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Marc H. Gerstein ( or if already on Twitter, search for @MHGerstein) is an independent investment analyst/consultant specializing in rules-based equity and ETF investing strategies, with particular emphasis on small-cap equities and leveraged ETFs. Many of is views... More
My company:
Portfolio123 and Ariston Advisors
My blog:
Forbes Low-Priced Stock Report
My book:
Screening The Market
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  • Carving Smart Alpha From The S&P 500


    It's a challenge to generate alpha under any conditions, but limiting consideration to S&P 500 constituent stocks magnifies the task. Yet this stock group is too large and liquid to ignore. So it can pay to keep trying. While I can't comment on the efficacy of the dramatically increased computer power some are marshaling to process information and trade within nanoseconds, I have noticed that plain-vanilla basics can be surprisingly productive.

    Feeling Blue Battling the Blue Chips

    If you find it hard to beat the blue-chip index, it's not just you. There are bona fide reasons why it's harder to pick successfully among the stock market's titans.

    In a fascinating 1984 paper introducing behavioral elements into our understanding of price-setting in the stock market, Nobel Laureate Robert Schiller expressed total demand for stocks as based on demand from knowledgeable investors, those who aim at objectively-determined valuations, and ordinary investors, those who don't. Stanford's Dr. Charles M.C. Lee re-labeled these "value" (NYSE:V) and "noise" (NYSE:N) and stated that P, price, is equal to V plus N.

    Of interest to us right now are the factors that determine how important noise is. A huge consideration in diminishing the role of noise is information costs. In practical terms, this addresses the quantity and quality of company disclosure (the extent to which a company will provide more and better information than the minimum required by law), the number of analysts watching and analyzing, the extent of media coverage, and the number of investors watching and acting upon newly disclosed data and opinions, and even the nature of the business, the extent to which it's even possible to attempt to apply a valuation model to the stock. The more available information is, i.e. the lower the information costs, the less influence noise (factors other than rational credible valuation) will have on a stock price. While reasonable but differing ideas about valuation can produce alpha, noise is much more likely to offer a more fruitful hunting ground. And there is often much less noise among the most closely-watched intensively-analyzed stocks on the planet. Another factor, trading costs, also tends to depress noise among S&P 500 constituents, which can be traded with minimal slippage.

    Cherrypicking the Blue Chips: a Portfolio123 Ready-to-Go Model

    There is more than one way to accomplish anything in the investing arena, so the Portfolio123 Ready-to-Go Model I'm introducing here is by no means THE answer. It's just one answer - so far. Nothing is forever, but I've been succeeding with this with real money since April 9, 2013. You can follow it here if you register for a free membership on Portfolio123. (You can also be notified of trades as they are made if you follow me on Twitter at @MHGerstein.)

    Here's how the model works:

    · It looks for the simultaneous presence of two characteristics that might strike many as antagonistic; value and sentiment (in the context of the above behavioral references, we could see the latter as a proxy for market noise, not as much noise as might influence smaller-capitalization issues, but as much noise as we're likely to see among the blue chips).

    · Value and sentiment each serve as a check against potentially troublesome excesses in the other. Requiring favorable analyst sentiment guards against value traps, situations where low valuation metrics reflect the market's pessimistic judgments about the future. Requiring favorable valuation characteristics guards against excesses that may accompany overly buoyant sentiment.

    · The Value ranking system that's used here is based on PE, PEG, Price/Sales, Price/Free Cash Flow and Price/Book.

    · The Sentiment model is based on EPS Estimate Revision, Earnings Surprise, Analyst Ratings and Changes in Analyst Ratings.

    · Value and Sentiment ratings of 80 or better (on a zero to 100 scale) are required for a stock to be eligible for inclusion in the portfolio.

    · From among stocks that pass the value and sentiment thresholds, the portfolio buys the ten that rank highest under a balanced Quality-Value-Growth-Momentum ranking system.

    · The Value component of that model is as described above.

    · The Quality model is based on Margin, Turnover, Financial Strength and Return on Capital.

    · The Growth model is based on historic EPS and Sales growth over various periods.

    · The Momentum ranking system is based on relative share-price strength over various six- to nine-month intervals and indications of recent strength in volume.

    · The model is refreshed weekly (on Monday morning) and stocks are sold if there is deterioration (even very modest deterioration) in analyst sentiment (i.e. if the Sentiment rank falls to 70 or lower).

    Performance Since Live Launch ("Out of Sample")

    Since the positions in the portfolio are usually close to being equally weighted, I track performance against the S&P 500 Equal Weight Index. The results are summarized in Figures 1 and 2, from Portfolio123.

    Figure 1

    Figure 2

    (click to enlarge)

    Portfolio Positions

    After the portfolio was refreshed on 8/17/15, the ten holdings were as follows:



    % Wt.


    Anthem Inc



    Bank of New York Mellon Corp (The)



    Cincinnati Financial Corp



    D.R. Horton Inc.



    Goodyear Tire & Rubber Co



    Hartford Financial Services Group Inc. (The)



    Lam Research Corp



    PVH Corp



    Tesoro Corp



    Valero Energy Corp


    Many have long been accustomed to using lists like this, often based on stock screens, as time savers that narrow the number of stocks one should research the aim being to select just a few. I too used to work that way. But when testing became more readily available, I changed course and now purchase the entire list, defining my idea as the strategy, rather than any individual stock. This is an important distinction, one that can play a huge role in the ultimate success or failure of an investment effort.

    I'll discuss this further in subsequent posts, but for now, suffice it to say that if you want to benefit from the overall elements of the strategy, you'll want to own a portfolio that, on average, delivers the characteristics the model sought. Aberrations are a fact of life, so it's important to diversify them away. (The so-called official justification for diversification is to reduce volatility. That's fine as far as it goes. But there are other important reasons for diversification that have nothing to d with stock volatility. Mitigating data oddities, situations where a company may follow the letter of the law of a model but violate its spirit, is a crucial consideration.)

    Still, it is important for investors to understand the kinds of stocks called for by particular smart-alpha protocols. So separate posts will spotlight representative issues appearing in this model, as well as other models that will be introduced here in the future.

    Aug 20 4:54 PM | Link | Comment!
  • Smart Alpha: Smart Beta’s Smarter Cousin


    Smart Beta has been getting a lot of play lately. As mentioned my last post, it's a good idea wrapped in a silly label. It's really an aspect of smart alpha, something some theoreticians will tell you, based on their research, can't exist but which others will tell you, based on the money in their brokerage accounts, that it's very much for real.

    Market Efficiency: Says Who!

    The case against smart alpha is tied to the case against any sort of alpha (a return greater than what's expected based on market performance and the level of risk that is assumed). It's the idea that stock prices reflect all available information and that absent occasional instances of luck, nobody can consistently earn excess returns. Adherents to this idea go on to argue that money management is useless, that asset management fees are rip-offs, and that everybody should passively invest in the SPDR S&P 500 ETF ($SPY) and be done with it.

    Interestingly, they never explain what makes this a passive choice. It seems to me like an active bet on U.S. large-cap value-momentum stocks, in contrast to what you might get by picking up the smaller cap iShares Russell 2000 ETF ($IWM), more complete size-oriented market exposure through the iShares Russell 3000 ETF ($IWV) or the iShares S&P 1500 ETF ($ITOT), or a Wisdom Tree or RAFI ETF that strips the momentum bias out of the market cap weighted indexes. (I can go on and on, but I think you're likely getting the point. "Passive Investing" doesn't exist, unless you want to apply the label to stuffing money under the mattress). The idea of "passive" Investing is useful only to fund companies pushing ETFs based on the most well-known indexes or researchers trying to explain why THEY haven't been able to come up with a way to beat the market.

    Let's try this from a different angle. It's impossible for everybody to be above average. So, the passive advocates argue, focus on average.

    Yes, that is true. The market is a zero sum game and ultimately, it is all about being average. But that applies only to the total of the gazillion investors that make up the whole market, or as a former boss once put it, God's investment portfolio. That doesn't prevent you from trying to be one of those who is above average. Anybody who thinks this is unreasonable because there's no room for everybody to do it really needs to get out more. Trust me, most of the world is not going to strive to be above average, at anything.

    I've stalled long enough. It's now time to get to the most indelicate argument, one hinted at above and which sounds so nasty, but is really the one that's most valid. I'll address this to the many who can and do publish articles and studies demonstrating money management's historic the lack of success. The logic of this "research" violates a well-known and well-established logical fallacy, argumentum ad ignorantiam, which holds that a proposition cannot be deemed false merely because it hasn't been proven true. In other words, these studies only prove that the authors (and those with whom they worked and who they may have surveyed) have been unable to figure out how to generate alpha. But they cannot presume that others are unable to do so.

    We're Already Seeing Some Market Inefficiency and Alpha

    I screened the database, I found that out of 8,428 U.S. open-end equity mutual funds, 3,363 have succeeded in generating three-year alpha above zero. Of those, 2,038 generated alphas at an annual rate above 1%. Going to the Portfolio123 ETF Screener and searching among U.S. Equity ETFs that openly strive to improve on plain-vanilla equity indexes (those whose Method is listed as "Quant Model"), I saw that 14 out of 109 have successfully generated positive five-year alpha, and that 12 out of 68 so-called-Smart Beta ("Special Weights") ETFs also generated positive Alpha.

    OK. I get it. Those are three and five year track records. What about 20-year histories? What about 50-year histories? What about 346-year histories? I don't know. Ask me in 20 years, in 50 years (or rather ask my grandkids), or in 346 years. We can only work with what we have. And what we have is a new set of analytic tools that enables investment strategists to work in ways that were impossible or largely inaccessible to the generations of researchers and money managers whose historic performance woes have been well documented. The situation is analogous to human flight. It was well known and conclusively proven to have been absolutely impossible - until barely a century ago, when somebody figured out that wing surfaces should be curved. The investment community's curved-wing innovation consists of modern databases and the analytical platforms that empower us to model using the data.

    Consider an idea as simple as this: I want stocks for which the Price Earnings ratio is less than the average of other companies in the same industry. Imagine what it would have taken for Graham & Dodd to crunch numbers and arrive at an answer. Start by imagining the nightmarish task of even collect the numbers, since they'd probably have had to wait for each company to mail its financials to them, and probably by third class mail (as was typical among companies back as recently as the early 1980s, when I started working at Value Line). And once they finish (ugh, grunt) they have to almost immediately start over as prices move and new earnings reports are issued. Note, too, that they'd need to sort, identify, collect, and crunch PEs for each company in the same industry. And all that is for just one little simple measly relative PE ratio. I couldn't imagine what it would take to add consideration of sales growth, margins, turnover, balance sheets, earnings quality, return on investment, and so forth. Note, too that a lot of academic research is based on annual data, which is stale for most of the year, and whatever manual crunching PhD students can be browbeat into doing.

    And yes, the percentage of funds that have produced positive lately is low. But this whole area of quantitative fundamental research based on accounting data (where we might model based on accruals to assets rather than physics-like concepts such as Brownian motion) is very, very new. I can't show 20-plus years of success because we don't yet have enough investors who have been doing it that long. But personally, I'd be embarrassed to count myself among those who say it can't be done because . . . well, because.

    Based on Financial Theory, Not Alchemy

    It would be reasonable to wonder whether the promise of a new set of tools will actually be realized. Just because you give somebody the ability to do something better doesn't mean they'll successfully take advantage of it. There have recently been published articles gleefully pointing out the missteps of less-capable practitioners (which can be fun to write and which can draw lots of eyeballs if the headline is sufficiently enticing).

    I'm not going to play that game. I'd rather help you form your own opinions by showing you, up close, what the quest for alpha is really about, what it looks like from the vantage point of those who do it.

    There's no mystery at all to how stocks are priced. We know the answer with complete certainty. A stock is worth the present value of future expected dividends. And theoreticians should agree here since this is, after all, an academic concept. The reason we don't all succeed with every investment is because of the difficulties in articulating the required inputs. In fact, none of the inputs can be articulated with any reasonable degree of certainty. But we can and do look to the available data for clues that make it more probable than not that a stock is priced more in line than not with this theoretical target. That is what fundamental analysis is all about. Technical analysis involves piggybacking on the price and volume movements caused by the shifting opinions and actions of those who've done this sort of fundamental analysis. And there's a branch of sentiment, or behavioral analysis that can be done to assess the impact of those who trade (and impact stock prices) based on factors having nothing to do with objectively assessed valuation.

    Legitimate practitioners who work to generate alpha are not flipping and flapping this way and that way until they do the financial equivalent of turning straw into gold. We are working, albeit in new more modern and efficient ways, with classic investment finance theory. Tis can best be seen with a simple demonstration.

    Smart Alpha in Action

    As a quick example, I created a Portfolio123 screen that identified Russell 3000 constituent stocks for which the forward-looking PEG ratio (PE using the current-year estimate as E and the consensus long-term growth-rate forecast as G) was in the cheapest 20% relative PEG ratios of other stocks in the same industry. From among the 235 stocks that satisfied this requirement, I selected the 25 that ranked highest in a sort based on trailing-12-month return on equity. I backtested against an ETF that gives passive exposure to the Russell 3000 ($IWV) and assumed the equally-weighted portfolio would be refreshed based on a new run of the model every four weeks. I tested over the past 10 years and assumed price slippage of 0.25% for each buy and sell/trade. The hypothetical portfolio achieved a simulated annual alpha of 1.36%. (I'm aware that the portfolio is equally weighted while the benchmark is market-cap weighted. So I checked by creating another portfolio that included all Russell 3000 stocks on an equal-weighted basis. The latter showed a minus 0.54% annual alpha.)

    I'm not rushing out to invest real money based on this model. Performance relative to the market is somewhat less stable than I'd want to see. But I've little doubt the model could be tweaked to the point where it would be more readily usable because of my confidence in the logic behind the selection of factors, as well as my experience investing based on other models built on the basis of similar ideas.

    And speaking of the ideas and how they relate to theory, here they are:

    · I start knowing I want to be in reasonably liquid (i.e. not penny) stocks that are properly priced relative to the present value of future dividends. Knowing that I can't specifically execute that calculation, I try to identify stocks for which there's reason to believe the price may be reasonably, if not precisely, aligned with that ideal (and in the case of non-dividend payers, we look for firms with current characteristics that support projecting into a theoretical future when dividends would, presumably, be paid).

    · A stock with a low PE stands a better chance of being aligned with future dividends because dividends (future if not presently) come from earnings.

    · A stock that's reasonably priced relative to the company's earnings growth rate stands a better chance if making the grade since dividend growth is an important part of the core academic present-value model.

    · Since stocks are valued with respect to future dividends rather than past achievements, I choose to use forward-looking numbers for the E and G in PEG, which is not something that is universally done.

    · There really isn't a serious reason for putting the PEG threshold at 1, as many do, or any other number. That's why I choose to sort relative to industry peers. I'm looking for situations that are potentially attractive on their own, rather than because of a rising-tide-lifts-all-boats phenomenon.

    · The final sort, based on recent return on equity, is motivated by this metric's

    stature as the single best measure of company quality and more particularly, the company's ability to generate good profit growth in the future (which, of course, is logically tied to good future dividend growth).

    · I select 25 stocks because that's a number that diversifies me in a way that mitigates data risk (the risk that an oddity in a company's numbers will cause PEG or ROE to have a real-world meaning that differs from the spirit of the law; a topic known to quants as the "mis-specified model" further discussion of which will/be deferred for another day) without presenting me, as an individual, with undue trading burden.

    · Finally, I rebalance every four weeks, an interval/that strikes a good balance between my wanting to use data that's reasonably fresh with the need to give ideas tome to work (we can transmit information in nanoseconds, but it still takes time for investment cases to get reflected in the market).

    There you have it. That's an example of the way a smart alpha protocol can come into being. No magic. No tealeaf reading. No chanting or spells. No physics. No rocket science. No fancy math. It's plain old-fashioned logic consistent with common sense and bedrock investment theory. I would feel absolutely zero jitters about sitting down, if possible, with Graham, Dodd, Buffett or anybody and discussing this. And I already know the kinds of modifications they'd suggest (after all, this is just something I created in less than a minute for purposes of demonstration).

    You'll notice I made reference to a backtest. So, too, do some other smart-alpha strategies. Understandably, that can raise some concerns. Testing is a vital process but one that can be misused. But that's so with every endeavor and in every profession. The hallmarks of a professionally proper test are:

    1. Use of a point-in-time database that eliminates survivorship bias and look-ahead bias (in other words, companies that vanish due to bankruptcy, acquisition, etc. aren't retroactively pulled from the database but are included up until the day their shares stopped trading, and data is made available to the test only when it became available to investors, so fourth quarter numbers aren't available to the test on January 1st). We use a point-in-time database on Portfolio123. If you're looking at a Smart Alpha test results elsewhere, ask about it, and feel free to draw negative conclusions from a non-response.

    2 A well-articulated strategy that rests on the logic behind why some stocks should perform better than others. The purpose of testing is not to discover "what works" (or, rather, what just so happened to have worked in a particular study period maybe through substance and maybe through luck, a disreputable practice known as curve-fitting, data-mining) but to test the efficacy of the strategy developer's effort to translate the ideas into language that can be read by a computer and processed using a database. In other words, smart-alpha strategy development is not so much a statistical process as it is an exercise in language translation. When you encounter ETFs and so forth that use smart alpha, note that the providers have a right to protect intellectual property (the way they translated ideas into computer-speak) so don't expect as much detail as I supplied here in this demonstration. What you're looking for is clear-cut indication that the strategy springs from reputable ideas.

    I'll present and maintain for you some genuine smart-alpha models starting next week.

    What Makes Alpha Smart

    For a definition of Smart Alpha, I'm going to start with a characterization by Bruce J. Jacobs and Kenneth N. Levy and their Invited Editorial Comment "Smart Alpha versus Smart Beta" the Summer 2014 issue of Journal of Portfolio Management (p.1), where they say describe the approach as one that "rests on the proposition that the equity market is not entirely efficient, that security prices are subject to a large number of interrelated inefficiencies, and that it is possible, although not easy, to detect and exploit these inefficiencies with proprietary factors."

    Sometimes these factors can be complex. Often, though, they can be quite simple, as per the above demonstration, with the proprietary element being the decision to choose and combine these particular factors in lieu of an infinite number of possibilities.

    Ultimately I'll say the difference between regular alpha and smart alpha turns on whether the alpha we see is simply what we computed after subtracting Expected Return from Realized Return, or whether the alpha is linked to a thoughtful valid strategy as discussed above. Smart Alpha is the latter.

    Where Smart Beta Fits

    Smart Beta's place is in the world of marketing and public relations. Period.

    The weighting protocols used by those who market under that label, however, fall squarely within the world of smart alpha.

    In the above demo, I chose stocks on the basis of meeting certain thresholds relating to PEG and ROE. A small number of stocks (25) made the grade. Most didn't.

    Suppose, on the other hand, I want to launch an ETF based on my ideas. Liquidity and asset-gathering considerations suggest 25 stocks may not be enough. I may, therefore, carve out a bigger chunk of the Russell 3000, or even the entire constituent list, and apply a RAFI-like fundamental-weighting protocol. In my case, the fundamental score would be based on PEG relative to the industry norm, and trailing-12-month ROE.

    That's all there is to the difference. In one case, I use my ideas to select or reject stocks. In the other case, I use my ideas to determine how much money within a portfolio should be allocated to particular stocks with larger weightings going to those that rank higher in terms of compliance. Either way, the success or failure of the portfolio is going to be governed by the efficacy of my decision to drive performance on the basis of stocks with particular exposure to low relative PEG and high relative ROE. The differences are in the details of implementation, differences that are quite logical considering the difference between a portfolio for an individual (such as are offered in Portfolio123 Ready-to-Go) or an ETF marketed broadly to the public at large.

    Appendix - For Quants

    Shortly before completing this post, I fielded an interesting question from an investment advisor who showed one of my alpha-producing models to a quant with whom we are both acquainted. The latter asked what the "residual error" was.

    To translate this to plain English, let's restate the Capital Asset Pricing Model which equates expected return(ER) to the risk-free rate (NYSE:RF), Beta (NYSE:B) and the equity-risk premium. In an earlier post, I expressed the model as:

    · ER = RF + (B * RP)

    Actually, a serious quant would have phrased it thusly:

    · ER = RF + (B * RP) + e

    The last add-on, e, is the residual error term and is appended to any model in this form. Ideally, if the model truly explains the market as a whole, e will be zero or an insignificant element of randomness. But e in this model is not random and not zero. That's why Fama French were able to expand it to also include RP-like factors representing the small-cap effect and stock valuation (each with their own B-like coefficients).

    The work I do does not purport to explain the market as a whole. So I don't care about minimizing e. If the model I create is generating alpha for me, I'm satisfied, and remain so even if other different models, also generate alpha. If I think the other factors subsumed by e can also produce alpha (which is usually the case), then I build other models based on them. And rather than investing all my money based on one model, I "diversify" (call it intellectual diversification) by having other portfolios based on other models. And it's why I'll present and maintain multiple models (stock lists) to and for you, rather than one grand Gerstein model.

    Tags: Smart Alpha
    Aug 18 5:33 PM | Link | Comment!
  • Smart Beta Is A Silly Label But A Sensible Idea


    I just Googled the phrase "Smart Beta," and the search engine reported 65.1 million hits in 0.28 seconds. So whether or not people know what this means, I presume many have one way or another come into contact with the phrase.

    Can Beta Really Be Smart?

    The definition of "Beta" is clear. Its' an index of risk (i.e. volatility) relative to the market with high Betas signifying more risk and vice versa.

    "Smart Beta" is . . . uh . . . hmm . . . well . . . aw heck. I give up. I have no idea what makes Beta smart or dumb. So I'll just tag along with what Investopedia says: "Investment managers that seek to follow a smart beta investing strategy seek to passively follow indexes, while taking into account alternative weighting schemes such as volatility." (Oddly, as of this morning, nobody had thought to create a Wikipedia page explaining the term.)

    Despite the clarity of the definition as presented on Investopeidaand the many other sources that are consistent with it, I wonder if there's something I'm missing. I really wish I could see some sort of logical relationship with plain ordinary Beta. I stopped wondering, though, when I found an August 2014 article quoting Nobel Laureate William Sharpe, one of the originators of the capital asset pricing model, the thing that beta is designed for, as having said that the phrase smart beta makes him "definitionally sick."

    That settles it. If William Shapre can dis the term, so can I. So I'll say straight out it's a cute marketing handle. In the Portfolio123 ETF screener, we use a more descriptive phrase to help users identify these funds. When screening ETFs based on Method, the funds labeled Smart Beta can be found under the more informative but less snazzy category known as "Special Weights."

    Wisdom Tree and Research Affiliates (OTCPK:RAFI) are the biggies in this field. Both firms involve high-profile names (Jeremy Siegel in the case of Wisdom Tree, and Rob Arnott and Jason Hsu in the case of RAFI), and both are known for particular weighting protocols. Wisdom Tree got famous for dividend weighting: Instead of weighting stocks based on market capitalization, as is done in the S&P 500 and most well-known indexes, it weights by the amount of dividends the companies pay (not necessarily yield, it's dollars paid). At RAFI, stocks in a portfolio are weighted not by market capitalization, but by a proprietary formula that computes and weighs based on "composite of fundamental factors, including total cash dividends, free cash flow, total sales and book value."

    I'll simplify things. Both firms are saying bigger is better (i.e. they want bigger issues to play more important roles in their portfolios and indexes) but that unlike S&P and the rest of the establishment crowd, they are defining big on the basis of something other than market capitalization.

    Here's the Beef

    Although this alternative weighting idea has absolutely nothing to do with any sort of Beta (hence my sympathy for Sharpe's sense of definitional sickness and my having labeled the term a marketing gimmick), I think the idea is terrific. The aim here is to define size (and base index/portfolio allocations) on something truly substantive, something that does not depend on the stock price, which can bounce around out of proportion to rhyme or reason. Let's see an example of this.

    Table 1 - April




    Sales ($ mill.)



    No. Shares (mill.)



    Stock Price



    Market Cap. ($ mill.)



    Price/Sales ratio





    Market Cap Weighted



    Sales Weighted



    That's fine. It makes little difference here which weighting scheme we use. Either way, we're investing a bit more than twice as much money in XYZ because it's a bit more than twice as large.

    Suppose a month later, there is no significant change in the fundamentals of either company and that the price of ABC has remained constant. But XYZ raised guidance by a penny a share, so the stock soared and is now up to $12. Watch what happens to the weightings.

    Table 2 - May




    Sales ($ mill.)



    No. Shares (mill.)



    Stock Price



    Market Cap. ($ mill.)



    Price/Sales ratio





    Market Cap Weighted



    Sales Weighted



    We've now got a noticeably bigger allocation in XYZ. That alone raises an eyebrow since the size of XYZ Company has not changed relative to ABC. Only market sentiment has changed. But wait. That's not all. We're now more exposed to valuation risk. Not only did the Price/Sales (PS) ratio rise for XYZ, so, too, did its allocation. We started with a portfolio average PS of 1.26, but if we're market cap weighted, our average PS is now 1.75.

    Suppose in the next month, an analyst raises XYZ from Buy to Strong Buy, and Mr. Market celebrates by bidding the stock up to $17.

    Table 3 - May




    Sales ($ mill.)



    No. Shares (mill.)



    Stock Price



    Market Cap. ($ mill.)



    Price/Sales ratio





    Market Cap Weighted



    Sales Weighted



    Now, the amount allocated to XYZ, which started as being a little more than double the allocation to ABC, is well more than triple. And our now-severe overweighting is occurring in a more seriously overvalued security, one with a PS that stands at 2.68. On a portfolio average basis (assuming market cap weighting) the PS is 2.45. Two months ago, before XYZ rallied, the portfolio PS was just 1.26.

    So we've dramatically lessened our diversification and exposed ourselves to a lot more valuation risk, not because of any meaningful change in either company, but strictly because of Mt. Market's exaggerated reaction to minor developments at XYZ. For an example let's look at what happened in 2000-2002.

    How this Played Out in the Real World

    Who among those who was in the market in 2000-02 can ever forget what happened back then. Interestingly, though, many got through it, if not unscathed, at least with less painful bruises. That bear market to some degree reflected an earnings recession but to a much-larger degree, was caused by a valuation crisis. Those who invested in market cap weighted indexes put themselves in much worse positions as a result of the dynamics illustrated in the above Tables.

    We remember, for example, the disastrous mess that was the NASDAQ Composite. It fell about 72% from 3/1/00 through 1/2/03. Yet during that same period, an equally weighted portfolio of reasonably tradable NASDAQ stocks fell 43%. While the names in my hypothetical portfolio don't necessarily completely match the constituents of the Composite, they're close enough to demonstrate the huge penalty paid simply for having weighted stocks by market capitalization.

    The same was true for the S&P 500. The capitalization weighted SPDR S&P 500 ETF ($SPY) dropped 32%, a big fall for such a "blue-chip" group. But a portfolio that held those same stocks in equal weights would have - get ready for this - posted a plus 2% return. Yeah. Market cap weight if you want to. But Wisdom Tree and RAFI are right. You might wind up paying a big penalty for the privilege of doing so.

    Of course cap weighting works in reverse, and to your advantage, in an aggressive bull market. But if you were in stocks back then, which period made a more lasting mark: 1997-2000, or 2000-2003? If you want to cap weight, you really should be a good market timer. Speaking for myself, I'm not.

    It's Not Related to the Small Cap Effect

    Skeptics could wonder if the relative strength of the equal-weighted portfolios in 2000-03 is based on the small-cap effect, a well-known tendency of smaller capitalization issues to outperform their larger brethren prolonged periods. The idea is that by resisting the typical tendency to give bigger allocations to some stocks based solely on larger market capitalization, smaller issues are able to play more prominent roles in portfolio performance.

    For one thing, the ongoing validity of this factor, attributed to a famous 1993 paper by Eugene Fama and Eugene French, is debatable. (In my experience, I find it easier to identify factors that effectively differentiate among better and lesser small-cap stocks, but that's not the same as saying better performance is due to size alone. Comparing ten-year returns for $SPY and the small-cap oriented iShares Russell 2000 ETF ($IWM), I do not see a significant difference ($IWM was better but by only 0.09% per year). And during the 3/1/00-1/3/03 bear market, $SPY declined 30.4% while $IWM fell 31.6%. These ETFs differ in size orientation, but both are market cap weighted.

    Note, too, that even within the same small-cap universe, we saw noticeably better 03/1/00-1/3/03 performance by an equally weighted basket of Russell 2000 stocks (down 11.7%) relative to the cap-weighted index (down 30.4%).

    So clearly, there is valuation-related merit to the notion of allocating stocks in a portfolio or index on the basis of something other than market capitalization, or more particularly, something that does not depend on Mr. Market's ever-changing moods.

    Weighting Based on What?

    So if we don't weight base on market cap, what should we use? Is Wisdom Tree right about dividend weighting? Is RAFI right about its fundamental weighting formula? Should we be weighting on the basis of something altogether different? How do we decide?

    That brings us to the edge of another, less widely used phrase, "smart alpha," which I'll introduce in my next post.

    Tags: Smart Beta
    Aug 18 5:27 PM | Link | Comment!
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