Eugene Fama grew up in Boston, a third generation Italian-American.
While an undergraduate at Tufts University, he excelled in athletics
and majored in French-an inauspicious beginning for a future giant
in the field of economics. But he also worked for a professor
who was trying to develop "buy" and "sell" signals based on price
momentum. Although the theories the professor devised worked well
when applied to the past, they worked poorly when Fama tested
them in real time. That puzzle, plus the skills that he acquired
evaluating stock market data, drew Gene Fama to business school.
After earning his doctorate at the University of Chicago, he joined
the faculty there in 1963.
A simplified version of his dissertation, "Random Walks in Stock
Market Prices," was published in Institutional Investor
magazine, provoking a stir. It was Gene's article that introduced
the still-controversial efficient market theory to the investment
community. (There are many variations of the efficient market
theory, but they all postulate that stock prices promptly and
fully reflect all public information.) Very few academics specializing
in investment research have any audience in the investment community,
but that article made Gene Fama very well-known on Wall Street.
But he is an academic and technical terms are used in this interview.
We covered some of these terms earlier in the book, but here's
a quick, but non-scientific, refresher course on some of the lingo:
Peter J. Tanous: How did you first get interested in stocks?
Fama: As an undergraduate, I worked for a professor at Tufts
University. He had a "Beat the Market" service. He figured out
trading rules to beat the market, and they always did!
I beg your pardon?
They always did, in the old data. They never did in the new
data [laughter].
I see. Are you saying that when you back-tested the trading
rules on the historic data, the rules always worked, but once
you applied them to a real trading program, they stopped working?
Right. That's when I became an efficient markets person.
Okay. Let's get into it. You're known for your work on efficient
capital markets. In fact, on Wall Street, the phrase "efficient
market" is often attributed to you. I believe you and Ken French
made the point that stock market returns are, in fact, predictable
over time. How does that jibe with the random walk theory?
The efficient market theory and the random walk theory aren't
the same thing. The efficient market theory is much more powerful
than the random walk theory, which merely postulates that the
future price movements can't be predicted from past price movements
alone. One extreme version of the efficient market theory says,
not only is the market continually adjusting all prices to reflect
new information but, for whatever reason, the expected returns-the
returns investors require to hold stocks-are constant through
time. [For example, we know that, since the '20s, returns on
the New York Stock Exchange common stocks have averaged a little
over 10% per year.] I don't believe that. Economically, there
is no reason why the expected return on the stock market has
to be the same through time. It could be higher in bad times
if people become more risk-averse; it could be lower in good
times when people become less risk-averse.
So risk is the component that leads to how much you get
paid?
It could be just taste, too, you know. People's taste for holding
stocks can change with time. None of that is inconsistent with
market efficiency and it can give rise to some predictability
in returns. The predictability is simply based on the returns
people require to hold securities.
But, in one of your papers, you did refer to the predictability
of returns over time. Is that just the investor getting paid
for the risk he was willing to take? Is that the point?
It could be that or it could be that people are simply more
risk-averse in bad times.
On a related subject, I think you also said that fundamental
analysis is of value only when the analyst has new information,
which was not fully considered in forming current market prices.
When I hear that I say: Hey Gene, that's the point! The analyst
believes he knows something, or infers something, that other
analysts don't see. He sees an evolution taking place or he
believes this company is doing better than people think, and
that's why he gets paid millions of dollars on Wall Street to
pick stocks. What's wrong with this thesis?
Well, not everybody can have that talent. In fact, as far as
I can tell, not many do. The system is designed to make that
very difficult. By that, I mean that under US accounting [and
regulatory] systems, if you reveal anything, you have to reveal
it to everybody.
Fair enough, but what if the analyst is making a judgment
on the future prospects of the company. For example, the analyst
might say, "The Street says this company is going to earn $0.82
per share and I say it's going to earn $1.10 because I'm seeing
order flow, consumer demand, customers' tastes for the product
and what have you." Now, if the analyst is right, he's worth
the millions he gets paid. My question is: in your thesis, if
he's right, is he right because he's so smart or just because
he's lucky?
For the most part, I think it is luck. The evidence is pretty
strong that active management doesn't really do better than
passive management.
Except, of course, when we start talking about the so-called
outliers, those managers, like the Gurus in this book, who have
persistently outperformed the market. That, in turn, leads to
the other great exercise in our business, particularly with
mutual funds, which is the predictability of future investment
success based on past success. I know you've done some work
on that, too.
One of my students just finished his thesis on that subject,
actually. What he found was that performance does repeat when
it's on the negative end! In other works, funds that do poorly,
tend to do poorly persistently.
Why couldn't one postulate that the same would be true at
the other end of the spectrum?
One could postulate it, but it doesn't seem to be true. On
the negative end of the spectrum, you have things like turnover
and fees and all that kind of stuff, which can explain why you
have negative persistence in poor returns.
Yes, but good managers trade and charge fees, too. They
might even deserve them more!
Poorly performing funds tend to be higher fee and higher expense
funds. In fact, when my student adjusted for fees and expenses
he could explain most of the persistent under-performance.
One thing I did a couple of years back was take all the funds
that survived from the beginning of the Morningstar tapes, which
is 1976. Now, funds that survive that long will have survivor
bias built into the test, because only the successful funds
survive. So I split the sample period in half and took the 20
biggest winners of the first 10 years, or the first half of
the period, and I asked how did they do in the second half of
the period. Well, in the second half of the period, half of
them were up and half of them were down.
Wow. Half were up and half were down? [That indicates that
there was no predictive value in the fact that these managers
all finished in the top half in the first ten year period.]
Exactly half, relative to a risk-corrected model.
How did you adjust for risk?
I used the three-factor model.
The three-factor model takes into account market risk; value
versus growth styles; and also size, which is the large-cap
stocks versus small-cap distinction, right?
Yes. But since most retail funds have a bias toward growth
stocks, the adjustment helped them.
So even risk tested, the data came out 50/50, which means
that the mutual funds that did the best for ten years only had
a 50/50 chance of repeating their success. I'm curious to know
who the biggest winner was in both periods?
Fidelity Magellan.
What's the reason for that?
Obviously, the performance of that fund has been really good.
It has, to Peter Lynch's credit. Another issue you have
addressed: that old subject, value stocks versus growth stocks.
Are stocks of good companies good stocks to invest in?
They're good stocks, they just don't have high expected returns.
Then growth stocks are stocks of good companies, not good
stocks, right?
To me stock prices are just the prices that produce the expected
returns that people require to hold them. If they are growth
companies, people are willing to hold them at a lower expected
return.
As we get into this, I think our readers are going to be
surprised to read that value stocks are riskier than growth
stocks. That is counterintuitive.
I don't know why it's counterintuitive.
Well, we used to think of value stocks as stocks that may
have already had a decline, that are languishing. We believe
we're buying value stocks at the bottom and waiting for them
to go back up again.
Value stocks may continue to take their knocks. Their prices
reflect the fact that they are in poor times. As a result, because
people don't want to hold them-in our view because they are
riskier-they have higher expected returns. The way we define
risk, it has to be associated with something that can't be diversified
away. Everybody relates to a market risk. If you hold stocks,
you bear stock market risk. But the stock market is more complicated
than that. There are multiple sources of risk.
In our business, we usually associate growth stocks with
high earnings multiples, and value stocks with low earnings
multiples. Multiples are themselves usually an element of risk.
So, if a growth stock falters on its anticipated growth path,
it declines precipitously because it no longer deserves the
multiple that had previously been awarded to it when its prospects
were better. Therefore, a lot of people think that growth stocks,
in fact, are riskier. What's wrong with that thesis?
Just look at the data. It's true that growth stocks vary together,
and it's true that value stocks vary together. In other words,
their returns tend to vary together, which means that there
is a common element of risk there. Now, for growth stocks that
seems to be a risk that people are willing to bear at a lesser
return than the return they require for the market as a whole.
Whereas, if I look at the value stocks, which we also call distressed
stocks, their returns vary together, but people aren't willing
to hold those except at a premium to market returns.
So you're saying that I expect to make more money when I
buy value stocks than I do when I buy growth stocks.
Right. On average. Of course, sometimes you get clobbered.
We've always associated the risk of getting clobbered more
with growth stocks than with value stocks that have already
taken their lumps.
The data don't support that.
The other dimension, of course, is size. Now the size effect
is very easy for those of us in the investment community to
accept. The notion that small companies are riskier than large
companies seems obvious.
That's not the reason the community accepts it. What they think
is that small companies pay higher returns because they're unknown,
or something like that. It's not because they're more risky.
The risk, in my terms, can't be explained by the market. It
means that, because they move together, there is something about
these small stocks that creates an undiversifiable risk. That
undiversifiable risk is why you get paid for holding them.
What causes that risk?
You know, that's an embarrassing question because I don't know.
Fascinating. I would assume that the risk is that small
companies have a lower survival rate than large companies.
No. That's not it at all. The good news and the bad news about
that is that the reason small companies don't survive is because
some of them fail, others get merged; that's bad news and good
news. Here's a fact I always use. First I say I don't know,
but then I say it's fair. Here's my example. The 1980s were,
supposedly, the longest period of continuous growth the country's
seen since the second world war. Yet, in that decade, small
stocks were in a depression. Small stock earnings never recovered
from the '80-'81 recession. They were low the whole decade.
The market was fooled every year by that, because in every previous
recession, the small companies came back. Why did that happen
in the '80s? I don't know. But it happened. And it tells you
there is something about small stocks that makes them more risky.
Another question that comes up frequently is if markets
are correctly priced, how do you explain crashes when they go
down twenty percent in one day?
Take your example of growth stocks. If their prospects don't
go as well as expected, then there will be a big decline. The
same thing can happen for the market as a whole. It can also
be a mistake. I think the crash in '87 was a mistake.
But if '87 was a mistake, doesn't that suggest that there
are moments in time when markets are not efficiently priced?
Well, no. Take the previous crash in 1929. That one wasn't
big enough. So you have two crashes. One was too big [1987]
and one was too small [1929]!
But in an efficient market context, how are these crashes
accounted for in terms of "correct pricing"? I mean, if the
market was correctly priced on Friday, why did we need a crash
on Monday?
That's why I gave the example of two crashes. Half the time,
the crashes should be too little, and half the time they should
be too big.
That's not doing it for me. What am I missing?
Think of a distribution of errors. Unpredictable economic outcomes
generate price changes. The distribution is around a mean-the
expected return that people require to hold stocks. Now that
distribution, in fact, has fat tails. That means that big pluses
and big minuses are much more frequent than they are under a
normal distribution. So we observe crashes way too frequently,
but as long as they are half the time under-reactions and half
the time over-reactions, there is nothing inefficient about
it.
Let's go back to value stocks versus growth, and large versus
small stocks. Tell us why the three-factor model contributes
to our knowledge of risk in investments.
The three factors are the market factor, the size factor, and
the distress [value] factor. We distinguish between distress
and growth. What we find is that, in addition to the market
factor in returns, in other words the fact that stocks move
together, it's also true that small stocks move together, and
big stocks move together, but not in the same way. The value
stocks move together and the growth stocks move together but
the two groups are different from each other. There are at least
three dimensions of risk: market risk, small stock versus big
stock risk, and distress stock versus growth stock risk. When
I say risk, I mean that these groups move together. We could
have found that they didn't move together, and then it would
have been market inefficiency.
What would that have told us?
It would have told you that you could get a diversified portfolio
of small stocks, and a diversified portfolio of big stocks,
short the big stocks and buy the small stocks, and get a positive
return with no risk.
Why would that be true?
It would only be true if there weren't a common factor in the
return on small stocks that caused them to have randomness that
wasn't shared with big stocks.
I'm not sure I follow.
If there's no small stock risk, and I take a diversified small
stock portfolio, I would be able to explain its return entirely
in terms of the market risk. So there's nothing left over.
I see. We're comparing small stock returns to the market
as a whole. What you're saying is that small stock returns have
risk that's not explained by the market. And this higher risk
is the size risk you talk about in the three-factor model? Is
that correct?
Right. Take a diversified portfolio of value stocks. Those
stocks will move together. That portfolio's return will not
be perfectly explained by the market even if it has a few thousand
stocks in it.
If that's the case, wouldn't growth stocks mirror the market
as a whole?
Growth stocks do come closer to mirroring the market as a whole.
So once you've decided to take the market risk, creating
your portfolio seems to come down to deciding what your overall
risk level is, and then you allocate by size, and between growth
and value, to achieve your risk/reward goals.
Have there been any studies that have ever impressed you
about active management in any capacity? I mean, has there been
any evidence that would suggest to you that all of the Wall
Street analysts, gurus, salesmen, and research departments are
anything but a complete waste of time?
You used the key word: salesmen. I might be willing to say
that the people who get pointed at consistently, who have shown
consistent performance even after they have been pointed at,
really do have something. These are always the same people,
Warren Buffet, Peter Lynch, and then who?
Okay. You talk to Rex Sinquefield, and he'll tell you that
in any normal distribution you're going to get those outlying
orangutans.
I put it carefully. I said if you identify them, and in the
future they continue to do well, then I'm starting to believe
it. This sounds like the frustrations of my college days when
I found that the system that worked on the old data didn't work
with the new data!
So, in fact, there may be a Lynch and a Buffett effect out
there somewhere?
There may be, but the non sequiturs that people jump to after
that is to say, Aha! Active management pays!
No, it means that Peter Lynch and Warren Buffett pay! And
what is it about them that we can clone? Where's the next one?
Yeah. I don't think that's something you can teach anybody
or anything like that. The Magellan Fund [once managed by Peter
Lynch] by any risk-adjusted model, is off the map. But there
are only one or two like that.
Isn't it interesting that the last three years' performance
at Magellan Fund isn't Peter Lynch's? Jeff Vinik's performance
was also good. I presume because he made a big bet on technology
stocks and won.
Another thing I found when I looked at Magellan was that it
had a greater small stock bias when it was a smaller fund.
Are you working on anything now that you could share with
us?
We're trying to extend the three-factor model internationally.
The scientific approach is always to say: does it work out of
sample? In other works, does it work on new data, in this case,
foreign stocks? So, what we are doing is trying to use international
data to see if we can come up with a global view of risk and
return.
How does it look so far?
The problem is that the international data stink. You can't
get the kind of data we can get here in the US going back to
1926. We also have good accounting data going back to the early
'60s. Internationally, you don't really have returns before
1988. And you only have a sub-sample of stocks.
How much data do you need to get a valid sample?
You never know until you do it, because it's a function of
how variable the returns are. The problem with stock returns
is the variability is so high. It takes long samples to really
document anything. But, so far, the new data turn up the same
kind of risk factors.
I guess we still haven't found a way to predict the future.
That kind of reasoning will get you closer to my way of thinking!
The trouble with you academic guys is that you all approach
this with such religious zeal that I feel like a heretic if
I disagree with any of you. Like I'm going to be excommunicated
any second.
No. We'll just throw you out of the scientific community. You
get to stay in the active management community.
Gene, you're very well known in our business for your work
on returns. Do you do much work in the private sector?
Not a lot. I'm a little lazy! Most of the outside work I do
is in a forum framework. I mean how am I going to manage to
do all that if I go windsurfing every afternoon?
How's your windsurfing coming along?
I'm probably the best in the world over age fifty!
Who knows, Gene, maybe you're the millionth orangutan on
the surfboard, the fifty-year-old outlier who wins the world
championship.
A couple of things struck me about this discussion. You might
or might not agree, but I thought I sensed a much more open attitude
from Gene about market efficiency, the concept he developed. I
felt that his was not the extreme version of the efficient market
theory that some others adopt, but rather an open-minded attitude
which says that, yes, market efficiency is there and chances are
you will never do better that the markets, and as a rule, active
management just doesn't pay.
On the other hand, the door seemed open a crack to the reality
that there are the occasional Peter Lynches and others who achieve
truly great performance records over extended periods of time.
The term the academics like to use for this is "persistence."
Yes, these guys exist, but there aren't that many. Still, the
sobering example Fama used that throws cold water on the performance
expectations is the study he did on mutual fund performance over
a ten year period since 1976. He then took the top performing
funds in the group and analyzed them for the following ten years.
The result: the top performing group only had a 50/50 chance of
staying in the top half in the second ten year period. What are
you going to do? I think it's time we talked to another active
manager.
February 1997
