7. Efficient Markets

And I want to talk today about
efficient markets, which is a theory that is a half-truth,
I will say. Before I start, I wanted to just
give a few thoughts about David Swensen’s lecture
last period. Let me say, first of all, the
Efficient Markets Hypothesis or the Efficient Markets
Theory is a theory that markets efficiently incorporate
all public information. And that, therefore, you
cannot beat the market, because the market has all
the information in it. You think you’re smarter
than the market, that you know something? No, the market knows
more than you do. And you’ll find out that the
market wins every time. That’s the Efficient
Markets Hypothesis. So, it’s a very far-reaching
hypothesis. It means that, don’t even
try to beat the market. That was a very rudimentary introduction to today’s lecture. But here I brought in David
Swensen, who is claimed to have beaten the market
consistently since 1985, and dramatically. And so, what do we
make of that? That’s the subject of
today’s lecture. By the way, after class one of
you came up — thank you, it was nice, I don’t know
where you are — one of you came up and thanked
Swensen for his scholarship at Yale. Yale now has need-blind
admissions for the world. And so people, not just from the
United States, people are helped out, so that people who
have managed to meet the high admission standards here, it
makes it possible for them to actually come here. And that’s substantially David
Swensen who did that, because it’s not just the generosity
of the university. They have to have the
money to do it. And so, somehow he seems
to have made it. And I know that there are
still many cynics — the Efficient Markets Hypothesis has
a lot of adherents still. And as I say, it’s
a half-truth. So, some people will say, well,
Swensen was just lucky. And I say, how could he
have been lucky for 25 years in a row? Well, not every single year,
but pretty much. And they say, well, you’re
picking the one guy out of millions who is just the
luckiest. So, those arguments are made. Anyway, one of you asked a
question, which I thought it was very good, at the end. And that is why, in all of my
discussion about Swensen and all of his talk we never
mentioned the Sharpe ratio? Because as we said,
the Sharpe ratio corrects for risk taking. That was one of our fundamental
lessons. And we showed you the Efficient
Portfolio Frontier, and the tangency line. You can get any expected return
you want at the expense of higher uncertainty. You do a very risky portfolio,
and you have high expected return, because of the risk. If the risk is measured right. But I think that’s a good
very good question. I caught myself not correcting
for it, I just said Yale’s portfolio had a high return. I didn’t correct for standard
deviation of return. So, David Swensen, in his
answer, as you recall, essentially said he doesn’t
believe in Sharpe ratios, because we can’t measure
the standard deviation. The Sharpe ratio is the excess
return of a portfolio over the market, divided by the standard deviation of the return. And that scales it down, so if
the excess return is very high but also has a very high
standard deviation, that shows they were just taking risks. And so the Sharpe ratio
would reveal that. But Swensen said, I don’t think
that you can measure the standard deviation of return. Isn’t that what is answer was? You were here. I may not be quoting
him exactly right. So, why wouldn’t you be able
to measure the standard deviation of returns? He gave a reason, which was
that, well, when you’re looking at a broad portfolio
like Yale’s, a lot of the things in there are
private equity — that means privately
held, so it’s not traded on stock exchanges. Or it’s real estate. Real estate is only traded every
10 years or 20 years, and so who knows what
it’s worth? All you have is an appraisal,
but that’s just some appraiser’s estimate, so the
standard deviation would be artificially low. He’s right about that,
but I think there’s even more to that. I know there’s more to this. There’s a literature on this. The point that I wanted to make
is that you can do a — suppose you’re managing money. And suppose the world out there
is evaluating you by your Sharpe ratio. And suppose you have
no ethics. You just want money. This isn’t so obviously
criminal, this is not criminal I suppose. So, you say, I just want to have
the best Sharpe ratio for a number of years running. I’ll get more and more people
that will put money in my investment fund, and
eventually I don’t care what happens. I’ll move to Brazil
or something. Some foreign country
— pick out one. I want to get out of here
with the money. So, all I have to do is fool
people into thinking I have a high Sharpe ratio for a while. So, what do I do? Well, there’s an interesting
paper on this by — there’s a lot of papers
on this, but I’m going to cite one — by Professor Goetzmann and
co-authors, here at Yale. It’s actually Goetzmann,
Ibbotson, Spiegel, and Welch. Maybe I’ll put all
their names on. Roger Ibbotson is a professor
here, Matt Spiegel, and Ivo Welch. What they did is they calculated
the optimal strategy for someone who
wants to play games with a Sharpe ratio. So, you want to fool investors
and get a spuriously high Sharpe ratio. And they found out what the
optimal strategy is. And that is to sell off
the tails of your distribution of returns. So, if your return distribution
looks like this — this is returns. And you have a probability
distribution, say a bell-shaped curve. And so the mean and standard
deviation of this would be the inputs to the Sharpe ratio. But if you’re cynical and you
want to play tricks, what you can do is sell the upper tail. These are very unlikely
good events. Sell them and get money now,
and then double up on the lower tail. So, you push the lower tail to
something like that, and you wipe out the upper tail,
so it goes like that. So, that means you’ll get money,
because you sold the upper tail. You would do that by selling
calls — we haven’t talked about options yet — but you
can do it by selling out-of-the-money calls. And you would do this by writing
out-of-the-money puts. OK, but what you do it you make
it, so that if there’s really a bad year, it’s going
to be a doozer bad year for your investors. And if there’s ever a good
year, then, hey, you won’t get it. But these good or bad years
occur only infrequently. So, in the meantime, you’re
making profits from these sales and you have a
high Sharpe ratio. But little do they know — you sold off the tails. And so nothing happens for many
years and you just look like the best guy there. So, it turns out that this
is not just academic. There was a company called
Integral Investment Management that did something like
this strategy. It was a hedge fund. So, it was Integral Investment
Management. It did something like this,
by trading in options. And it got lots of investors
to put millions in them. Notably, the Art Institute of
Chicago put $43 million into this fund and its associated
funds. And then, in 2001, when the
market dropped a lot, the Art Institute of Chicago
was wiped out. They lost almost all of
their $43 million. And so, they got really angry,
and they sued this company. Because they said, you
didn’t tell us. What have you been doing? And then the company pointed
out, in its defense, that it actually said somewhere in the
fine print that if markets go down more than 30%, there
would be a problem. And somehow nobody at the
Art Institute read that or figured it out. They thought the guy was a
genius, because this company had the highest Sharpe ratio
in the industry. You see what they’re doing? They’re playing tricks. They’re making it look like
there’s less risk than there really is. And there’s a strategy
to do that. But it didn’t end well for
Integral Investment Management, because the Art
Institute of Chicago managed to stick them on other things
— they disclosed it. They told people that they
were doing this strategy. The artists didn’t
figure it out. But there were other
dishonesties that they nailed these guys on. What Goetzmann and his
co-authors did is they showed that you can play tricks with
the — you can play a lot of tricks in finance. But one of them is to play a
trick with the Sharpe ratio. But their trick was
very explicit. It involved particular
portfolio composition involving options, and any
professional would immediately know that that’s a trick. And it didn’t work for these
guys, they were too aggressive in their manipulation. But you can do subtler things as
a portfolio manager to get your Sharpe ratio up. Instead of manipulating with
special derivatives positions you could just buy companies
that have large left tails. They have a small probability
of massive losses. And you haven’t done anything
but pick a stock. And nobody knows whether it
really has a small probability of massive losses,
and you could systematically invest in that. And then you’d have a high
Sharpe ratio for a while, and then you’d just blow up and lose
everything eventually. I was thinking of an example
from recent news. What about the strategy of
investing in Egyptian companies that are
tied to Mubarak? It might have looked very good
for a long time, right? But the companies might have
been underpriced, because people sensed there’s some
instability in Egypt. And look how fast it came on. I don’t know what the outcome
will be at this point, but it just happened — bang. That’s a tail event, right? You might look at Egyptian
securities and think everything is stable and fine,
it’s been 30 years, nothing has happened. But someone knows or suspects
that there’s something maybe unstable. And you, as an investor,
wouldn’t know that by looking at the numbers. What I’m getting at is really
what is the essence of Swensen’s skill or
contribution? It’s not his ability to
manipulate Sharpe ratios or numbers like that. I think what it has, to me, in
my mind, is something to do with character and his real self
and his real objectives. And this gets at what people in
finance are really doing. I think that when you take
a finance course in a university, you may not get a
proper appreciation of how one, in a career in finance,
develops a reputation for integrity. Nobody can really judge what
you’re doing as an investor, because they can’t judge
it from the statistics. Even though we’ve developed this
nice theory about Sharpe ratios and the like, you end up
judging the person and what the person’s real
objectives are. I’ll probably come back
to that theme again. Let me also say that’s about
doing the Goetzmann — you understand the strategy? It’s like investing
in securities with time bombs in them. They’re going to go off
eventually, you don’t know exactly when. And they look good for a while,
but they’ll blow up. What does the law
say about this? Well, the law in the United
States and other countries emphasizes that an investment
manager must not fail to disclose relevant information
about a security. And it has to be more than
boilerplate disclosure. For, say Integral Investment
Management, you could write up a prospectus and say, but of
course past returns are not a guide to the future and
something could go wrong. That’s a boilerplate disclosure,
because people think, well, that’s what
everybody says. The law says that you have to
actually actively disclose. If there’s something that’s
relevant that would make your statistics misleading, you have
to get their attention and explain it to them. That’s the law. So, I think it’s laws like
that, and it’s the development of — it’s not something that we
specialize in academia. Well, we are, we’re trying to
develop character I suppose. But it’s not just
Sharpe ratios. And I think that tendencies to
rely on numbers like this has led to errors in the past. So, let me go more directly
into today’s lecture. It’s about the Efficient Markets
Hypothesis, which is, to me, a fascinating theory,
which is not completely true, which makes it all the
more interesting. The first statement that
I could find — I’m interested in the history
of thought — so, the first statement of the
efficient markets hypothesis that I could find was in
Gibson, who wrote a book in 1889 called The Stock
Exchanges of London, Paris, and New York. I’m quoting George Gibson,
1889: “When shares become publicly known in an open
market, the value, which they acquire there, may be regarded
as the judgment of the best intelligence concerning them.” He described the stock market
as a kind of voting machine where people vote. If you think a share is worth
more, you vote by buying it. If you think it’s worth less
in the market, you sell it. And everybody in the
world can do that. It’s open to the public. So, the smartest people get into
it, and then they soon make a lot of money doing it,
so they have a lot of votes. So, the smarter people
have more votes. And you’ve got everyone there. If anyone has a special clue,
they go right in and they buy if it’s positive, or they
sell if it’s negative. So, the smartest people
go right in there and aggressively affect the value. Until it’s right, and then
there’s no there’s no incentive to buy or sell. The other thing about
Gibson — I should have copied the quote,
but as I remember from the book, he says something like
”in our modern electric age, information flows with the
speed of light.” I say, what is he talking
about in 1889? Well, you know what he’s
talking about. The telegraph. In fact, they had
ticker machines. They were electronic
printers that would print out stock quotes. So, they were really in the
information age by 1889. So, this is what happened,
Gibson said, you can’t beat the market. It’s just smarter because —
it’s like Wikipedia is smarter than any one of us, right? Because it puts together
all the thinking of all the people. Well, they had Wikipedia of a
sort, because they had the stock market. They had the price. So, that is the statement
that — he didn’t use the word
”Efficient Markets.” Actually, tried to find the
origin of “Efficient Markets.” Sometimes in the 19th century,
people would say ”Efficient Markets.” But it wasn’t a
cliche yet, it wasn’t a phrase that would be recognizable. Even in this context,
they would use it. But it was not a name
for a theory yet. The next Efficient Markets
theorist — and I put this on your reading list — Charles Conant, who wrote a 1904
book called Wall Street and the Country. I put one chapter on that on the
reading list, because it was a statement of the Efficient
Markets Hypothesis, which was remarkably
well-written, I thought. He starts out the chapter by
pointing out that a lot of people think speculation
is a kind of gambling, or a kind of evil. Speculating on the stock market,
that sounds like some wild activity that ought
to be ruled out. Then he said, how can that
possibly be true? The stock market is a
central institution of all modern economies. To think that it’s just
gambling just defies common sense. Then he goes on and describes
what it is that it does. And there’s some really nice
passages in Conant’s book. In one passage he says, suppose
for a moment that stock markets of the world were closed, what would happen? He said no one would know
what anything is worth. No one could make any calculated
decisions. He said that, in fact, capital
moves around from one industry to another in respect to the
prices that are quoted in these markets. And if you didn’t see the
prices, you would be blind. It’s often said about the
Soviet economy — which did not have
stock markets or financial markets — that they relied on prices
in the rest of the world. Nobody in the Soviet Union could
plan very well, because they didn’t know what
anything was worth. But at least they had the rest
of the world, and they thought, well that’s an
approximation to what prices ought to be in the
Soviet Union. I just wanted to reiterate a
little bit about the intuition of Efficient Markets. The idea is that if you trade
securities, the advantage to being there a little bit ahead
of anyone else is enormous. If you know five minutes before
the other investors about some good news or bad
news, either way it doesn’t matter — you know it five
minutes earlier, you jump right in and trade. You can trade ahead of them
and prices haven’t changed yet, you make money. So, that has created an
industry that speeds information. The first such industry
that I would tell you about is Reuters. Mr. Reuters, I think in the
1840s before the telegraph, created a financial information
service using carrier pigeons. You know, these are birds. So, when he was in London with
some information, they had carrier pigeons that were
brought from Paris, and as soon as new information came
out, they would tie it to the foot of the little bird and
they’d let it go, and it would fly to Paris. It would go to its roosting
place, the message would be read, and subscribers
would be notified. And that was no joke, that
really worked, because you would have information hours or
even days before everyone else in Paris. So, you could make a killing. So, Reuters today, it’s now
called Thomson Reuters, is still in that business. The carrier pigeons were
a brilliant idea. But they had to keep up with
the times, because, shortly thereafter, the telegraph was
invented and pigeons no longer were the leading technology. But maybe that was the beginning
of the information age, with pigeons. Now we have beepers, and
we have the internet. A beeper is something you can
carry in your pocket that beeps when there’s
financial news. So, suppose a company makes an
announcement that that it has, say, a new drug that’s
successful in trials. As soon as they make the
announcement, it then goes out electronically everywhere, and
the beepers start beeping. And all the investment analysts,
they drop their morning coffee, because they
know they have to act fast. So, you get this new
announcement. Now it’s t plus 20 seconds. He’s got his drug specialist
on the phone. What does this mean? Quick, how much is it
going to go up? And so the guy says, I don’t
know, first thought, maybe it’s going to go
up $2 a share. OK, it’s only gone up $1 a
share, I’ll buy right now. And now it’s two minutes after
the announcement, and then the analyst says, I’ve thought about
it a little bit more. No, I only think it’s
$1.50 a share. This is homing in, and so the
price is jiggling around rapidly as all this is happening
for a few minutes, and then it settles down. Because after — I may be exaggerating — after
10 minutes, they’ve kind of figured it out and it’s
reached its new level. They’ll be thinking about it the
next morning when they’re taking their shower, and they’ll
get a better and better, more refined idea
of what the price is. But here’s the Efficient
Markets theme. You, the next day, read about it
in the Wall Street Journal in the morning. You’re now 24 hours late. So, you call your broker and
say, maybe I should buy this stock, they’ve got this
new breakthrough. Your broker might laugh
at you, right? Because you’re 24 hours late. And what do you know, anyway,
about pharmaceuticals? That’s where the Efficient
Markets Hypothesis is true. You can’t expect to routinely
profit from information that’s already out there. If you’re going to profit,
you’ve got to come up with something faster, something
that you can get faster. I think this is somewhat what
David Swensen was referring to yesterday
[correction: last lecture], when he talked about different
asset classes. Remember how he talked about
comparing the top quartile and bottom quartile of investment
managers, in terms of returns, or at different asset classes? Well, the managers weren’t
able to beat the bond market very much. The top quartile wasn’t
able to beat the stock market very much. But when you get to unusual
assets, private equity, which is not traded on stock
exchanges, or absolute return investments that he talked
about — they’re unusual, smaller, rare investments that
the public doesn’t have a lot of information about. And these guys can get ahead
on those things. So, part of what makes Swensen a
success is picking his game. Even so the stock market is not
completely efficient, but it’s so much more efficient,
because it’s so many people involved in it and
watching it. So, maybe I should write here,
because this is what we’re talking about here. The Efficient Markets
Hypothesis. I’ll write it down. This is the name for this
idea that was coined — or sometimes people say
Efficient Markets theory. They’re referring to what Conant
and Gibson and other people had been talking about
for a long time, and it was common knowledge. But the first person to use this
term apparently was Harry Roberts, a professor at the
University of Chicago. But he was made famous by Eugene
Fama, who referred to it as Harry Roberts’s idea. Eugene Fama is maybe the
best-known finance professor in the country, I think. He is also at the University
of Chicago. He’s been talked about as a
Nobel Prize candidate for a long time, and he should have
won probably, even though his theory is not entirely right. I think his chances of getting
it have dimmed a little bit, because the theory is not looked
upon as quite such an absolute truth as
it used to be. As I said, it’s a half-truth. What Conant said is all
well-taken and right, but there’s other nuances and
it’s not exactly — when you first read Conant, you think, the guy is brilliant. That’s what I thought,
this is right on. Then you read it again, you
think, well, you know maybe there is a little gambling
in the financial market. Things don’t always
work right. So, he was maybe a little
bit too positive. I can give a little
history of this. It was in 1960. The University of Chicago
is kind of the forerunner in this. In 1960, Ford Foundation gave
a grant to the University of Chicago to assemble all stock
price data back to 1926. And to get it right. So, they set up the Center for
Research in Securities Prices at Chicago. The Center for Research in
Securities Prices, or CRSP, as it’s called, had a Ford
Foundation grant to go to the stock exchanges in the United
States and get all the data, and get it right. Remember, I told you they
have splits in stocks. So, you see the price of a
share, and then suddenly the price will fall in half or maybe
it will double, because they changed the units
of measurement. If you wanted to know, what is
the price history of stocks over the long haul, no one had
ever organized that and figured out things like that,
and got it right. And when were the
dividends paid? When did you actually
get the dividends? So they said, let’s
get it right. Let’s put it on — hey, this
is really super modern — a UNIVAC tape. That’s a computer tape. We’ll put it on a tape and
we’ll sell it at cost to anybody in the world. And so the Ford Foundation,
which is a non-profit, said: Good idea, let’s do it. So, that CRSP tape has launched
a revolution in finance, because nobody
had the data. They were throwing it away,
it was not used. How do you know what Sharpe
ratios are if you don’t have the data? I mean, you could find it in
newspapers, but it wasn’t organized right, it wasn’t
set up right. So, with the invention of the
CRSP tape in 1960, it really gave impetus to the Efficient
Markets revolution. And by the end of that decade,
there were thousands of articles testing market
efficiency using the CRSP tape. And, in particular in 1969,
Eugene Fama wrote one of the most cited articles in the
history of finance. It was called “Efficient Capital
Markets: A Review”. So, he reviews all of these
studies of the CRSP tape. And it looked authoritative for
the first time, because we were using the whole universe
of stocks, all the way back to 1926. That sounded like a long time,
and a lot of data. And Fama said it’s not uniform,
there are some negative results, but the
evidence is that markets are remarkably efficient. And this is a truth that
we’ve discovered. And that was really a bombshell,
because he was discrediting, or seeming to
discredit, practically all the investment managers
in the country. It was a huge industry. And he’s claiming the successful
ones must have just been lucky, because the market
is so efficient that we can’t see any way that you could
make money in the market. The high point of the Efficient
Markets Hypothesis was probably in the 1970s. I’ll call that the high point. It seemed, at that time, that
all of the scholars were finding that there was no
way to beat the market. But it started to deteriorate,
the support for the Efficient Markets Hypothesis. There’s been a change
in thinking. Efficient Markets is still
regarded with respect, but not the same respect that it
had in 1969 or in 1979. I have here some indication of
how thinking has changed about Efficient Markets. The textbook that I used to
use for this course — I’ve been teaching this
course for 25 years — Fabozzi et al. wasn’t even written when I
started, so I was using a textbook called Brealey
& Myers, Principles of Corporate Finance. I still have all these
old editions. It’s a very successful
textbook. Since I was teaching out of it
all those years, I went back and looked — I don’t have the first edition
of that book, I have the second edition, 1984 — and I
was teaching out of it in this same class in 1984. At the end of that book, there’s
a chapter on the seven most important ideas
in finance. And one of the ideas is
Efficient Markets. And quoting the textbook,
Brealey and Myers say: “Security prices accurately
reflect available information and respond rapidly to new
information as soon as it becomes available.” They they
do qualify it, in 1984. “Don’t misunderstand the
Efficient Markets idea. It doesn’t say there are
no taxes or costs. It doesn’t say there aren’t some
clever people and some stupid ones, it merely implies
that competition in actual capital markets is very tough. There are no money machines and
security prices reflect the true underlying value of
assets.” Let me repeat that: “Security prices reflect the
true underlying value of assets.” That’s a pretty strong
statement, right? But that’s Efficient Markets. They just said it in their
second edition of the book. Not many people would
say that, right? Trust the stock market, don’t
trust people you know and love and trust. Trust the
stock market. Well, they deleted that from
later editions of their book. I think that’s a sign
of changes. So, I was looking at
the 2008 edition. They’ve now taken on
a third author. They’re getting tired of coming
out with more and more editions of their book, so
they’ve taken on Franklin Allen from the Wharton School. They’ve deleted what I just
read, and now it says — I’m quoting from them: “Much
more research is needed before we have a full understanding of
why asset prices sometimes get so out of line with what
appears to be their discounted future payoffs.” That’s
a complete turnaround in the textbook. This is one of the most popular
textbooks, and they’ve changed completely. So, I think we have an idea that
started around the 1960s. It was somehow associated with
computers, and electronic databases, and modern
thinking, and mathematical finance. They kind of went
too far with it. They concluded that you just
can’t beat the market. Another thing I put on the
reading list is a reading from The New Yorker magazine. It just came out in December. I thought it was relevant. It’s by Jonah Lehrer and the
title of the article is “The Truth Wears Off: Is There
Something Wrong With the Scientific Method?” I don’t
think there’s anything wrong with the scientific method, but
I was interested in this article, because what The New
Yorker article points out is that a lot of scientists —
and this is outside of finance, I’m making a parallel
here — a lot of scientists who follow careful scientific
procedures seem to generate results that are later
discredited. And nobody can figure out why. It’s like the universe
is changing. He gives an example in The
New Yorker article — and this is from drugs — there’s a class of drugs called
second-generation antipsychotics. These are used for people who
are either schizophrenic or — I guess it can be used more
generally than that — some of the drugs are called Abilify,
Seroquel, Zyprexa. When these drugs were first
introduced, careful studies that passed muster in the best
medical journals found that they were highly effective. And they were written up as a
godsend, a way of dealing with problems that used to
weigh on people. Wonderful. The medical procedures involved
careful controls on studies including a double
blind procedure. When you want to test a drug
on human subjects, both the subject doesn’t know whether he
or she is getting the drug, and the experimenter, who runs
experiment, doesn’t know which one is the drug. So, you give bottle A and bottle
B to the experimenter, and the experimenter is never
told, which one is Zyprexa and which one is a placebo. And then the experimenter has to
write up a whole report on drug A and drug B,
not even knowing. This is to eliminate
any possible bias. So, these drugs passed that. You see what I’m saying? The controls were right,
everything was good. And as years go by, the tests
start coming out — the new attempts to replicate
those start coming out more negative. They didn’t disprove the drugs,
they just weren’t such wonder drugs as they thought. So, how can that be? And what the article says, well,
it must be that somehow scientific bias, when there’s
an enthusiasm for some new theory, it creeps in even
if you try to make the strongest controls. You say, how could that happen
with a double blind procedure? Well, maybe they broke the
double blind somehow. They tried, but the guy,
experimenter, figured it out. And then he started not
deliberately fabricating results, but it’s the kind of
thing where one subject says, I didn’t take my Abilify
regularly. I took two tablets last week. I have to decide whether to
throw this person out of the sample, and then I kind of
remember that the drug wasn’t working for this person, and
it colors my judgment, so I throw them out. And another thing that happens
is that the studies that didn’t find it might have
been suppressed. Someone might have done
an Abilify test and gotten bad results. And then showed it to their
superior and said, should I publish this? And the superior said, wait
a minute, there must be something wrong here. Abilify is wonderful,
so let’s look. And then they find something
that might be wrong, and he says, you shouldn’t
publish this, because they find something. So, the publication process
is biased for a while, but eventually it catches up. So, I think the same thing
happened with the Efficient Markets Hypothesis. In the initial enthusiasm,
anybody who found that the Efficient Markets Hypothesis
wasn’t supported by the evidence, that person would
be told, look again. Maybe you’ve done
something wrong. So that’s what happened. I wanted to do a little bit more
history and describe the concept of Random Walk, which
is central to the Efficient Markets Hypothesis. Let me start, though, with a
little bit more history. Technical Analysis. This term goes back — must be over 100 years. Technical analysis is the
analysis of stock prices, or maybe other speculative asset
prices, by looking at charts of the prices and looking for
patterns that suggest movements in prices. The classic text of technical
analysis is Edwards and McGee. McGee, there’s a famous
story about him. He was not a professor, he was a
Wall Street analyst. And the story about him is that he
believed that you look at the prices and you can
predict prices. And in fact, he said, I don’t
want to look at anything else. I just want to see prices. I’ll do plots, and I can — using my judgment, I
can figure out what it’s going to do. And so, the story about McGee
is, everyone on Wall Street wants the corner office
overlooking the World Trade Center, whatever. He said, I wanted an interior
office with no windows. I don’t want any distractions,
I don’t want the real world impinging in my judgment. And so, that’s McGee. But he said that there
are certain things that you see obviously. For example, resistance level. When the Dow Jones Industrial
Average approached 1,000 — I think it was in the 1960s — it’s way above that
now, as you know. But when it first approached
1,000, technical analysts said, you know maybe it’s going
to have trouble crossing 1,000, because that’s a
psychological barrier. That’s sounds magical, how can
the Dow be worth over 1,000? Wow, I’m going to sell. And so, the idea was that people
would sell when it approached 1,000. And the technical analysts
seemed to be right, because the Dow bounced around just
below 1,000 for a long time. I guess it was months or a year,
like it couldn’t cross the resistance level. That’s one example. I have another example,
which is from Edwards and McGee’s book. [SIDE CONVERSATION] PROFESSOR ROBERT SHILLER: That’s
Edwards and McGee, this is one of the patterns. McGee was actually a student of
psychology, and he thought certain kinds of patterns
seemed to have really spooked people. And this is one pattern, which
he called ”Head and Shoulders.” That’s the head,
that’s one shoulder, that’s the other shoulder. He said, when you see this
pattern, watch out. It’s going to, actually, totally
collapse, as it is shown doing. These are stock prices
plotted against time. These are days, each of
these points is a day. And this is from their book,
so it’s hypothetical. You hardly ever see
such perfect Head and Shoulders patterns. And so, maybe that’s Edwards
and McGee’s most famous. Head and Shoulders. So, the question is,
does it work? Does it really work? In the early 1970s, when the
efficient markets hypothesis was really strong, Burton
Malkiel, who was a professor at Princeton, and then later
he was the Dean of the Yale School of Management, wrote a
book called A Random Walk Down Wall Street, which claimed
that technical analysis was bunk. And he said many studies have
shown that it doesn’t work. This Head and Shoulders
doesn’t work. None of Edwards and McGee’s
stuff worked. There were lots of studies, and
I actually met him at a cocktail party after
his book came out. And I said, you didn’t footnote
all those studies about technical analysis. Where are they? I can’t find them. I did a search. Not on the internet, I did it
on something else, but I was able to search. I couldn’t find them,
where are they? And I found that he didn’t
have an immediate answer. I suspect that he was
extrapolating — there was a literature on testing
market efficiency. They looked for things
like momentum, whether that continued. But there was something a little
bit wrong with the literature. Not many people really
confronted technical analysis. Later, there were people who
did look at some of Edwards and McGee’s points, and
they found some element of truth to them. So, I think the answer is, McGee
wasn’t a total idiot, as you might infer from the
Efficient Markets Theory. But it’s not going to make
you rich, either. If anything, technical analysis
is a subtle art that can augment trading
strategies. I bet David Swensen doesn’t
do it at all. I could have asked, I
don’t know for sure. Let me talk about Random
Walk, which is a central idea in finance. The idea is that, if stock
prices are really efficient, then any change from
day to day has to be due only to news. And news is essentially
unforecastable. Therefore, stock prices
have to do a random walk through time. That means that any future
movement in them is always unpredictable. The changes are totally
random. So, the term Random Walk
is an important term. It was coined not by a finance
theorist, but by a statistician, Karl Pearson,
writing in the scientific journal Nature in 1905. Now, he didn’t link
it to finance. But what he said is, the
movements in some — well, he was thinking theoretically. Actually, I believe he used
the example of a drunk. Let’s take someone who
is so drunk that each step is random. This person has no direction at
all, staggering randomly. So, he starts out
at a lamp pole. And what would you predict —
this is what Pearson asked — what would you predict is his
position in 10 minutes? He happens to be at a
lamp pole right now. And what Pearson said is, well,
your best forecast is that he’s right where
he is now. Because you have no bias. He could go in any direction,
equally likely. So, what’s most likely? It’s that he stays right
where he is. And what is the probability
distribution? Well, it turns out that the
standard deviation around that point goes up with the square
root of n steps. Because each step is independent
of the other, so the square root rule applies. So, if you’re asked to forecast
his position after an hour — that’s a lot of steps
— you would say, I predict he’s right where he is now, but
I now have a big standard deviation around it. Pearson’s article is
a very simple idea. Among the readers, apparently,
was Albert Einstein and Norbert Wiener, the
mathematician who had invented a continuous version
of the Random Walk, called the Wiener Process. But it got into finance later. And in the Efficient Markets
revolution, they started to realize that stock prices look
more like more like a Random Walk than a Head
and Shoulders. You look at these Head and
Shoulders patterns and they’re hard to find. So, what is a Random Walk? Let me just define it. A Random Walk is where you have
a series x sub t equals x sub t minus 1 plus epsilon
sub t, where epsilon sub t is noise. Just unforecastable
noise: mean 0 and some standard deviation. Ideally, it would be normally
distributed, so it would have a bell-shaped curve, and then
the math would be very easy and very simple. So, I want I want to contrast
that with an alternative, which is called a
”First-Order Autoregressive.” Let me
get my notation here. Let’s take a process that starts
at 100 — that’s like the lamp post. We’ll say x
sub t equals — how am I putting this — 100 plus some number rho —
that’s a rho — times x sub t minus 1 minus 100 plus
epsilon sub t. So, this is an AR-1. That’s First-Order
Autoregressive. It’s like a regression model
where 100 times 1 minus rho is the constant term. And the coefficient of
the lagged x is rho. And we usually require that rho
is between minus 1 and 1. Normally, rho is positive. So, that means that it’s
mean-reverting, but slowly. First of all, in a special case
where rho equals 1, I wouldn’t call it an AR-1
anymore, because it reduces to a Random Walk, right? If you make rho 1, then the
constant term drops out. I’ve got 100 minus 100 — there’s no constant term —
and then I’ve got x sub t equals x sub t minus 1
plus epsilon sub t, that’s a Random Walk. So, in the extreme case where
rho gets to one, then a First-Order Autoregressive
process converges to a Random Walk. I wanted to show you some
this here is a plot I had. Let’s first look at
the black line. The black line is the Standard &
Poor’s composite stock price index in real terms. I have that
from 1871 until recently. That’s just there for
comparison, that is the actual stock market. The pink line is a Random Walk
that I generated using this formula [correction: The pink
line is a Random Walk with a time trend, to be explained
below.], and a random number generator that generates random
normal variables. I started them out at the same
level, but don’t they look kind of similar? If you look at the stock market
without comparing it with a Random Walk, it looks
like it has patterns in it. In fact, here’s a Head and
Shoulders, right here. Bang, bang, bang. When is that? I think this is 1937. This is — I am not sure — 1930, 1931? And this is just
before the war. I’m not sure exactly. It’s a nice Head
and Shoulders. Hey, Edwards and McGee are
sort of right, right? It dropped a lot after that. You know, I can find Head and
Shoulders up here, too, right? Maybe. The black line is actual
U.S. history, that’s the stock market. The pink line is a fake stock
line generated with pure random noise. And the fact that it’s going
up is just chance. I can actually use
this program to generate other examples. This should change, let
me see, make sure it’s working here. The black line is the same. I’m not going to change
the black line, the black line is history. I just did a brand new Random
Walk calculation using my random number generator, which
is there on Excel. That looks pretty good,
doesn’t it? I’m going to do more for you. Which one is the real
stock market? I find that hard
to tell, right? The insight is that people get
deceived when they look at stock price charts. They think they see patterns. That pink line is guaranteed to
have no patterns, because I generated it, so that there
are no patterns, except random patterns. But when I look at this pink
line, which just came up, look at that up-trend. Wow. That’s called a bull market,
when it goes up. And I can make all kinds of
theories about why that’s happening, but you know those
would be fake theories, because I know what’s
really happening. This is pure randomness. I’ll do it again, I can
do this forever. That’s another one. Here the trend wasn’t
quite so positive. Oh, actually, I have to say
one thing, I forgot. I did put an up-trend
in the Random Walk. I am sorry. In this simulation, I did
add a constant, so that it pushed it up. Otherwise, it was
a Random Walk. I forgot what I did. It was a Random Walk
with trend. But it doesn’t guarantee
that it will go up, because it’s random. I’ll do another simulation. See, how fast I can do these? It’s the wonder of
modern computers. If this were our history, people
would say, the amazing stock market of the first half
— look at that stock market in the first half of
the 20th century! We would be devising all kinds
of theories to explain it, but in fact it’s just nonsense. It’s just randomness. I’ll do a few more. Look at that. Boy, that would be
a hump shape for the whole 20th century. We’d have historians trying
to figure that one out. Oh, this one downturned. This is a bad outcome. Jeremy Siegel would not be
pleased with this outcome, because it has the stock
market gaining nothing in 100 years. These are all equally
likely outcomes. Look at that one. If that was the world that we
inherited, we would really think there was a linear trend
in the market, right? People would be making
models, saying — look at how straight
that line is. The point is that you start to
see a sort of reality which is really just randomness. That’s why Nassim Taleb, a
friend of mine, wrote a book called Fooled by Randomness. I thought that was a
great title for a book and a great book. People don’t understand how
things are just purely random, and your mind tries to make
sense out of them. And you start looking at
patterns and the patterns don’t mean anything. So, I can just keep doing this,
but maybe I’ll stop. Look at that one. Of course, I’m helped along by
the trend that I added in. So, I want to see if I can
get a down-trend one. Because I put a trend in, it’s
hard to get a real down-trend. That’s sort of a down-trend. That’s where there were a
lot of negative shocks. So, you see the comparison of
the Random Walk with the actual stock market. So, the actual stock
market looks a lot like a random walk. One thing is different, though,
you don’t — look at this pattern, here. In 1929, and this is the
crash, after ’29. You know, I’m not getting that
in any of my simulations, and you know why I’m not? Because I chose a normally
distributed shock. No fat tails, I didn’t put fat
tails into my simulation. And you didn’t notice
that, right? But in this simulation, we never
see such sudden drops. So, there’s something that, if
you spend time searching on it, you might see something
not quite right. But basically, the Random Walk
looks a lot like the actual stock market. I’m trying to get one that
really matches up, but I’m not quite succeeding. That’s pretty good, isn’t it? In this simulation, 1929 wasn’t
quite as strong and the Depression wasn’t as bad. Anyway, what I want to do now
is compare the Random Walk with the AR-1. Now I’m going to do the same
thing, but I’m going to do it with — [SIDE CONVERSATION] PROFESSOR ROBERT SHILLER: I
know what I did, sorry. The pink line is now
an AR-1 process, which is this process. So, it’s mean reverting now. I’m not comparing the Random
Walk with the AR-1, I’m just doing the same thing
now with an AR-1. Now, the thing about AR-1 is,
you realize that it wants to come back to 100. I put a trend in. So, it’s actually coming back
to a linear up-trend. What I did is I put in
a time trend as well. But the point is that it tends
to hug the trend somewhat. I have it here shown
not around a trend, but around 100. What an AR-1 does is,
say rho is 1/2. If rho is 1/2, then it means
that if x sub t minus 1 was above 100 — last period it was
above 100 — it will be above 100 this time,
but only half as much above 100, so it’s going
back to 100. And then the next time, it’ll
only be half, again, as much above 100 as it was
the last time. So, it tends to go
back to a trend. But what I’ve shown here is
a simulation with a random number generator of an AR-1
around a trend, where the trend matches the actual trend
in the stock market. Now, in this case, this
is not Random Walk. And there is a profit
opportunity, and the profit opportunity is when it’s below
trend, buy, when it’s above trend, sell. Because it will tend to
come back to trend. I chose a rho which was very
small, something like 1/2, so it tends to come rapidly
back to trend. This does look different than
the actual stock market, doesn’t it? You see how much it
hugs the trend? So, it doesn’t seem
to fit as well. I can do simulations
of this, too. This is different. You can see the difference,
right? This pink line doesn’t look as
much like the actual stock market, because it really
wants this trend. We saw trendy ones occasionally,
by chance with a Random Walk, but here we’re
seeing it’s always on a trend. And so, in this world, if the
stock market were an AR-1, there would be a profitable
strategy. Always buy when it’s below
trend, and sell when it’s above trend. Because you know it’ll come
back, you see how reliable this comes back to a trend? So, you can see that there’s a
fundamental difference, the Random Walk seems to fit the
data better than the AR-1. The Random Walk theory says
that stock prices are not mean reverting. Where they go from today
is all random. If they’re above the historical
trend, it’s meaningless. The historical trend
is just nonsense. It’s just random. And forget trends,
forget anything. It’s always the drunk at the
lamp post, no matter where you are in history. This one, if rho is
substantially less than 1, it looks a lot different
doesn’t it? With rho equals 1/2, this is
not the world we live in. It would be too easy
to make money. All these little oscillations
around the trend I could profit from. But in the real world,
it’s not like that. But suppose the real world is
AR-1 with rho equal to 0.99. What about that? Well that’s not much different
from a Random Walk, is it? It’s going to be hard to
tell the difference. In the Efficient Markets Theory
period, people were really excited about the
Random Walk Hypothesis. That’s why Burton Malkiel’s
book, A Random Walk Down Wall Street, which he came
out with — I think it was in 1973,
right after Fama. It became a huge best seller, it
sold over a million copies, because at that time people
were thinking, this is exciting new wisdom. We’ve learned that the stock
market is a Random Walk. And there’s all kinds of
implications for that. The problem is — I have to wrap up — that the Random Walk Hypothesis wasn’t exactly right. It’s sort of right, you’ve
gotten some insights. But you know, maybe the real
world is AR-1 with a rho close to 1. And in that world, there are
profit opportunities, but they take a long time to come. So, if the real world is AR-1
with rho equal to 0.99 or 0.98, that means you
can buy stocks when they’re below trend. But then you have to wait
10, 20 years for them to get back to trend. So, it’s like the drunk
on the lamp pole. The drunk is standing next
to a lamp pole, it was a random walk. But now we put in elastic band
around the drunk’s ankle and tie it to the lamp pole
and it pulls him back. Now, if we have a very loose
elastic, this guy can wander for a long time, but will
eventually be pulled back. You’d never know when. But if you have a tight elastic,
then it would be obvious that the drunk
is coming back. The problem is that the real
world seems, maybe, to be more like the drunk with
the loose elastic. And so, it’s kind
of unsatisfying. You can beat the market, but
simple trading rules like Edwards and McGee are not
powerful, short-run profit opportunities. In that sense, the Efficient
Markets Hypothesis is right. So, don’t forget the Efficient
Markets Hypothesis. I’ll repeat what I said
at the beginning. It’s a half-truth,
it’s half true. Remember that, but don’t put too
much faith in it, either.

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