from the exceptions to the EMH, and the different types of
inefficiencies should be understood, and hence traded, differently.
The EMH was contemporaneously developed from two distinct
directions. Paul Samuelson (1965) introduced the idea to the
economics community under the umbrella of “rational
expectations theory.” At the same time, Eugene Fama's studies
(1965a, 1965b) of the statistics of security returns led him to the
theory of “the random walk.”
The idea can be stated in many ways, but a simple, general
expression is as follows:
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A market is efficient with respect to some information if it is
impossible to profitably trade based on that information.
And the “profitable trades” are risk-adjusted, after all costs.
So, depending on the information we are considering, there are
many different EMHs, but three in particular have been
extensively studied:
The strong EMH in which the information is anything that is
known by anyone
The semi-strong EMH in which the information is any publicly
available information, such as past prices, earnings, or
analysts' studies
The weak EMH in which the information is past prices
The EMH is important as an organizing principle and is a very
good approximation to reality. But, it is important to note that no
one has ever believed that any form of the EMH is strictly true.
Traders are right. Making money is hard, but it isn't impossible.
The general idea of the theory and also the fact it isn't perfect is
agreed on by most successful investors and economists.
“I think it is roughly right that the market is efficient, which
makes it very hard to beat merely by being an intelligent
investor. But I don't think it's totally efficient at all. And the
difference between being totally efficient and somewhat
efficient leaves an enormous opportunity for people like us to
get these unusual records. It's efficient enough, so it's hard to
have a great investment record. But it's by no means
impossible.”
—Charlie Munger
Even one of the inventors of the theory, Eugene Fama, qualified
the idea of efficiency by using the word good instead of perfect.
“In an efficient market, at any point in time, the actual price of a
security will be a good estimate of its intrinsic value.”
—Eugene Fama
There is something of a paradox in the concept of market
efficiency. The more efficient a market is, the more random and
unpredictable the returns will be. A perfectly efficient market will
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be completely unpredictable. But the way this comes about is
through the trading of all market participants. Investors all try to
profit from any informational advantage they have, and by doing
this their information is incorporated into the prices. Grossman
and Stiglitz (1980) use this idea to argue that perfectly efficient
markets are impossible. If markets were efficient, traders wouldn't
make the effort to gather information, and so there would be
nothing driving markets toward efficiency. So, an equilibrium will
form where markets are mostly efficient, but it is still worth
collecting and processing information.
(This is a reason fundamental analysis consisting of reading the
Wall Street Journal and technical analysis using well-known
indicators is likely to be useless. Fischer Black [1986] called these
people “noise traders.” They are the people who pay the good
traders.)
There are other arguments against the EMH. The most persuasive
of these are from the field of behavioral finance. It's been shown
that people are irrational in many ways. People who do irrational
things should provide opportunities to those who don't. As Kipling
(1910) wrote, “If you can keep your head when all about you are
losing theirs, … you will be a man, my son.”
In his original work on the EMH, Fama mentioned three
conditions that were sufficient (although not necessary) for
efficiency:
Absence of transaction costs
Perfect information flow
Agreement about the price implications of information
Helpfully for us, these conditions do not usually apply in the
options market. Options, particularly when dynamically hedged,
have large transaction costs. Information is not universally
available and volatility markets often react slowly to new
information. Further, the variance premium cannot be directly
traded. Volatility markets are a good place to look for violations of
the EMH.
Let's accept that the EMH is imperfect enough that it is possible to
make money. The economists who study these deviations from
perfection classify them into two classes: risk premia and
inefficiencies. A risk premium is earned as compensation for
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taking a risk, and if the premium is mispriced, it will be profitable even after accepting the risk. An inefficiency is a trading
opportunity caused by the market not noticing something. An
example is when people don't account for the embedded options
in a product.
There is a joke (not a funny one) about an economist seeing a
$100 bill on the ground. She walks past it. A friend asks: “Didn't
you see the money there?” The economist replies: “I thought I saw
something, but I must've imagined it. If there had been $100 on
the ground, someone would've picked it up.” We know that the
EMH is not strictly true, but the money could be there for two
different reasons. Maybe it is on a busy road and no one wants to
run into traffic. This is a risk premium. But maybe it is outside a
bar where drunks tend to drop money as they leave. This is an
inefficiency. There is also the possibility that the note was there
purely by luck.
It is often impossible to know whether a given opportunity is a risk
premium or an inefficiency, and a given opportunity will probably
be partially both. But it is important to try to differentiate. A risk
premium can be expected to persist: the counterparty is paying for
insurance against a risk. They may improve their pricing of the
insurance, but they will probably continue to pay something.
By contrast, an inefficiency will last only until other people notice
it. And failing to differentiate between a real opportunity and a
chance event will only lead to losses.
Some traders will profit from inefficiencies. Not all traders will. A
lot of traders will use meaningless or widely known information.
Many forecasts are easy. I can predict the days the non-farm
payroll will be released. I can predict what days fall on weekends. I
can predict the stock market closes at 4 p.m. eastern time. In
many cases, making a good prediction is the easy part. The hard
part is that the forecast has to be better than the market's, which
the consensus of everyone else's prediction is. For developed stock
indices the correlation between the daily range on one day and the
nex
t is roughly between 65% and 70%. So a very good volatility
estimator is that it will be what it was the day before (a few more
insights like this will lead you to GARCH). It is both hard and
profitable to make an even slightly better one-day forecast. And
whether it is because the techniques that are used are published,
employees leave and take information with them, or just that
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several people have a similar idea at the same time, these forecast edges don't last forever.
Aside: Alpha Decay
The extinction of floor traders is an example of a structural shift in
markets destroying a job. Similar to most people, traders tend to
think that their skills are special, and their jobs will always be
around. This isn't true. The floors have gone. Fixed commissions
have gone. Investment advisors are being replaced by robo-
advisors. There are fewer option market-makers, each trading
many more stocks than in the past. Offshoring will definitely come
to trading, and it is quite possible that a market structure such as a
once-a-day auction could replace continuous trading.
But as well as these structural changes, the alpha derived from
market inefficiencies (as opposed to the beta of exposure to a
mispriced risk factor) doesn't last forever. Depending on how easy
it is to trade the effect, the half-life of an inefficiency-based
strategy seems to be between 6 months and 5 years. Mclean and
Pontiff (2016) showed that the publication of a new anomaly
lessens its returns by up to 58%. And publication isn't the only
thing that erodes alpha. Chordia et al. (2014) showed that
increasing liquidity also reduces excess returns by about 50%.
Sometimes the anomaly exists only because it isn't worth the time
of large traders to get involved. A similar effect is that the easy
access to data will kill strategies. Sometimes the alpha isn't due to
a wrinkle in the financial market. It is due to the costs of
processing information.
Just as some traders will profit by using a stupid idea like
candlestick charting, some traders will succeed for a while with an
overfit model. I'm in no way using this to condone data-mining,
but we can learn a valid lesson from this. As Guns and Roses
pointed out, “nothing lasts forever.” Lucky strategies will never
last but even the best, completely valid strategy will have a
lifetime. So, when you are making money don't think that being
“prudent” is a good idea. The right thing to do is to be as
aggressive as possible. Amateurs go broke for a lot of reasons, but
professionals often suffer in bad times because they didn't fully
capitalize on good times, instead thinking that making steady but
small profits was the best thing to do.
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They also spend too much in good times, forgetting that they won't last. I've had a floor trader tell me about his new Ferrari about an
hour before laughing about the stupid spending habits of NFL and
NBA players (the last I heard he was selling houses). Many times,
traders have short careers because a valid strategy dies. Amateurs
blow up, but professionals don't allow for alpha-decay. For
example, many floor traders didn't survive the death of the open-
outcry pits. Their edge disappeared, and their previous spending
habits left them with little. (In this case “trickle-down” economics
was correct, as profits from market-making trickled down to
prostitutes, strippers, and cocaine dealers. At least it wasn't
wasted.)
Behavioral Finance
Think about how stupid the average person is, then realize half
of them are stupider than that.
—George Carlin
The history of markets is nowhere near as big as we often assume.
For example, equity options have only been traded in liquid,
transparent markets since the CBOE opened in 1973. S&P 500
futures and options have only been traded since 1982. The VIX
didn't exist until 1990 and wasn't tradable until 2004. And the
average lifetime of an S&P 500 company is only about 20 years. In
the long term, values are related to macro variables such as
inflation, monetary policy, commodity prices, interest rates, and
earnings. And these change on the order of months and years.
Even worse, they are all co-dependent.
So, what might seem like a decent length of history that we can
study and look for patterns, quite possibly isn't (this does not
apply to HFT or market-making where a huge number of data
points can be collected in what is essentially a stationary
environment). When it comes to volatility markets, I think that
although there appear to be many thousands of data points, there
might only be dozens. A better way to think of market data might
be that we are seeing a small number of data points, and that they
occur a lot of times.
I think this makes quantitative analysis of historical data much
less useful than is commonly thought.
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But there is something that has been constant: human nature.
Humans have been essentially psychologically unchanged for
300,000 years when Homo sapiens (us) first appeared. This
means that any effect that can conclusively be attributed to
psychology will effectively have 300,000 years of evidence behind
it. This seems to be potentially a much better source for gaining
clarity.
The problem with psychological explanations (for anything) is that
they are incredibly easy to postulate. As the baseball writer Bill
James was reported to say, “Twentieth-century man uses
psychology exactly like his ancestors used witchcraft; anything you
don't understand, it's psychology.” The finance media is always
using this kind of pop psychology to justify what happened that
day. “Traders are exuberant” when the market goes up a lot;
“Traders are cautiously optimistic” when it goes up a little, and so
on. I try not to do this, but I'm as guilty as anyone else. I think
psychology could be incredibly helpful, but we have to be very
careful in applying it. Ideally, we want several psychological biases
pointing to one tradeable anomaly, and we want them to have
been tested on a very similar situation to the one we intend to
trade.
Further, traders aren't psychologists and reading behavioral
finance at any level from pop psychology to real scientific journals
is probably just going to lead to hunches and guesses. To be fair,
traders currently make the same mistakes from reading articles
about geopolitics or economics. One week, traders will be experts
on the effects of tariffs on soybeans and the next week they will be
talking about Turkish interest rates. It is far easier to sound
knowledgeable than to actually be so. It isn't obvious that badly
applied behavioral psychology is any more useful than badly
applied macroeconomics. And it is obvious that traders can't do
better than misapply, either.
After I explained this nihilistic view to an ex-employer he said,
“Well, I have to do something.�
� And what we do is exactly what
I've said isn't very good: we apply statistics and behavioral
finance. These are far from perfect tools, but they are the best we
have. The edges they give will be small, but some edges can be
found. We will always know only a small part of what can be
known. Making money is hard.
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Proponents of behavioral finance contend that various
psychological biases cause investors to systematically make
mistakes that lead to market inefficiencies. Behavioral psychology
was first applied to finance in the 1980s, but for decades before
that psychologists were studying the ways people actually made
decisions under uncertainty.
The German philosopher Georg Hegel is famous (as much as any
philosopher can be famous) for his triad of thesis, antithesis, and
synthesis. A thesis is proposed. An antithesis is the negation of
that idea. Eventually, synthesis occurs, and the best part of thesis
and antithesis are combined to form a new paradigm. Ignoring the
fact that Hegel never spoke about this idea, the concept is quite
useful for describing the progress of theories. A theory is
proposed. Evidence is found that supports the theory. Eventually
it becomes established orthodoxy. But after a period, either for
theoretical reasons or because new evidence emerges, a new
theory is proposed that is strongly opposed to the first one.
Arguments ensue. Many people become more dogmatic and hold
on tightly to their side of the divide, but eventually aspects of both
thesis and antithesis are used to construct a new orthodoxy.
From the early 1960s until the late 1980s the EMH was the
dominant paradigm among finance theorists. These economists
modeled behavior in terms of rational individual decision-makers
who made optimal use of all available information. This was the
thesis.
In the 1980s an alternative view developed, driven by evidence
that the rationality assumption is unrealistic. Further, the
mistakes of individuals may not disappear in the aggregate. People
are irrational and this causes markets to be inefficient. Behavioral
finance was the antithesis.
Synthesis hasn't yet arrived, but behavioral finance is now seen as
neither an all-encompassing principle nor a fringe movement. It
Positional Option Trading (Wiley Trading) Page 4