At this month’s CFA Institute Annual Conference in Seattle, Sharpe said that Smart Beta made him sick because it implied that index investors had to be “dumb beta.” Sharpe believes the so-called dumb beta investors would eventually gravitate to Smart Beta strategies because no one is that dumb for long, and then the advantages of Smart Beta would simply melt away into average beta.

As regular readers know, Fama and French (F&F), and later Lakonishok et al. (LSV)  (See F&F and LSV tab above) demonstrated as early as 1992 that two factors consistently resulted in outperformance in the long run: Value and Small Cap. It is largely these two factors that put the “smart” in “Smart Beta.” F&F and LSV were not the first academics to publish papers on the value and small-cap factors, but they certainly popularized the factors in academia. Before these academics came along, we had research from practitioners Ben Graham from the 1930s through the 1970s; Warren Buffett from the 1950s to today; and Seth Klarman from the 1980s to today; that demonstrated that value strategies consistently outperform the market in the long run.

Since F&F and LSV published their research in the 1990s, there has been an overwhelming amount of academic research that demonstrates that value strategies outperform. Most of that research proves that value outperforms for reasons that are not related to risk, therefore value has consistently delivered alpha in the long run.

Most Smart Beta strategies are nothing more than systematic ways for managers to capture some of the factors that are known to deliver this alpha in the long run. The adoption of this approach in a more systematic and passive way somewhat proves Sharpe’s theory that no one stays that dumb for long. However, value and small-cap strategies outperform over long periods not necessarily because value and small-cap investors are smarter than everyone else, but because behavioral flaws and institutional constraints do not permit EVERYONE to FULLY capture the alpha in value and small cap. I remind you that it did take over 150 years for Smart Beta to be born.

Only small investors with contrarian streaks (see my future post on Investor DNA) can fully exploit these factors. Even Smart Beta strategies will fail to fully exploit these factors because of the amount of capital that Smart Beta will need to invest. Much of that capital will have to be allocated to large cap firms, but most of the alpha in these factors is found in relatively unknown and un-followed small-cap firms.

So, my answer to Sharpe’s queasiness is this: Smart Beta is just a label. Would he have taken less umbrage if that label were “Behavioral Beta” or “Factor-Focused Beta?”

http://advisorperspectives.com/newsletters14/Bill_Sharpe-Smart_beta_makes_me_sick.php

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In his latest column (3/2/13), Jason refers to a paper about to be published by the University of Rochester’s Robert Novy-Marx (RNV). The column:

http://online.wsj.com/article/SB10001424127887323293704578334491900368844.html

RNV has written often on the value premium–the item that my investment firm has been trying (with some success) to capture. He has been trying to understand why and where it exists, such as in the following paper that I read a few years ago that relates the value premium to operating leverage (Note: the draft of the operating leverage paper that I read was dated May 18, 2007):

http://rof.oxfordjournals.org/content/early/2010/08/16/rof.rfq019.abstract

In RNV’s latest paper, which was the impetus for Jason’s column, RNV introduces a quality formula to improve the value premium. It seems to be generating a lot of buzz because Jason wrote that DFA’s founder David Booth and AQR’s Cliff Asness are planning to create funds based on RNV’s quality paper. Booth even called it one of those investing ideas that only come along once every twenty years or so.

But, RNV compares his measures with several tools that my firm has used since day one, including those with a quality component, specifically value investing tools based on the work of Fama, French, Lakonishok, Shleifer, Vishny (see F&F and LSV tab above), Haugen, Piotroski, and Greenblatt.

Fama and French (F&F) were not the first to point out that high book-to-market (BtM) stocks (value) trounced low BtM stocks (glamour) in generating returns, but their 1992 paper brought the issue to the forefront because they are staunch defenders of the Efficient Market Hypothesis (EMH), which their paper seemed to discredit. F&F swiped that cognitive dissonance aside by claiming (“hoping” actually) that the extra returns were compensation for risk (that they did not quantify).

LSV and Haugen later showed that value’s better performance was earned with less risk than the market leaving F&F with only hope. In 2000, Piotroski showed that the performance of the F&F model could be further improved with a nine-point measure of quality based on nine financial statement metrics. Piotroski called it an F score. And Greenblatt used a two factor joint measure of quality and price.

When executing its strategy, my firm has leaned on F&F’s BtM research and on Piotroski’s F score in addition to the research from the others mentioned above. I have always thought of our process as a joint value/quality approach, so I find it interesting that many feel as if this is something new. It may be new in that the research is presented in a new way with data through 2011, but this approach has been practiced by many value investors for a while and RNV’s results are not very different from the results of other Value/Quality practitioners. In addition, I have called my process a sorting process and not a screening process, because we sort rather than screen for the best investment ideas. RNV used a similar sorting process.

In the end, however, most of a practitioner’s ability to capture the value premium is going to be determined by whether they have the stomach to enter the order and buy some temporarily ugly looking businesses at the height of their grotesqueness (when their prices are dropping) and to sell those businesses when those prices bounce back to intrinsic value. As cliff Asness once said, a model never loses its nerve. I would add that a model never gets greedy. The ability to manage fear and greed are paramount.

Here is a draft of RNV’s paper to which Jason refers in his column:

http://rnm.simon.rochester.edu/research/QDoVI.pdf

It’s good stuff if only to help explain why so many value investors have beaten the market for so long.

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“Would you rather retire in Diamond Head Hawaii or Diamond Bar California? If you invest with value investing principles, you can retire in Diamond Head. If you invest as if markets were efficient, you will probably not be able to afford Diamond Head.”

The Haugen claim–backed by significant research–that was most shocking to those indoctrinated in the Efficient Market Hypothesis (EMH) was that value investors not only earned the best long-term returns, but that they did so with much less risk than the risk assumed by index fund investors.

Haugen’s book was a rare part of the CFA curriculum that criticized the EMH. It resonated with me. The fourth edition of The New Finance has been added to the Value Investing Bookstore tab above. It is out of print but can be bought through Amazon.

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http://online.wsj.com/article/SB10001424127887324432004578306153331261978.html

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Tebow is not the only quarterback who comes to mind. Ever hear of a guy named Joe Montana? Montana played at a little known football college called Notre Dame. He was recruited by ND, but in 1977 at the beginning of his fourth year in the program (an injury gave him five years of eligibility) Montana was still listed third on the depth chart behind Rusty Lisch and Gary Forystek.

ND started the season 1-1 in 1977 and Montana did not play until there were eleven minutes left in the third game of the season with ND trailing by more than a touchdown. He rallied ND to a victory (Data provided by Wikipedia) and never lost his starting job after that. In fact, ND did not lose another game that year after he got the chance to play.

ND finished 1977 with a win in the Cotton Bowl over then-number-one ranked University of Texas and ND was voted the National Champions. All Montana did in college was win, usually late as he led his team in one comeback after another.

You would think that such a clutch performer who led his team to a National Championship would be viewed favorably by the experts in the NFL, but the scouts did not think very highly of Montana. They ranked his arm strength as particularly weak. So, the following quarterbacks were drafted ahead of Montana:

First Round: Jack Thomson, Phil Simms, Steve Fuller 

No other quarterbacks were chosen in that draft until Montana was taken with the last pick in the third round, number 82 overall. All Montana did in the NFL was win four Super Bowls, win three Super Bowl MVP awards, get selected for eight Pro Bowls, and get elected to the NFL Hall of Fame. He is widely considered to be the greatest quarterback of all time.

Okay, so maybe you heard of Montana, but have you ever heard of a guy named Tom Brady? I will admit I did not like the guy until this year when I saw an ESPN film called “The Brady 6.”  It is the story of Brady and the six quarterbacks who were drafted ahead of Brady in the 2000 NFL draft.

You can and should watch “The Brady 6″ on YouTube (I embedded part I below), so I will not bore you with Brady’s story here. But, I found one thing especially noteworthy: the experts at the NFL combine had ranked 576 college quarterbacks in the speed and agility categories in the multi-decade history of the NFL Combine. Brady’s overall ranking in the history of the combine was 576.

The football experts rely on these combine numbers the way baseball scouts heavily rely on batting average for hitters and velocity for pitchers; the way fund analysts rely on pedigree for performance prospects and beta for risk measurement. Oh, and the experts all felt that Brady had poor arm strength.

The two quarterbacks who earned 100 wins in their careers in the fewest number of starts were Joe Montana and Tom Brady. Watch the Brady 6; it may forever change the way you think of experts in sports and elsewhere.

Today, we have Tebow, who is being criticized for a supposed lack of NFL-caliber skill, and the criticism is often nasty. The silence from Denver’s front office, scouts, and coaching staff has been deafening. It should be noted that Tebow was drafted by a different front office and coaching staff from the current one in Denver.

Yet, today I found out that Tebow is 4 -3 5 – 2 (5-3 6 – 2 after the win against the Jets) in his first seven starts in Denver compared with Hall-of-Famer John Elway’s 1 – 6 record in his first seven. Tebow is now 4-1 this year after Denver started 1-4 without him and he has Denver in the playoff hunt. Tebow has something like eight touchdowns to one interception in that seven game span and Elway had those numbers reversed. The knock on Tebow has been that he does not have the arm strength to be an NFL quarterback. It sounds familiar.

Maybe the experts will have gotten this one right in the end. After all, Tebow did have more success in college than Montana and Brady; he did win two National Championships at The University of Florida and a Heisman Trophy. And, unlike Montana and Brady, Tebow was taken in the first round of the NFL draft.

I am choosing NFL quarterbacks for this criticism of experts, but you could pick any position in the NFL, or any position in any other sport, and you will find that experts often get it wrong. Or, you could choose experts in any field from finance to climate science. Many are not just proven wrong, but fantastically wrong. Perhaps it is because those who are deemed experts are usually the ones who are the most sure of themselves; they make the best media, board room, or draft room presentations, but perhaps they are not necessarily the best at understanding talent or analyzing complex phenomena. Often times, the one who is the most aggressive and talks the loudest wins the day.

Michael Lewis’s great book Moneyball is all about experts who get it wrong, leaving cheap bargains available for savvy analysts who can see through the nonsense. Here are some reasons that experts make mistakes in evaluating baseball talent: evaluating a player based on whether or not he has a square jaw (“a baseball face”); whether he looks good in jeans; or whether he has a pretty girlfriend. That is the kind of analysis that experts provided before Moneyball, and many still have similar biases.

One such bias in baseball that has not disappeared is a bias against “soft tossers;” i.e. pitchers who cannot consistently break 90-miles per hour on the radar gun. Remind you of the supposedly weak arms of Montana, Brady, and Tebow? Baseball pitching experts are enamored with velocity and are blind to practically every flaw in a pitcher who can throw hard. But, if a pitcher does not throw hard they will ignore him even if he has few flaws, even if he can knock a fly off a catcher’s mitt, make the ball move, change speeds, and collect wins. Never mind that the greatest pitcher in the last thirty years rarely used velocity to get hitters out, but could hit practically every spot he wanted to within an inch or two, move the ball, and change speeds: five-time Cy Young winner Greg Maddux.

So, should we rely on experts to the degree that we do? Warren Buffett points out that  experts concluded that his and other value investors’  accomplishments were either lucky–like a coin flipper who gets heads twenty times in a row–or that there is just not enough data to evaluate their success. His now-famous story is of 225 million orangutans spread evenly throughout the country who mindlessly flips coins; by sheer luck 215 of them will get heads twenty times in a row. But, he says that if forty of those 215 orangutans are from the same zoo, maybe they are on to something.

Were the experts right, but the outlier successes of the Montanas, Bradys, Madduxes, Buffetts, Klarmans, and Einhorns to be expected as merely the lucky random ones who fell under the far reaches of the bell curve?

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I must confess that I was a skeptic on behavioral finance until a few years ago. At that point, the amount of information that had been accumulated on the “irrational” behavior of investors became so overwhelming that I faced one of two choices. I could ignore reality and live in the clean, rational world of classical economics or I could face up to facts and think about how investment and corporate finance decisions should be made in the messy world that we live in. After struggling with the conflict, I think I am making some progress.

http://aswathdamodaran.blogspot.com/2011/03/behavioral-economics-thoughts-on-value.html

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Howard Marks has also been delivering great risk-adjusted returns for his investors for years. You see, like virtually every great investor of the last 100 years, Howard Marks is a value investor.

In this week’s “Intelligent Investor” column, Jason Zweig quotes Marks’s thoughts on risk. Although the concept in Marks’s comments is not new—virtually every great value investor knows the truth of it—it does completely contradict Modern Portfolio Theory (MPT). You know the theory; it, along with the Efficient Market Hypothesis (EMH), has kept the Nobel committee busy printing economics prizes for much of the last few decades.

Zweig’s column this week is critical of James K Glassman’s predictions—both his 1999 prediction of Dow 36,000 and now when Glassman predicts further disaster. Both predictions depend on Glassman’s assessment of risk. Glassman believed in 1999 that the equity premium was going to decline rapidly, possibly to zero, meaning stocks and bonds would have the same discount rate because they would finally be perceived to have the same risk. That meant there was a lot of upside room for the stock market, hence the title of his book: Dow 36,000.

Zweig’s column:

http://online.wsj.com/article/SB10001424052748704329104576138271281667798.html?KEYWORDS=zweig#articleTabs%3Darticle

Glassman insists his argument wasn’t radical. In one way he is right: Economists contend that riskier assets must offer higher returns, or no one would invest in them. That is a fallacy, says Howard Marks…Mr. Marks is author of a superb forthcoming book, The Most Important Thing, that helps explain risk clearly.

Riskier assets don’t necessarily offer higher returns, Mr. Marks says; they only appear to do so. “It’s really simple,” he says. “If risky investments could be counted on for higher returns, then they wouldn’t be risky. And if investments weren’t risky, then they probably wouldn’t appear to promise higher returns.”

By chasing the potential for higher return in riskier assets, investors drive prices up…by Mr. Marks’s common sense definition of risk—“the likelihood of losing money”—rising prices are pure investment poison. The higher and faster prices go up, the farther and harder they have to fall.

Marks’s common sense approach to risk has repeatedly been proven to be true in academic research and in practice. For example, Fama and French, two of the biggest academic proponents of MPT and the EMH, have shown that value stocks consistently provide superior long-run returns. The only way that Fama and French could reconcile that fact with their belief system was to claim (without proving it) that it was because value investments are riskier. Lakonishok, Shleifer, and Vishny (LSV) later proved that Fama and French’s riskiness claim for value stocks was patently untrue.

James Montier, Robert Haugen, LSV, and many others have used the classic definition of risk—volatility, as opposed to loss—against EMH and MPT proponents. Below is Montier’s 2008 chart of the return and risk characteristics of US companies from 1950 through 2007. Montier used the cash flow to price ratio to separate value companies from growth companies in this analysis. The 20% of companies with the highest cash flow to price are value companies. The 20% with the lowest CF to price are growth. The figures in the chart are average annual data for the 57-year period.

So, using the favored risk measurement of EMH and MPT proponents–volatility–we can completely discredit the MPT hypothesis that one has to accept more risk to earn a higher return.  This shows that Marks is correct. When you focus on and minimize risk, you wind up with better returns. Value investors tend to use intrinsic value estimates to estimate risk. To manage risk, they only buy when they receive a margin of safety.

This chart is consistent with all of the data available for value and growth companies, so whether we separate value from growth via cash flow, book value, dividends, or some other metric, value companies deliver consistent, superior, long-run risk-adjusted returns. And, it is a long-run edge that is not likely to disappear any time soon.

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Efficient Market Hypothesis proponents, like good lawyers, argue that there is absolutely no such thing as a permanent edge in investing and any permanent edge that does exist is riskier than the alternatives. (“Your honor, my client was never in that woman’s apartment and he was only there to return her lost kitten”). I mean, why paint yourself into a corner?

Fama and French (F&F) and almost every other speaker in the above video said that the reason that value stocks consistently outperformed “growth” (glamour) stocks was that there was some additional risk in value stocks. Unfortunately, this only seems to be F&F’s hope because they certainly do not quantify that risk in their now-legendary 1992 paper in the Journal of Finance, “A Cross Section of expected Stock Returns.” To my knowledge they were not able to quantify that risk at any point in the last eighteen years.

F&F thought that they saw some evidence of low earnings in small cap stocks and value stocks. That led them to write the following in the conclusion to “A Cross Section of expected Stock Returns:”

“The systematic patterns in fundamentals give us some hope that size and book-to-market equity proxy for risk factors…”[1]

So, to summarize F&F’s conclusion: immediately prior to a company being added to a value cohort in their study, some value stocks experienced a period of low earnings. That was all they had.  I am not aware of any follow up in the last eighteen years to F&F’s hope that small stocks and value stocks proxy for risk because of low earnings or for any other reason. It seems to me that hypothesis could be tested pretty easily.

Real risk usually has little to do with a period of low earnings alone; virtually every company goes through that. But, real risk is related to high leverage, which can quickly lead to bankruptcy. What are the odds that a highly leveraged business will have a high book-to-market ratio (book value / market value)? That is, what are the odds that a highly leveraged business would finds its way into F&F’s value cohort? Considering that for a given capital structure, book value is reduced by leverage, all other things being equal, I suspect you will find few highly leveraged businesses in the F&F high book-to-market (value) deciles but that you will find a higher percentage of highly leveraged businesses in the low book-to-market  (glamour) deciles.

Other real risk factors include low operating leverage; product obsolescence; low barriers to entry; regulatory risk; loss of suppliers; a concentration of buyers; low current ratios or long periods of low cash flow relative to short-term liabilities, to name a few. These are not mentioned in the F&F literature as risk factors that need to be managed.  In fact, the people in the video identify risk with volatility because most of the academic finance literature of the last fifty years identifies risk with volatility.

But the coup de grace to F&F’s riskiness tack came as early as 1994. LSV picked up on F&F’s genuflection to hope and empirically demonstrated in another Journal of Finance article, “Contrarian Investment, Extrapolation, and Risk,” that risk had nothing to do with value’s superior results. LSV (and Haugen, Montier, and many others since LSV) showed that when using the various proxies for risk—Beta, volatility, etc.—they could prove that value stocks exhibited less volatility than glamour stocks. That is, value stocks are significantly less risky than glamour stocks. LSV also showed that in periods of stress—recessions, bear markets, etc.—when risky investments tend to be punished and safe investments tend to be hoarded, value stocks consistently beat glamour.

[1] P. 452

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Many believe that there can be no permanent edge in investing because they believe that market forces will quickly eliminate any edge. That is, many believe that sophisticated investors will bid up prices in investment strategies that tend to outperform and bid down prices in strategies that tend to lag until the edge disappears.

For my first post on this blog, I will write about a persistent investing edge–value investing–and one reason why that edge may never disappear; that reason being human behavior, which rarely changes. We can thank Jason Zweig (Your Money and Your Brain) and James Montier (Behavioural Investing) for describing in their books the behavioral experiment described in this post.

The Persistence of Value Investing’s Edge

There are many academic studies that demonstrate that value investing beats glamour investing[1]in the long run; see, for example, Fama and French (F&F)[2]or Lakonishok, Shleifer and Vishny (LSV)[3] or the many studies that built on F&F’s original research and examined performance data going back to the 1920s.

Besides academic studies, we have many examples of practicing value investors who have grown wealthy by consistently beating the market in the long run—from Benjamin Graham to Warren Buffett (before his capital grew to an enormous size) to David Einhorn. Conversely, we have few examples of glamour investors who have consistently beaten the market.

As for the academics, Fama and French also happen to be, as James Montier likes to say, two of the “High Priests” of the Efficient Market Hypothesis (EMH). Their concession that value beats glamour is even more remarkable because the EMH implies that all such edges must disappear.  If value investing offers superior returns, then the EMH also implies that value stocks must be riskier than glamour stocks, but we shall show in another post that in the long run value investing is actually safer than all other equity investing styles.

What may be most remarkable about the F&F story is that these two high priests of the EMH later became integral partners at Dimensional Fund Advisors (DFA), which is a $130 billion-plus fund firm that attempts to beat the market. In fact, according to the DFA video below, DFA as we know it would never have been possible without F&F’s research on the persistent success of value investing. And, DFA was not the only firm built on this analysis. The firm LSV–yes, the same LSV as above–currently runs over $30 billion in assets based on the value-investing principles they discovered in their academic research.

Why isn’t Every Investor a Value Investor?

So all of this begs the question: If value almost always outperforms in the long run and is almost always less risky in the long run, then why aren’t all investors value investors? Part of the answer appears to lie in human evolution and its mark on our behavior; behavior that makes it difficult to execute a value-investing strategy.

Behavioral Finance

The field of behavioral economics and finance arose in the last half of the last century. It evolved from the outcomes of experiments that revealed how people actually behaved in different environments. In that regard, it developed unlike traditional economic theory, which was created by economists who simply assumed that humans behaved rationally almost all of the time. Traditional economic theorists then went about creating precise mathematical models to prescribe policy for rational beings. I agree that in a world of perfectly rational behavior, almost all investing edges should disappear.

I also agree that if everyone acted rationally and processed readily available information the way the theorists supposed, there would be few obese people in the United States. We all know that we need to eat right and exercise in order to look our best and postpone pain, debilitating disease, and death. The rational thing to do in that case is eat right and exercise. But, somewhere between the theorists’ expectations and the outcome, our behavior fails us.

Pigeons versus Humans

So, consider the following expirement which may explain one of our behavioral flaws: Pigeons and rats are shown flashing lights—one red and one green. Eighty percent of the time the light flashes green, and 20% of the time red, but they flash in a random order. When the animals guess the next flash correctly they are rewarded with a morsel of food. Rats and pigeons figure out fairly quickly that it is best to predict a green flash every time because when they do they are rewarded 80% of the time.

We humans, on the other hand, have been programmed in our evolution to look for patterns. Even when we are told explicitly that the flashes of light are random we still believe we can discern a pattern and predict the next red flash. Humans in this experiment tend to guess that the next color that will flash will be red approximately 20% of the time. That behavior leads to a virtual certainty that humans will do worse than the pigeons in this experiment. For the pigeon, the probability of guessing the correct color flash is 80%; for humans it is only 68%. Eighty percent of the time humans will guess green and eighty percent of those times they will be correct; twenty percent of the time they guess red and twenty percent of those times they will be correct (0.8 * 0.8 + 0.2 * 0.2 = 0.68).

This is one possible answer to why so few investors are true value investors despite the obvious success of the strategy over long periods of time. Markets are complex and when examined over short periods they seem to deliver random results—up one day and down the next. And, value strategies will not outperform in each and every period. One can make the analogy that investing is like the light flash experiment: value investing is a green flash that “wins” far more often than the red flash (glamour investing), but we humans will still occasionally move capital to glamour strategies because we believe we can discern a pattern and so we try to predict the next time that glamour will beat value.

This, of course, is not the only behavioral reason that there are few true value investors; our emotions often get the best of us. And there are cognitive and institutional constraints, too, that aid in maintaining value’s performance edge. We will try to cover them here on this blog. I hope you enjoy reading about them as much as I do.

Jason Zweig makes the following important points about our attempts to divine patterns in random results, which he calls our prediction addiction: [4]

It is vital to recognize the basic realities of pattern recognition in your investing brain:

1. It leaps to conclusions. Two in a row of almost anything—rising or falling stock prices, high or low mutual fund returns—will make you expect a third.
2. It is unconscious. Even if you think you are fully engaged in some kind of sophisticated analysis, your pattern-seeking machinery may well guide you to a much more instinctive solution.
3. It is automatic. Whenever you are confronted with anything random, you will search for patterns within it. It’s how your brain was built.
4. It is uncontrollable. You can’t turn this kind of processing off or make it go away.

I think it would be useful to study the brains of great value investors like Buffett to determine if their pattern recognition programming is more subdued than others. If so, it may be that great investors are born and not made.

Conclusion

True value investors like Buffett are the stubborn pigeons whose research has determined that buying with a margin of safety wins more often than it loses—and that when it wins it tends to win by a lot. They stick with their strategy and processes regardless of the vicissitudes of the market, and in the long run they trounce all other investors and indexes. Market timers believe they can guess the next market move, but will almost certainly fall short. Value investors are few in number for a variety of behavioral, cognitive, and institutional reasons, one of which is our brains’ inability to tune out spurious pattern recognition.

[1] Like LSV, we use the term glamour instead of growth because all value investors desire growth; it is just that they are rarely willing to pay as much for that growth as the rest of the market. Glamour connotes a spurious value, which makes it more appropriate to contrast with true value.

[2] 1992, “The Cross-Section of Expected Stock Returns,” Journal of Finance, vol, 47, no. 2 (June): 427-465.

[3]1994, “Contrarian Investment, Extrapolation, and Risk,” Journal of Finance, vol, 49, no. 5 (December): 1541-78.

[4]Zweig; Your Money and Your Brain; p. 61

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