By Samuel Lee
This article was published in the April 2013 issue of
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When I first began investing, I caught the passive investing bug
bad. The efficient-markets hypothesis, or EMH, was like a divine
revelation to me. It was elegant--almost beautiful--and blessed by
an impressive-sounding body of authorities. The University of
Chicago was my church, Eugene Fama my high priest, and Jack Bogle a
saint. He still is, of course.
I'd like to think I wasn't a blind convert. I had, after all,
looked at the data and listened to the experts. As far as I could
tell, there was a consensus: Beating the markets is close to
impossible. And there was a corollary: Active management is a
The biggest challenge to my belief system was discovering the
existence of momentum--more precisely, it was discovering how
certain people reacted to the evidence. Everyone acknowledged it
existed. However, the diehard efficient-markets academics had
baffling explanations: It was a mystery, perhaps tied to some kind
of hidden "risk factor," or it was a statistical illusion, or it
was impossible to exploit after fees and taxes. Some just shrugged
Momentum is obviously behavioral in origin, made possible by
irrational exuberance or pessimism. No other explanation makes
sense. And it is exploitable because the effect is so powerful and
prevalent in even the most liquid markets, such as large-cap
stocks. The effect is a dagger in the heart of the most
watered-down version of the EMH, "weak-form efficiency," which
holds that an asset's future price cannot be forecast using past
prices (that is, prices move in a random walk). Unbelievably,
academics completely overlooked momentum's existence until 1993.
Their statistical tests were so weak, they didn't detect what's now
acknowledged to be the most powerful and pervasive anomaly in the
I began to rethink the assumptions built into the studies I'd
taken as gospel when I saw finance's most prestigious journals had
published after-the-fact gee-whiz models that could justify the
late-1990s tech bubble as a rational market response. It takes a
lot of learning to be so wrong. When the scales fell from my eyes,
I realized this wasn't an isolated example. The EMH greased the way
to publication, no matter how badly its lens distorted the picture.
It reminded me of another form of collective delusion that still
stalks parts of academia today: the idea of humans as blank slates
and the diminishment of heredity as an explanation for, well,
anything. (Steven Pinker's
The Blank Slate
is a great treatment of this topic.)
My simplistic understanding of what all the smart guys believed
evolved as I delved into the academic literature. There actually
never was an academic consensus since at least the 1980s. While
Eugene Fama and Kenneth French stumped for efficient markets,
giants of the field like Andrei Shleifer, Larry Summers, Richard
Thaler, and Robert Shiller were pushing back with behavioral
explanations. One of the hottest points of contention was the
"value effect," the tendency for stocks with low valuation ratios
to outperform stocks with high valuation ratios. Fama and French
argued value stocks were riskier than growth stocks; equally
credentialed academics, led by Shleifer, Robert Vishny, and Josef
Lakonishok, argued investors irrationally extrapolated bad results
for value stocks and good results for growth stocks, creating
As I poked around, I discovered that many EMH-inspired findings
looked an awful lot like zombies: They just wouldn't stay dead.
Ever heard of the capital asset pricing model, or CAPM? It's a
wildly unrealistic model that proves owning the market portfolio is
"mean-variance efficient," meaning it's impossible to find a
portfolio with a better volatility-adjusted return. CAPM also says
an asset's expected return is deter-mined by one factor only: how
closely its returns move in tandem with the market portfolio, its
beta. Many stock analysts to this day use beta to estimate the
expected return of a stock, or its cost of equity, despite the fact
that it doesn't work. In fact, high CAPM beta predicts low future
returns and vice versa, a phenomenon called the "low-volatility
Somehow all of this has gotten lost in the active versus passive
debate. The science of investing is not all about efficient
markets--far from it.
Bad Ideas and Bad Incentives in Science
I'm a slow learner. It took me a while to realize that the
sophistication of a study had little to do with its merit. I clued
onto this when I began reading the work of John Ioannidis, a doctor
who also happens to be a math whiz.
Ioannidis published an influential 2005 paper that argued most
published findings are bound to be false.
You've experienced this phenomenon first-hand if you've paid
attention to the news: It seems researchers have discovered cures
for cancer, AIDS, old age, and obesity several times over.
His argument is sensible. Publications tend to look for positive
results (for example, "drug X cures cancer in mice"- wow!) and
ignore negative results ("drug X does, um, nothing in mice"-
boring!). Unfortunately, the most interesting findings are also the
ones most likely to come about by dumb luck or error.
Researchers also have a lot of leeway in the way they collect
and interpret data. Small tweaks can turn an unpublishable negative
result into a statistically significant "discovery." Hence all the
exciting initial findings that come to naught.
Ioannidis' provocative conclusion: "Moreover, for many current
scientific fields, claimed research findings may often be simply
accurate measures of the prevailing bias."
The same year, Ioannidis published a paper looking at the most
highly cited clinical research studies that were followed up with
by studies that were larger or better constructed.
Five of the six nonrandomized studies and nine of the 39 randomized
controlled studies were contradicted or weakened. These weren't bad
studies. They were published by top researchers using
Subsequent research is consistent with Ioannidis' argument.
Pharmaceutical firm Bayer AG(
) found it couldn't replicate the results of about two thirds of 67
studies it looked at.
) found that it couldn't reproduce the results of more than 90% of
53 promising papers in cancer research.
And this is biomedical science, where the methodologies are
rigorous (double-blind trials are common) and your study-if
influential or interesting enough-is going to be replicated by
deep-pocketed pharma giants or academics looking to make a name for
themselves. If there's one place where researchers have the
incentive to get it right, it's there. And yet the failure rate is
My intuition is economics and finance studies are even worse.
There are two ways to validate an economic or financial theory:
wait 100 years and collect new data, or look at a fresh new data
set, such as another time period or different markets. It can take
decades before someone's held accountable for a bunk theory. On top
of that, it's easy to run many different "experiments" on the
historical data-just change the programming code-and prove your
point, and no one can tell how many experiments you've run. (This
is a very bad thing, a sin in empirical science.)
I've learned to not be overly impressed with a single study or
even a series of studies, no matter how credentialed the authors.
The data can be tortured to confess to anything. You need to apply
liberal doses of common sense-more when the claims are outlandish.
A new theory has to be backed by many independent sources of data,
ideally data the theory's originators have never seen, and you need
to really kick the tires of any assumptions it makes.
The best models or theories are the ones that best predict
previously unseen data using the fewest and weakest assumptions
possible. It's the litmus test of whether you've struck truth: Can
you rely on it to work in the future? If not, it's useless; it's a
prettified story, nothing more. Risk manager Aaron Brown argues
many finance academics would never bet money on their more arcane
models-such models are optimized for publication, to show how
clever you are, not optimized to say something true about the
world. The arguments put forth in high finance can have an
otherworldly quality. Consider the closed-end fund "puzzle," the
fact that some investors buy CEFs at big premiums in initial public
offerings, despite the sad reality that the premium almost always
collapses within a couple of months into a discount. Prominent
researchers have published papers with models and supporting data
showing why such behavior is rational; common sense says IPOs are
foisted on naïve investors. Unless you've overdosed on math, it's
clear which is probably right.
Fama-French Versus Graham-Dodd-Buffett
The efficient-markets debate is really a competition between two
theories. The efficient marketeers, led by Eugene Fama and Kenneth
French, see the market as rational and calculating. Value
investors, exemplified by Benjamin Graham and Warren Buffett, see
it as a bipolar creature, either ecstatic or depressed.
The EMH scores highly on elegance: It makes asset-pricing theory
amenable to beautiful theorems (such as CAPM) and does a lot to
connect "macro" and "micro" models. Behavioral explanations aren't
so accommodating to economists' physics envy; it's hard to produce
a grand theory of everything once you throw irrational human beings
into the mix.
Elegance can't be for its own sake. A theory has to be
predictive. This is where the EMH falls flat on its face and the
behavioral model shines.
Imagine yourself back in New York City on May 17, 1984. Columbia
University is hosting a debate of a kind in celebration of the 50th
anniversary of the publication of Benjamin Graham and David Dodd's
classic text "Security Analysis." On the offensive is Michael
Jensen, an influential University of Rochester professor, who's
there to stump for efficient markets, a near-unanimous academic
consensus. On the defensive is Warren Buffett, Graham's most famous
disciple and already recognized as one of the greatest investors
Jensen starts. He reviews the academic literature, reciting a
litany of studies showing no statistically significant evidence of
skill. It sounds impressive. (I'm filling in the details here; it
seems no copies of his speech survive on the Internet.) He ends by
describing the fund industry as a coin-flipping game-enough
coin-flippers and someone's bound to enjoy a long streak that in
isolation looks impossible.
Buffett responds. He asks you to imagine a national
coin-flipping competition with all 225 million Americans. Each
morning the participants call out heads or tails. If they're wrong,
they drop out. After 20 days 215 coin-flippers will have called 20
coin flips in a row-literally a one in a million phenomenon for
each individual flipper, but an expected outcome given the number
of participants. Then he asks, what if 40 of those coin-flippers
came from one place, say, Omaha? That's no chance. Something's
going on there.
Buffett argues "Graham-and-Doddsville" is just that place. He
presents nine different funds that have beaten the market averages
over long periods, all sharing only two qualities: a value strategy
and a personal connection to Buffett. He emphasizes that they
weren't cherry-picked with the benefit of hindsight.
In closing, he boldly predicts "those who read their Graham and
Dodd will continue to prosper." The crowd goes wild. Later on, at
the cocktail reception, everyone's talking about how Buffett
If you're anything like me, you would've disagreed. Buffett
claimed the funds weren't cherry-picked, but how could you tell?
And at least a few of them had the same ideas as Buffett. Sequoia(
) was an early investor in Berkshire Hathaway(BRK.A) (BRK.B) stock,
and by 2004 it had 34% of its portfolio in Berkshire. It's not
clear how independent of his success the funds really were.
Buffett's survey would have failed to gain publication in a
respectable journal because it wasn't reproducible.
But the ultimate test of a theory isn't how credentialed its
proponents are or whether it's published in a prestigious journal,
it's this: Does it have predictive power? In 1984, an efficient
marketeer would predict the following: Over the long run, it's
highly likely Warren Buffett will continue to earn excess returns
only by taking on more risk. A value investor would predict "those
who read their Graham and Dodd will continue to prosper."
Which theory did a better job? From 1985 to 2012, Berkshire
Hathaway's book value would go on to grow 18% annualized, beating
the S&P 500 by 7.4% annualized, with lower volatility. The
value investors would avoid the worst of the tech bubble. The idea
of value stocks outperforming would a decade later be accepted by
academics and integrated into their models as the "value premium,"
a compensatory return boost for bearing more "risk." (Decades
later, they're still debating what this mysterious risk is!) In an
ideal world, the efficient-markets theorist in 1984 would become
closer to a value investor by 2013.
Sadly, I can't find many old-school efficient-markets academics
who've marked their beliefs to market. I'm not surprised.
Scientific history is a procession of the old guard clinging to old
ideas, defending them to the bloody death. In the early 20th
century, physicist Max Planck experienced this firsthand when he
introduced quantum mechanics, which assumed some physical
phenomena, such as light, occurred in discrete quantities, as if
nature operated with dials that could only be rotated into set
notches rather than smoothly spun. He is quoted as saying, "A new
scientific truth does not triumph by convincing its opponents and
making them see the light, but rather because its opponents
eventually die, and a new generation grows up that is familiar with
Ironically, Planck rejected the Copenhagen interpretation of
quantum mechanics, devised by a trio of younger physicists, Niels
Bohr, Werner Heisenberg, and Wolfgang Pauli (Heisenberg and Pauli
being 20-something year olds at the time). It would go on to become
the canonical interpretation.
I wouldn't feel too smug pointing at the perceived failings of
those smarter than us. At least once in a while a scientist will
change his mind. Most people have never significantly altered
beliefs that took root while they were young. They cling to
comforting delusions, and for that everyone is worse off.
1 Josef Lakonishok, Andrei Shleifer, and Robert W. Vishny.
"Contrarian Investment, Extrapolation, and Risk." The Journal of
2 John P. A. Ioannidis. "Why Most Published Research Findings
Are False." PLOSMedicine, 2005.
3 John P. A. Ioannidis. "Contradicted and Initially Stronger
Effects in Highly Cited Clinical Research." The Journal of the
American Medical Association, 2005.
4 Florian Prinz, Thomas Schlange, and Khusru Asadullah. "Believe
It or Not: How Much Can We Rely on Published Data on Potential Drug
Targets?" Nature Reviews Drug Discovery, September 2011.
5 C. Glenn Begley and Lee M. Ellis. "Drug Development: Raise
Standards for Preclinical Cancer Research." Nature, 2012.
6 Warren E. Buffett. "The Superinvestors of
Graham-and-Doddsville." Hermes, 1984.
7 Max Planck. Wikiquote,
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