My Life in Finance

My Life in Finance
By Eugene F. Fama
Foreword

I was invited by the editors to contribute a professional autobiography for
the Annual Review of Financial Economics.  I focus on what I think is my
best stuff.  Readers interested in the rest can download my vita from the
website of the University of Chicago, Booth School of Business.  I only
briefly discuss ideas and their origins, to give the flavor of context and
motivation.  I do not attempt to review the contributions of others, which
is likely to raise feathers.  Mea culpa in advance.

Finance is the most successful branch of economics in terms of theory and
empirical work, the interplay between the two, and the penetration of
financial research into other areas of economics and real-world applications
. I have been doing research in finance almost since its start, when
Markowitz (1952, 1959) and Modigliani and Miller (1958) set the field on the
path to become a serious scientific discipline. It has been fun to see it
all, to contribute, and to be a friend and colleague to the giants who
created the field.
Origins

My grandparents emigrated to the U.S. from Sicily in the early 1900s, so I
am a third generation Italian-American. I was the first in the lineage to go
to university.

My passion in high school was sports. I played basketball (poorly), ran
track (second in the state meet in the high jump — not bad for a 5'8" kid),
played football (class B state champions), and baseball (state semi-finals
two years). I claim to be the inventor of the split end position in football
, an innovation prompted by the beatings I took trying to block much bigger
defensive tackles. I am in my high school's (Malden Catholic) athletic hall
of fame.

I went on to Tufts University in 1956, intending to become a high school
teacher and sports coach. At the end of my second year, I married my high
school sweetheart, Sallyann Dimeco, now my wife of more than 50 years. We
have four adult children and ten delightful grandchildren. Sally's family
contributions dwarf mine.

At Tufts I started in romance languages but after two years became bored
with rehashing Voltaire and took an economics course. I was enthralled by
the subject matter and by the prospect of escaping lifetime starvation on
the wages of a high school teacher. In my last two years at Tufts, I went
heavy on economics. The professors, as teachers, were as inspiring as the
research stars I later profited from at the University of Chicago.

My professors at Tufts encouraged me to go to graduate school. I leaned
toward a business school Ph.D. My Tufts professors (mostly Harvard economics
Ph.D.s) pushed Chicago as the business school with a bent toward serious
economics. I was accepted at other schools, but April 1960 came along and I
didn't hear from Chicago. I called and the dean of students, Jeff Metcalf,
answered. (The school was much smaller then.) They had no record of my
application. But Jeff and I hit it off, and he asked about my grades. He
said Chicago had a scholarship reserved for a qualified Tufts graduate. He
asked if I wanted it. I accepted and, except for two great years teaching in
Belgium, I have been at the University of Chicago since 1960. I wonder what
path my professional life would have taken if Jeff didn't answer the phone
that day. Serendipity!

During my last year at Tufts, I worked for Harry Ernst, an economics
professor who also ran a stock market forecasting service. Part of my job
was to invent schemes to forecast the market. The schemes always worked on
the data used to design them. But Harry was a good statistician, and he
insisted on out-of-sample tests. My schemes invariably failed those tests. I
didn't fully appreciate the lesson in this at the time, but it came to me
later.

During my second year at Chicago, with an end to course work and prelims in
sight, I started to attend the Econometrics Workshop, at that time the
hotbed for research in finance. Merton Miller had recently joined the
Chicago faculty and was a regular participant, along with Harry Roberts and
Lester Telser. Benoit Mandelbrot was an occasional visitor. Benoit presented
in the workshop several times, and in leisurely strolls around campus, I
learned lots from him about fat-tailed stable distributions and their
apparent relevance in a wide range of economic and physical phenomena.
Merton Miller became my mentor in finance and economics (and remained so
throughout his lifetime). Harry Roberts, a statistician, instilled a
philosophy for empirical work that has been my north star throughout my
career.
Efficient Markets

Miller, Roberts, Telser, and Mandelbrot were intensely involved in the
burgeoning work on the behavior of stock prices (facilitated by the arrival
of the first reasonably powerful computers). The other focal point was MIT,
with Sydney Alexander, Paul Cootner, Franco Modigliani, and Paul Samuelson.
Because his co-author, Merton Miller, was now at Chicago, Franco was a
frequent visitor. Like Merton, Franco was unselfish and tireless in helping
people think through research ideas. Franco and Mert provided an open
conduit for cross-fertilization of market research at the two universities.

At the end of my second year at Chicago, it came time to write a thesis, and
I went to Miller with five topics. Mert always had uncanny insight about
research ideas likely to succeed. He gently stomped on four of my topics,
but was excited by the fifth. From my work for Harry Ernst at Tufts, I had
daily data on the 30 Dow-Jones Industrial Stocks. I proposed to produce
detailed evidence on (1) Mandelbrot's hypothesis that stock returns conform
to non-normal (fat-tailed) stable distributions and (2) the time-series
properties of returns. There was existing work on both topics, but I
promised a unifying perspective and a leap in the range of data brought to
bear.

Vindicating Mandelbrot, my thesis (Fama 1965a) shows (in nauseating detail)
that distributions of stock returns are fat-tailed: there are far more
outliers than would be expected from normal distributions - a fact
reconfirmed in subsequent market episodes, including the most recent. Given
the accusations of ignorance on this score recently thrown our way in the
popular media, it is worth emphasizing that academics in finance have been
aware of the fat tails phenomenon in asset returns for about 50 years.

My thesis and the earlier work of others on the time-series properties of
returns falls under what came to be called tests of market efficiency. I
coined the terms "market efficiency" and "efficient markets," but they do
not appear in my thesis. They first appear in "Random Walks in Stock Market
Prices," paper number 16 in the series of Selected Papers of the Graduate
School of Business, University of Chicago, reprinted in the Financial
Analysts Journal (Fama 1965b).

From the inception of research on the time-series properties of stock
returns, economists speculated about how prices and returns behave if
markets work, that is, if prices fully reflect all available information.
The initial theory was the random walk model. In two important papers,
Samuelson (1965) and Mandelbrot (1966) show that the random walk prediction
(price changes are iid) is too strong. The proposition that prices fully
reflect available information implies only that prices are sub-martingales.
Formally, the deviations of price changes or returns from the values
required to compensate investors for time and risk-bearing have expected
value equal to zero conditional on past information.

During the early years, in addition to my thesis, I wrote several papers on
market efficiency (Fama 1963, 1965c, Fama and Blume 1966), now mostly
forgotten. My main contribution to the theory of efficient markets is the
1970 review (Fama 1970). The paper emphasizes the joint hypothesis problem
hidden in the sub-martingales of Mandelbrot (1966) and Samuelson (1965).
Specifically, market efficiency can only be tested in the context of an
asset pricing model that specifies equilibrium expected returns. In other
words, to test whether prices fully reflect available information, we must
specify how the market is trying to compensate investors when it sets prices
. My cleanest statement of the theory of efficient markets is in chapter 5
of Fama (1976b), reiterated in my second review "Efficient Markets II" (Fama
1991a).

The joint hypothesis problem is obvious, but only on hindsight. For example,
much of the early work on market efficiency focuses on the autocorrelations
of stock returns. It was not recognized that market efficiency implies zero
autocorrelation only if the expected returns that investors require to hold
stocks are constant through time or at least serially uncorrelated, and
both conditions are unlikely.

The joint hypothesis problem is generally acknowledged in work on market
efficiency after Fama (1970), and it is understood that, as a result, market
efficiency per se is not testable. The flip side of the joint hypothesis
problem is less often acknowledged. Specifically, almost all asset pricing
models assume asset markets are efficient, so tests of these models are
joint tests of the models and market efficiency. Asset pricing and market
efficiency are forever joined at the hip.
Event Studies

My Ph.D. thesis and other early work on market efficiency do not use the
CRSP files, which were not yet available. When the files became available (
thanks to years of painstaking work by Larry Fisher), Jim Lorie, the founder
of CRSP, came to me worried that no one would use the data and CRSP would
lose its funding. He suggested a paper on stock splits, to advertise the
data. The result is Fama, Fisher, Jensen, and Roll (1969). This is the first
study of the adjustment of stock prices to a specific kind of information
event. Such "event studies" quickly became a research industry, vibrant to
this day, and the main form of tests of market efficiency. Event studies
have also found a practical application — calculating damages in legal
cases.

The refereeing process for the split study was a unique experience. When
more than a year passed without word from the journal, we assumed the paper
would be rejected. Then a short letter arrived. The referee (Franco
Modigliani) basically said: it's great, publish it. Never again would this
happen!

There is a little appreciated fact about the split paper. It contains no
formal tests (standard errors, t-statistics, etc.) The results were
apparently so convincing as confirmation of market efficiency that formal
tests seemed irrelevant. But this was before the joint hypothesis problem
was recognized, and only much later did we come to appreciate that results
in event studies can be sensitive to methodology, in particular, what is
assumed about equilibrium expected returns — a point emphasized in Fama (
1998).

Michael Jensen and Richard Roll are members of a once-in-a-lifetime cohort
of Ph.D. students that came to Chicago soon after I joined the faculty in
1963. Also in this rough cohort are (among others) Ray Ball, Marshall Blume,
James MacBeth, Myron Scholes, and Ross Watts. I think I was chairman of all
their thesis committees, but Merton Miller and Harry Roberts were deeply
involved. Any investment in these and about 100 other Ph.D. students I have
supervised has been repaid many times by what I learn from them during their
careers.
Forecasting Regressions

In 1975 I published a little empirical paper, "Short-Term Interest Rates as
Predictors of Inflation" (Fama 1975). The topic wasn't new, but my approach
was novel. Earlier work uses regressions of the interest rate on the
inflation rate for the period covered by the interest rate. The idea is that
the expected inflation rate (along with the expected real return)
determines the interest rate, so the interest rate should be the dependent
variable and the expected inflation rate should be the independent variable.
The observed inflation rate is, of course, a noisy proxy for its expected
value, so there is a measurement error problem in the regression of the ex
ante interest rate on the ex post inflation rate.

My simple insight is that a regression estimates the conditional expected
value of the left-hand-side variable as a function of the right-hand-side
variables. Thus, to extract the forecast of inflation in the interest rate (
the expected value of inflation priced into the interest rate) one regresses
the ex post inflation rate on the ex ante interest rate. On hindsight, this
is the obvious way to run the forecasting regression, but again it wasn't
obvious at the time.

There is a potential measurement error problem in the regression of the ex
post inflation rate on the ex ante (T-bill) interest rate, caused by
variation through time in the expected real return on the bill. The model of
market equilibrium in "Short-Term Interest Rates as Predictors of Inflation
" assumes that the expected real return is constant, and this seems to be a
reasonable approximation for the 1953-1971 period of the tests. (It doesn't
work for any later period.) This result raised a furor among Keynesian
macroeconomists who postulated that the expected real return was a policy
variable that played a central role in controlling investment and business
cycles. There was a full day seminar on my paper at MIT, where my simple
result was heatedly attacked. I argued that I didn't know what the fuss was
about, since the risk premium component of the cost of capital is surely
more important than the riskfree real rate, and it seems unlikely that
monetary and fiscal actions can fine tune the risk premium. I don't know if
I won the debate, but it was followed by a tennis tournament, and I think I
did win that.

The simple idea about forecasting regressions in Fama (1975) has served me
well, many times. (When I have an idea, I beat it to death.) I have many
papers that use the technique to extract the forecasts of future spot rates,
returns, default premiums, etc., in the term structure of interests rates,
for example Fama (1976a,c, 1984b, 1986, 1990b, 2005), Fama and Schwert (1979
), Fama and Bliss (1987). In a blatant example of intellectual arbitrage, I
apply the technique to study forward foreign exchange rates as predictors of
future spot rates, in a paper (Fama 1984a) highly cited in that literature.
The same technique is used in my work with Kenneth R. French and G. William
Schwert on the predictions of stock returns in dividend yields and other
variables (Fama and Schwert 1977, Fama and French 1988, 1989). And
regressions of ex post variables on ex ante variables are now standard in
forecasting studies, academic and applied.
Agency Problems and the Theory of Organizations

In 1976 Michael Jensen and William Meckling published their groundbreaking
paper on agency problems in investment and financing decisions (Jensen and
Meckling 1976). According to Kim, Morse, and Zingales (2006), this is the
second most highly cited theory paper in economics published in the 1970-
2005 period. It fathered an enormous literature.

When Mike came to present the paper at Chicago, he began by claiming it
would destroy the corporate finance material in what he called the "white
bible" (Fama and Miller, The Theory of Finance 1972). Mert and I replied
that his analysis is deeper and more insightful, but in fact there is a
discussion of stockholder-bondholder agency problems in chapter 4 of our
book. Another example that new ideas are almost never completely new!

Spurred by Jensen and Meckling (1976), my research took a turn into agency
theory. The early papers on agency theory emphasized agency problems. I was
interested in studying how competitive forces lead to the evolution of
mechanisms to mitigate agency problems. The first paper, "Agency Problems
and the Theory of the Firm" (Fama 1980a) argues that managerial labor
markets, inside and outside of firms, act to control managers faced with the
temptations created by diffuse residual claims that reduce the incentives
of individual residual claimants to monitor managers.

I then collaborated with Mike on three papers (Fama and Jensen (1983a,b,
1985)) that study more generally how different mechanisms arise to mitigate
the agency problems associated with "separation of ownership and control"
and how an organization's activities and the special agency problems they
pose, affect the nature of its residual claims and control mechanisms. For
example, we argue that the redeemable residual claims of a financial mutual
(for example, an open end mutual fund) provide strong discipline for its
managers, but redeemability is cost effective only when the assets of the
organization can be sold quickly with low transactions costs. We also argue
that the nonprofit format, in which no agents have explicit residual claims
to net cashflows, is a response to the agency problem associated with
activities in which there is a potential supply of donations that might be
expropriated by residual claimants. Two additional papers (Fama 1990a, 1991b
) spell out some of the implications of Fama (1980a) and Fama and Jensen (
1983a,b) for financing decisions and the nature of labor contracts.

Kim, Morse, and Zingales (2006) list the 146 papers published during 1970-
2005 that have more than 500 cites in the major journals of economics. I'm
blatantly bragging, but Fama (1980a) and Fama and Jensen (1983a) are among
my six papers on the list. (The others are Fama 1970, Fama and MacBeth 1973,
Fama and French 1992, 1993. If the list extended back to ancient times,
Fama 1965a and Fama, Fisher, Jensen, and Roll 1969 would also make it.) I
think of myself as an empiricist (and a simple-minded one at that), so I
like my work in agency theory since it suggests that occasionally
theoretical ideas get sprinkled into the mix.
Macroeconomics

Toward the end of the 1970s, around the time of the agency theory research,
my work took a second turn into macroeconomics and international finance.
Fischer Black had similar interests, and I profited from many long
discussions with him on this and other issues during the years he spent at
Chicago in the office next to mine.

Since they typically assume away transactions costs, asset pricing models in
finance do not have a natural role for money. Fama and Farber (1979) model
a world in which financial markets are indeed frictionless, but there are
transactions costs in consumption that are reduced by holding money. Money
then becomes a portfolio asset, and we investigate how nominal bonds (
borrowing and lending) allow consumer-investors to split decisions about how
much money to hold for transactions purposes from decisions about how much
of the purchasing power risk of their money holdings they will bear. We also
investigate the pricing of the purchasing power risk of the money supply in
the context of the CAPM.

Extending the analysis to an international setting, Fama and Farber (1979)
show that exchange rate uncertainty is not an additional risk in
international investing when purchasing power parity (PPP) holds, because
PPP implies that the real return on any asset is the same to the residents
of all countries. The point is obvious, on hindsight, but previous papers in
the international asset pricing literature assume that exchange rate
uncertainty is an additional risk, without saying anything about PPP, or
saying something incorrect.

Three subsequent papers (Fama 1980b, 1983, 1985) examine what the theory of
finance says about the role of banks. The first two (Fama 1980b, 1983) argue
that in the absence of reserve requirements, banks are just financial
intermediaries, much like mutual funds, that manage asset portfolios on
behalf of depositors. And like mutual fund holdings, the quantity of
deposits has no role in price level determination (inflation). Bank deposits
also provide access to an accounting system of exchange (via checks and
electronic transfers) that is just an efficient mechanism for moving claims
on assets from some consumer-investors to others, without the intervention
of a hand-to-hand medium of exchange like currency. Because it pays less
than full interest, currency has an important role in price level
determination. The role of deposits in price level determination is, however
, artificial, induced by the requirement to hold "reserves" with the central
bank that pay less than full interest and are exchangeable for currency on
demand.
Corporate Finance

As finance matured, it became more specialized. The teaching and research of
new people tends to focus entirely on asset pricing or corporate finance.
It wasn't always so. Until several years ago, I taught both. More of my
research is in asset-pricing-market-efficiency (66 papers and 1.5 books),
but as a result of longtime exposure to Merton Miller, I have always been
into corporate finance (15 papers and half a book).

The burning issue in corporate finance in the early 1960s was whether the
propositions of Modigliani and Miller (MM 1958) and Miller and Modigliani (
MM 1961) about the value irrelevance of financing decisions hold outside the
confines of their highly restrictive risk classes (where a risk class
includes firms with perfectly correlated net cashflows). With the
perspective provided by asset pricing models, which were unavailable to MM,
it became clear that their propositions do not require their risk classes.
Fama (1978) tries to provide a capstone. The paper argues that the MM
propositions hold in any asset pricing model that shares the basic MM
assumptions (perfect capital market, including no taxes, no transactions
costs, and no information asymmetries or agency problems), as long as either
(i) investors and firms have equal access to the capital market (so
investors can undo the financing decisions of firms), or (ii) there are
perfect substitutes for the securities issued by any firm (with perfect
substitute defined by whatever happens to be the right asset pricing model).

The CRSP files opened the gates for empirical asset pricing research (
including work on efficient markets). Compustat similarly provides the raw
material for empirical work in corporate finance. Fama and Babiak (1968)
leap on the new Compustat files to test Lintner's (1956) hypothesis that
firms have target dividend payouts but annual dividends only partially
adjust to their targets. Lintner estimates his model on aggregate data. We
examine how the model works for the individual firms whose dividend
decisions it is meant to explain. It works well in our tests, and it
continues to work in subsequent trials (e.g., Fama 1974). But the speed-of-
adjustment of dividends to their targets has slowed considerably, that is,
dividends have become more "sticky" (Fama and French 2002). The more
interesting fact, however, is the gradual disappearance of dividends. In
1978 almost 80% of NYSE-Amex-Nasdaq listed firms paid dividends, falling to
about 20% in 1999 (Fama and French 2001).

Post-MM corporate finance has two main theories, the pecking order model of
Myers (1984) and Myers and Majluf (1984) and the tradeoff model (which has
many authors). These theories make predictions about financing decisions
when different pieces of the perfect capital markets assumption of MM do not
hold. The pecking order model does reasonably well, until the early 1980s
when new issues of common stock (which the model predicts are rare) become
commonplace (Fama and French 2005). There is some empirical support for the
leverage targets that are the centerpiece of the tradeoff model, but the
speed-of-adjustment of leverage to its targets is so slow that the existence
of targets becomes questionable. (This is the conclusion of Fama and French
2002 and other recent work.) In the end, it's not clear that the capital
structure irrelevance propositions of Modigliani and Miller are less
realistic as rough approximations than the popular alternatives. (This is
the conclusion of Fama and French 2002.)

In my view, the big open challenge in corporate finance is to produce
evidence on how taxes affect market values and thus optimal financing
decisions. Modigliani and Miller (1963) suggest that debt has large tax
benefits, and taxation disadvantages dividends. To this day, this is the
position commonly advanced in corporate finance courses. Miller (1977),
however, presents a scenario in which the tax benefits of debt due to the
tax deductibility of interest payments at the corporate level are offset by
taxation of interest receipts at the personal level, and leverage has no
effect on a firm's market value. Miller and Scholes (1978) present a
scenario in which dividend and debt choices have no effect on the market
values of firms. Miller (1977) and Miller and Scholes (1978) recognize that
that there are scenarios in which taxes do affect optimal dividend and debt
decisions. In the end, the challenge is empirical measurement of tax effects
(the marginal tax rates implicit) in the pricing of dividends and interest.
So far the challenge goes unmet.

Fama and French (1998) take a crack at this first order issue, without
success. The problem is that dividend and debt decisions are related to
expected net cashflows — the main determinant of the market value of a firm
's securities. Because proxies for expected net cashflows are far from
perfect, the cross-section regressions of Fama and French (1998) do not
produce clean estimates of how the taxation of dividends and interest
affects the market values of a firm's stocks and bonds. There are also
papers that just assume debt has tax benefits that can be measured from tax
rate schedules. Without evidence on the tax effects in the pricing of
interest, such exercises are empty.
The CAPM

Without being there one can't imagine what finance was like before formal
asset pricing models. For example, at Chicago and elsewhere, investments
courses were about security analysis: how to pick undervalued stocks. In
1963 I taught the first course at Chicago devoted to Markowitz' (1959)
portfolio model and its famous offspring, the asset pricing model (CAPM) of
Sharpe (1964) and Lintner (1965).

The CAPM provides the first precise definition of risk and how it drives
expected return, until then vague and sloppy concepts. The absence of formal
models of risk and expected return placed serious limitations on research
that even grazed the topic. For example, the path breaking paper of
Modigliani and Miller (1958) uses arbitrage within risk classes to show that
(given their assumptions) financing decisions do not affect a firm's market
value. They define a risk class as firms with perfectly correlated net cash
flows. This is restrictive and it led to years of bickering about the
applicability of their analysis and conclusions. The problem was due to the
absence of formal asset pricing models that define risk and how it relates
to expected return.

The arrival of the CAPM was like the time after a thunderstorm, when the air
suddenly clears. Extensions soon appeared, but the quantum leaps are the
intertemporal model (ICAPM) of Merton (1973a), which generalizes the CAPM to
a multiperiod world with possibly multiple dimensions of risk, and the
consumption CAPM of Lucas (1978), Breeden (1979), and others.

Though not about risk and expected return, any history of the excitement in
finance in the 1960s and 1970s must mention the options pricing work of
Black and Scholes (1973) and Merton (1973b). These are the most successful
papers in economics - ever - in terms of academic and applied impact. Every
Ph.D. student in economics is exposed to this work, and the papers are the
foundation of a massive industry in financial derivatives.

There are many early tests of the CAPM, but the main survivors are Black,
Jensen, and Scholes (BJS 1972) and Fama and MacBeth (1973). Prior to these
papers, the typical test of the CAPM was a cross-section regression of the
average returns on a set of assets on estimates of their market βs and
other variables. (The CAPM predicts, of course, that the β premium is
positive, and β suffices to describe the cross-section of expected asset
returns.) BJS were suspicious that the slopes in these cross-section
regressions seemed too precise (the reported standard errors seemed too
small). They guessed rightly that the problem was the OLS assumption that
there is no cross-correlation in the regression residuals.

Fama and MacBeth (1973) provide a simple solution to the cross-correlation
problem. Instead of a regression of average asset returns on their βs and
other variables, one does the regression month-by-month. The slopes are then
monthly portfolio returns whose average values can be used to test the CAPM
predictions that the β premium is positive and other variables add nothing
to the explanation of the cross-section of expected returns. (The point is
explained best in chapter 8 of Fama (1976b).) The month-by-month variation
in the regression slopes captures all effects of the cross-correlation of
the regression residuals, and these effects are automatically embedded in
the time-series standard errors of the average slopes. The approach thus
captures residual covariances without requiring an estimate of the residual
covariance matrix.

The Fama-MacBeth approach is standard in tests of asset pricing models that
use cross-section regressions, but the benefits of the approach carry over
to panels (time series of cross-sections) of all sorts. Kenneth French and I
emphasize this point (advertise is more accurate) in our corporate finance
empirical work (e.g., Fama and French 1998, 2002). Outside of finance,
research in economics that uses panel regressions has only recently begun to
acknowledge that residual covariance is a pervasive problem. Various new
robust regression techniques are available, but the Fama-MacBeth approach
remains a simple option.

Given the way my recent empirical work with Kenneth French dumps on the CAPM
, it is only fair to acknowledge that the CAPM gets lots of credit for
forcing money managers to take more seriously the challenges posed by the
work on efficient markets. Before the CAPM, money management was entirely
active, and performance reporting was shoddy. The CAPM gave us a clean story
about risk and expected return (i.e., a model of market equilibrium) that
allowed us to judge the performance of active managers. Using the CAPM,
Jensen (1968) rang the bell on the mutual fund industry. Performance
evaluation via the CAPM quickly became standard both among academics and
practitioners, passive management got a foothold, and active managers became
aware that their feet would forever be put to the fire.
The Three-Factor Model

The evidence in Black, Jensen, and Scholes (1972) and Fama and MacBeth (1973
) is generally favorable to the CAPM, or at least to Black's (1972) version
of the CAPM. Subsequently, violations of the model, labeled anomalies, begin
to surface. Banz (1981) finds that β does not fully explain the higher
average returns of small (low market capitalization) stocks. Basu (1983)
finds that the positive relation between the earning-price ratio (E/P) and
average return is left unexplained by market β. Rosenberg, Reid, and
Lanstein (1985) find a positive relation between average stock return and
the book-to-market ratio (B/M) that is missed by the CAPM. Bhandari (1988)
documents a similar result for market leverage (the ratio of debt to the
market value of equity, D/M). Ball (1978) and Keim (1988) argue that
variables like size, E/P, B/M, and D/M are natural candidates to expose the
failures of asset pricing models as explanations of expected returns since
all these variables use the stock price, which, given expected dividends, is
inversely related to the expected stock return.

The individual papers on CAPM anomalies did not seem to threaten the
dominance of the model. My guess is that viewed one at a time, the anomalies
seemed like curiosity items that show that the CAPM is just a model, an
approximation that can't be expected to explain the entire cross-section of
expected stock returns. I see no other way to explain the impact of Fama and
French (1992), "The Cross-Section of Expected Stock Returns," which
contains nothing new. The CAPM anomalies in the paper are those listed above
, and the evidence that there is no reliable relation between average return
and market β was available in Reinganum (1981) and Lakonishok and Shapiro
(1986). Apparently, seeing all the negative evidence in one place led
readers to accept our conclusion that the CAPM just doesn't work. The model
is an elegantly simple and intuitively appealing tour de force that laid the
foundations of asset pricing theory, but its major predictions seem to be
violated systematically in the data.

An asset pricing model can only be dethroned by a model that provides a
better description of average returns. The three-factor model (Fama and
French 1993) is our shot. The model proposes that along with market β,
sensitivities to returns on two additional portfolios, SMB and HML, explain
the cross-section of expected stock returns. The size factor, SMB, is the
difference between the returns on diversified portfolios of small and big
stocks, and the value/growth factor, HML, is the difference between the
returns on diversified portfolios of high and low B/M (i.e., value and
growth) stocks. The SMB and HML returns are, of course, brute force
constructs designed to capture the patterns in average returns related to
size and value versus growth stocks that are left unexplained by the CAPM.

Ken French and I have many papers that address questions about the three-
factor model and the size and value/growth patterns in average returns the
model is meant to explain. For example, to examine whether the size and
value/growth patterns in average returns observed by Fama and French (1992)
for the post 1962 period are the chance result of data dredging, Davis, Fama
, and French (2000) extend the tests back to 1927, and Fama and French (1998
) examine international data. The results are similar to those in Fama and
French (1992). Fama and French (1996, 2008) examine whether the three-factor
model can explain the anomalies that cause problems for the CAPM. The three
-factor model does well on the anomalies associated with variants of price
ratios, but it is just a model and it fails to absorb some other anomalies.
The most prominent is the momentum in short-term returns documented by
Jegadeesh and Titman (1993), which is a problem for all asset pricing models
that do not add exposure to momentum as an explanatory factor. After 1993,
work, both academic and applied, directed at measuring the performance of
managed portfolios routinely use the benchmarks provided by the three-factor
model, often augmented with a momentum factor (for example, Carhart 1997,
and more recently Kosowski et al. 2006 or Fama and French 2009).

From its beginnings there has been controversy about how to interpret the
size and especially the value/growth premiums in average returns captured by
the three-factor model. Fama and French (1993, 1996) propose a multifactor
version of Merton's (1973a) ICAPM. The weakness of this position is the
question it leaves open. What are the state variables that drive the size
and value premiums, and why do they lead to variation in expected returns
missed by market β? There is a literature that proposes answers to this
question, but in my view the evidence so far is unconvincing.

The chief competitor to our ICAPM risk story for the value premium is the
overreaction hypothesis of DeBondt and Thaler (1987) and Lakonishok,
Shleifer, and Vishny (1994). They postulate that market prices overreact to
the recent good times of growth stocks and the bad times of value stocks.
Subsequent price corrections then produce the value premium (high average
returns of value stocks relative to growth stocks). The weakness of this
position is the presumption that investors never learn about their
behavioral biases, which is necessary to explain the persistence of the
value premium.

Asset pricing theory typically assumes that portfolio decisions depend only
on the properties of the return distributions of assets and portfolios.
Another possibility, suggested by Fama and French (2007) and related to the
stories in Daniel and Titman (1997) and Barberis and Shleifer (2003), is
that tastes for other characteristics of assets, unrelated to properties of
returns, also play a role. ("Socially responsible investing" is an example.)
Perhaps many investors simply get utility from holding growth stocks, which
tend to be profitable fast-growing firms, and they are averse to value
stocks, which tend to be relatively unprofitable with few growth
opportunities. If such tastes persist, they can have persistent effects on
asset prices and expected returns, as long as they don't lead to arbitrage
opportunities.

To what extent is the value premium in expected stock returns due to ICAPM
state variable risks, investor overreaction, or tastes for assets as
consumption goods? We may never know. Moreover, given the blatant empirical
motivation of the three-factor model (and the four-factor offspring of
Carhart 1997), perhaps we should just view the model as an attempt to find a
set of portfolios that span the mean-variance-efficient set and so can be
used to describe expected returns on all assets and portfolios (Huberman and
Kandel 1987).

The academic research on the size and value premiums in average stock
returns has transformed the investment management industry, both on the
supply side and on the demand side. Whatever their views about the origins
of the premiums, institutional investors commonly frame their asset
allocation decisions in two dimensions, size and value versus growth, and
the portfolio menus offered by money managers are typically framed in the
same way. And it is testimony to the credibility of research in finance that
all this happened in a very short period of time.
Conclusions

The first 50 years of research in finance has been a great ride. I'm
confident finance will continue to be a great ride into the indefinite
future.
Addendum — Provided by the tenured finance faculty of Chicago Booth

When my paper was posted on the Forum of the website of the Chicago Booth
Initiative on Global Markets, the tenured finance faculty introduced it with
the following comments. EFF

This post makes available an autobiographical note by Gene Fama that was
commissioned by the Annual Review of Financial Economics. Gene's remarkable
career and vision, to say nothing of his engaging writing style, make this
short piece a must read for anyone interested in finance. However, as his
colleagues, we believe his modesty led him to omit three crucial aspects of
his contributions.

First, Gene was (and still is) essential to shaping the nature of the
finance group at Chicago. As he explains in a somewhat understated fashion,
he and Merton Miller transformed the finance group turning it into a
research oriented unit. For the last 47 years he has held court on Tuesday
afternoons in the finance workshop, in a room that now bears his name.
Through the workshop, generations of students, colleagues, and visitors have
been and continue to be exposed to his research style of developing and
rigorously testing theories with real world data that has become the
hallmark of Chicago finance.

Second, and equally important, is his leadership. Rather than rest on his
laurels or impose his own views on the group, Gene has always sought the
truth, even when it appeared at odds with his own views. He has promoted a
contest of ideas and outlooks, all subject to his exceptional standards of
quality. The makeup of the group has shifted as the world and what we know
about it has changed. The current finance group at Chicago includes a
diverse set of people who specialize in all areas of modern finance
including, behavioral economics, pure theory, and emerging, non-traditional
areas such as entrepreneurship and development that were unheard of when
Gene arrived at Chicago. Contrary to the caricatured descriptions, there is
no single Chicago view of finance, except that the path to truth comes from
the rigorous development and confrontation of theories with data.

Finally, each of us has our own personal examples of Gene's generosity,
kindness and mentorship. He is an impeccable role model. He is in his office
every day, and his door is always open. By personal example, he sets the
standards for the values and ethics by which we do research and run our
school. All of us have learned enormously from Gene's generous willingness
to discuss his and our work, and gently and patiently to explain and debate
that work with generations of faculty. Gene likely enjoys as high a ranking
in the "thanks for comments" footnotes of published papers as he does in
citations. He has made the finance group an exciting, collegial, and
welcoming place to work. He has greatly enhanced all of our research careers
and accomplishments. He is a great friend, and we can only begin to express
our gratitude.

We hope you enjoy reading Gene's description of his career that might just
as well be described as the story of how modern finance evolved at Chicago.

Gene's Tenured Finance Faculty Colleagues at Chicago Booth

John H. Cochrane, George M. Constantinides, Douglas W. Diamond, Milton
Harris, John C. Heaton, Steven Neil Kaplan, Anil K Kashyap, Richard Leftwich
, Tobias J. Moskowitz, Lubos Pastor, Raghuram G. Rajan, Richard Thaler,
Pietro Veronesi, Robert W. Vishny, and Luigi Zingales



The comments of Andy Lo and George Constantinides are gratefully
acknowledged. Special thanks to John Cochrane, Kenneth French, and Tobias
Moskowitz.
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