such as the Chaikin Power Gauge model, that generate active, actionable ideas for the real world. In his spare time, he tries to dull the pain of following the NY Jets and Knicks with reality TV and literature. (We have quantamental, so why not literatrash?)
Twitter: @MHGerstein
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