The Capital Asset Pricing Model - Theory and Evidence

Journal of Economic Perspectives—Volume 18, Number 3—Summer 2004—Pages 25–46

The Capital Asset Pricing Model:

Theory and Evidence

Eugene F. Fama and Kenneth R. French

T he capital asset pricing model (CAPM) of William Sharpe (1964) and John

Lintner (1965) marks the birth of asset pricing theory (resulting in a

Nobel Prize for Sharpe in 1990). Four decades later, the CAPM is still

widely used in applications, such as estimating the cost of capital for firms and

evaluating the performance of managed portfolios. It is the centerpiece of MBA

investment courses. Indeed, it is often the only asset pricing model taught in these

courses.1

The attraction of the CAPM is that it offers powerful and intuitively pleasing

predictions about how to measure risk and the relation between expected return

and risk. Unfortunately, the empirical record of the model is poor—poor enough

to invalidate the way it is used in applications. The CAPM’s empirical problems may

reflect theoretical failings, the result of many simplifying assumptions. But they may

also be caused by difficulties in implementing valid tests of the model. For example,

the CAPM says that the risk of a stock should be measured relative to a compre-

hensive “market portfolio” that in principle can include not just traded financial

assets, but also consumer durables, real estate and human capital. Even if we take

a narrow view of the model and limit its purview to traded financial assets, is it

1 Although every asset pricing model is a capital asset pricing model, the finance profession reserves the

acronym CAPM for the specific model of Sharpe (1964), Lintner (1965) and Black (1972) discussed

here. Thus, throughout the paper we refer to the Sharpe-Lintner-Black model as the CAPM.

y Eugene F. Fama is Robert R. McCormick Distinguished Service Professor of Finance,

Graduate School of Business, University of Chicago, Chicago, Illinois. Kenneth R. French is

Carl E. and Catherine M. Heidt Professor of Finance, Tuck School of Business, Dartmouth

College, Hanover, New Hampshire. Their e-mail addresses are �eugene.fama@gsb.uchicago.

edu� and �kfrench@dartmouth.edu�, respectively.

26 Journal of Economic Perspectives

legitimate to limit further the market portfolio to U.S. common stocks (a typical

choice), or should the market be expanded to include bonds, and other financial

assets, perhaps around the world? In the end, we argue that whether the model’s

problems reflect weaknesses in the theory or in its empirical implementation, the

failure of the CAPM in empirical tests implies that most applications of the model

are invalid.

We begin by outlining the logic of the CAPM, focusing on its predictions about

risk and expected return. We then review the history of empirical work and what it

says about shortcomings of the CAPM that pose challenges to be explained by

alternative models.

The Logic of the CAPM

The CAPM builds on the model of portfolio choice developed by Harry

Markowitz (1959). In Markowitz’s model, an investor selects a portfolio at time

t � 1 that produces a stochastic return at t. The model assumes investors are risk

averse and, when choosing among portfolios, they care only about the mean and

variance of their one-period investment return. As a result, investors choose “mean-

variance-efficient” portfolios, in the sense that the portfolios 1) minimize the

variance of portfolio return, given expected return, and 2) maximize expected

return, given variance. Thus, the Markowitz approach is often called a “mean-

variance model.”

The portfolio model provides an algebraic condition on asset weights in mean-

variance-efficient portfolios. The CAPM turns this algebraic statement into a testable

prediction about the relation between risk and expected return by identifying a

portfolio that must be efficient if asset prices are to clear the market of all assets.

Sharpe (1964) and Lintner (1965) add two key assumptions to the Markowitz

model to identify a portfolio that must be mean-variance-efficient. The first assump-

tion is complete agreement: given market clearing asset prices at t � 1, investors agree

on the joint distribution of asset returns from t � 1 to t. And this distribution is the

true one—that is, it is the distribution from which the returns we use to test the

model are drawn. The second assumption is that there is borrowing and lending at a

risk-free rate, which is the same for all investors and does not depend on the amount

borrowed or lent.

Figure 1 describes portfolio opportunities and tells the CAPM story. The

horizontal axis shows portfolio risk, measured by the standard deviation of portfolio

return; the vertical axis shows expected return. The curve abc, which is called the

minimum variance frontier, traces combinations of expected return and risk for

portfolios of risky assets that minimize return variance at different levels of ex-

pected return. (These portfolios do not include risk-free borrowing and lending.)

The tradeoff between risk and expected return for minimum variance portfolios is

apparent. For example, an investor who wants a high expected return, perhaps at

point a, must accept high volatility. At point T, the investor can have an interme-

Eugene F. Fama and Kenneth R. French 27

diate expected return with lower volatility. If there is no risk-free borrowing or

lending, only portfolios above b along abc are mean-variance-efficient, since these

portfolios also maximize expected return, given their return variances.

Adding risk-free borrowing and lending turns the efficient set into a straight

line. Consider a portfolio that invests the proportion x of portfolio funds in a

risk-free security and 1 � x in some portfolio g. If all funds are invested in the

risk-free security—that is, they are loaned at the risk-free rate of interest—the result

is the point Rf in Figure 1, a portfolio with zero variance and a risk-free rate of

return. Combinations of risk-free lending and positive investment in g plot on the

straight line between Rf and g. Points to the right of g on the line represent

borrowing at the risk-free rate, with the proceeds from the borrowing used to

increase investment in portfolio g. In short, portfolios that combine risk-free

lending or borrowing with some risky portfolio g plot along a straight line from Rf

through g in Figure 1.2

2 Formally, the return, expected return and standard deviation of return on portfolios of the risk-free

asset f and a risky portfolio g vary with x, the proportion of portfolio funds invested in f, as

Rp � xRf � �1 � x�Rg ,

E�Rp� � xRf � �1 � x�E�Rg�,

� �Rp� � �1 � x�� Rg�, x � 1.0,

which together imply that the portfolios plot along the line from Rf through g in Figure 1.

Figure 1

Investment Opportunities

Minimum variance

frontier for risky assets

s(R )

E(R )

Mean-variance-

efficient frontier

with a riskless asset

28 Journal of Economic Perspectives

To obtain the mean-variance-efficient portfolios available with risk-free bor-

rowing and lending, one swings a line from Rf in Figure 1 up and to the left as far

as possible, to the tangency portfolio T. We can then see that all efficient portfolios

are combinations of the risk-free asset (either risk-free borrowing or lending) and

a single risky tangency portfolio, T. This key result is Tobin’s (1958) “separation

theorem.”

The punch line of the CAPM is now straightforward. With complete agreement

about distributions of returns, all investors see the same opportunity set (Figure 1),

and they combine the same risky tangency portfolio T with risk-free lending or

borrowing. Since all investors hold the same portfolio T of risky assets, it must be

the value-weight market portfolio of risky assets. Specifically, each risky asset’s

weight in the tangency portfolio, which we now call M (for the “market”), must be

the total market value of all outstanding units of the asset divided by the total

market value of all risky assets. In addition, the risk-free rate must be set (along with

the prices

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of risky assets) to clear the market for risk-free borrowing and lending.

In short, the CAPM assumptions imply that the market portfolio M must be on

the minimum variance frontier if the asset market is to clear. This means that the

algebraic relation that holds for any minimum variance portfolio must hold for the

market portfolio. Specifically, if there are N risky assets,

�Minimum Variance Condition for M� E�Ri � � E�RZM �

� �E�RM� � E�RZM��iM , i � 1, . . . , N.

In this equation, E(Ri) is the expected return on asset i, and �iM, the market beta

of asset i, is the covariance of its return with the market return divided by the

variance of the market return,

�Market Beta� �iM �

cov�Ri , RM �

�2�RM �

The first term on the right-hand side of the minimum variance condition,

E(RZM), is the expected return on assets that have market betas equal to zero,

which means their returns are uncorrelated with the market return. The second

term is a risk premium—the market beta of asset i, �iM, times the premium per

unit of beta, which is the expected market return, E(RM), minus E(RZM).

Since the market beta of asset i is also the slope in the regression of its return

on the market return, a common (and correct) interpretation of beta is that it

measures the sensitivity of the asset’s return to variation in the market return. But

there is another interpretation of beta more in line with the spirit of the portfolio

model that underlies the CAPM. The risk of the market portfolio, as measured by

the variance of its return (the denominator of �iM), is a weighted average of the

covariance risks of the assets in M (the numerators of �iM for different assets).

The Capital Asset Pricing Model: Theory and Evidence 29

Thus, �iM is the covariance risk of asset i in M measured relative to the average

covariance risk of assets, which is just the variance of the market return.3 In

economic terms, �iM is proportional to the risk each dollar invested in asset i

contributes to the market portfolio.

The last step in the development of the Sharpe-Lintner model is to use the

assumption of risk-free borrowing and lending to nail down E(RZM), the expected

return on zero-beta assets. A risky asset’s return is uncorrelated with the market

return—its beta is zero—when the average of the asset’s covariances with the

returns on other assets just offsets the variance of the asset’s return. Such a risky

asset is riskless in the market portfolio in the sense that it contributes nothing to the

variance of the market return.

When there is risk-free borrowing and lending, the expected return on assets

that are uncorrelated with the market return, E(RZM), must equal the risk-free rate,

Rf. The relation between expected return and beta then becomes the familiar

Sharpe-Lintner CAPM equation,

�Sharpe-Lintner CAPM� E�Ri � � Rf � �E�RM � � Rf �]�iM , i � 1, . . . , N.

In words, the expected return on any asset i is the risk-free interest rate, Rf , plus a

risk premium, which is the asset’s market beta, �iM, times the premium per unit of

beta risk, E(RM) � Rf.

Unrestricted risk-free borrowing and lending is an unrealistic assumption.

Fischer Black (1972) develops a version of the CAPM without risk-free borrowing or

lending. He shows that the CAPM’s key result—that the market portfolio is mean-

variance-efficient—can be obtained by instead allowing unrestricted short sales of

risky assets. In brief, back in Figure 1, if there is no risk-free asset, investors select

portfolios from along the mean-variance-efficient frontier from a to b. Market

clearing prices imply that when one weights the efficient portfolios chosen by

investors by their (positive) shares of aggregate invested wealth, the resulting

portfolio is the market portfolio. The market portfolio is thus a portfolio of the

efficient portfolios chosen by investors. With unrestricted short selling of risky

assets, portfolios made up of efficient portfolios are themselves efficient. Thus, the

market portfolio is efficient, which means that the minimum variance condition for

M given above holds, and it is the expected return-risk relation of the Black CAPM.

The relations between expected return and market beta of the Black and

Sharpe-Lintner versions of the CAPM differ only in terms of what each says about

E(RZM), the expected return on assets uncorrelated with the market. The Black

version says only that E(RZM) must be less than the expected market return, so the

3 Formally, if xiM is the weight of asset i in the market portfolio, then the variance of the portfolio’s

return is

�2�RM� � Cov�RM , RM� � Cov� �

i�1

xiMRi , RM� � �

i�1

xiMCov�Ri , RM�.

30 Journal of Economic Perspectives

premium for beta is positive. In contrast, in the Sharpe-Lintner version of the

model, E(RZM) must be the risk-free interest rate, Rf , and the premium per unit of

beta risk is E(RM) � Rf.

The assumption that short selling is unrestricted is as unrealistic as unre-

stricted risk-free borrowing and lending. If there is no risk-free asset and short sales

of risky assets are not allowed, mean-variance investors still choose efficient

portfolios—points above b on the abc curve in Figure 1. But when there is no short

selling of risky assets and no risk-free asset, the algebra of portfolio efficiency says

that portfolios made up of efficient portfolios are not typically efficient. This means

that the market portfolio, which is a portfolio of the efficient portfolios chosen by

investors, is not typically efficient. And the CAPM relation between expected return

and market beta is lost. This does not rule out predictions about expected return

and betas with respect to other efficient portfolios—if theory can specify portfolios

that must be efficient if the market is to clear. But so far this has proven impossible.

In short, the familiar CAPM equation relating expected asset returns to their

market betas is just an application to the market portfolio of the relation between

expected return and portfolio beta that holds in any mean-variance-efficient port-

folio. The efficiency of the market portfolio is based on many unrealistic assump-

tions, including complete agreement and either unrestricted risk-free borrowing

and lending or unrestricted short selling of risky assets. But all interesting models

involve unrealistic simplifications, which is why they must be tested against data.

Early Empirical Tests

Tests of the CAPM are based on three implications of the relation between

expected return and market beta implied by the model. First, expected returns on

all assets are linearly related to their betas, and no other variable has marginal

explanatory power. Second, the beta premium is positive, meaning that the ex-

pected return on the market portfolio exceeds the expected return on assets whose

returns are uncorrelated with the market return. Third, in the Sharpe-Lintner

version of the model, assets uncorrelated with the market have expected returns

equal to the risk-free interest rate, and the beta premium is the expected market

return minus the risk-free rate. Most tests of these predictions use either cross-

section or time-series regressions. Both approaches date to early tests of the model.

Tests on Risk Premiums

The early cross-section regression tests focus on the Sharpe-Lintner model’s

predictions about the intercept and slope in the relation between expected return

and market beta. The approach is to regress a cross-section of average asset returns

on estimates of asset betas. The model predicts that the intercept in these regres-

sions is the risk-free interest rate, Rf , and the coefficient on beta is the expected

return on the market in excess of the risk-free rate, E(RM) � Rf.

Two problems in these tests quickly became apparent. First, estimates of beta

Eugene F. Fama and Kenneth R. French 31

for individual assets are imprecise, creating a measurement error problem when

they are used to explain average returns. Second, the regression residuals have

common sources of variation,

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such as industry effects in average returns. Positive

correlation in the residuals produces downward bias in the usual ordinary least

squares estimates of the standard errors of the cross-section regression slopes.

To improve the precision of estimated betas, researchers such as Blume

(1970), Friend and Blume (1970) and Black, Jensen and Scholes (1972) work with

portfolios, rather than individual securities. Since expected returns and market

betas combine in the same way in portfolios, if the CAPM explains security returns

it also explains portfolio returns.4 Estimates of beta for diversified portfolios are

more precise than estimates for individual securities. Thus, using portfolios in

cross-section regressions of average returns on betas reduces the critical errors in

variables problem. Grouping, however, shrinks the range of betas and reduces

statistical power. To mitigate this problem, researchers sort securities on beta when

forming portfolios; the first portfolio contains securities with the lowest betas, and

so on, up to the last portfolio with the highest beta assets. This sorting procedure

is now standard in empirical tests.

Fama and MacBeth (1973) propose a method for addressing the inference

problem caused by correlation of the residuals in cross-section regressions. Instead

of estimating a single cross-section regression of average monthly returns on betas,

they estimate month-by-month cross-section regressions of monthly returns on

betas. The times-series means of the monthly slopes and intercepts, along with the

standard errors of the means, are then used to test whether the average premium

for beta is positive and whether the average return on assets uncorrelated with the

market is equal to the average risk-free interest rate. In this approach, the standard

errors of the average intercept and slope are determined by the month-to-month

variation in the regression coefficients, which fully captures the effects of residual

correlation on variation in the regression coefficients, but sidesteps the problem of

actually estimating the correlations. The residual correlations are, in effect, cap-

tured via repeated sampling of the regression coefficients. This approach also

becomes standard in the literature.

Jensen (1968) was the first to note that the Sharpe-Lintner version of the

4 Formally, if xip, i � 1, . . . , N, are the weights for assets in some portfolio p, the expected return and

market beta for the portfolio are related to the expected returns and betas of assets as

E�Rp� � �

i�1

xipE�Ri�, and �pM � �

i�1

xip�pM .

Thus, the CAPM relation between expected return and beta,

E�Ri� � E�Rf� � �E�RM� � E�Rf��iM ,

holds when asset i is a portfolio, as well as when i is an individual security.

32 Journal of Economic Perspectives

relation between expected return and market beta also implies a time-series re-

gression test. The Sharpe-Lintner CAPM says that the expected value of an asset’s

excess return (the asset’s return minus the risk-free interest rate, Rit � Rft) is

completely explained by its expected CAPM risk premium (its beta times the

expected value of RMt � Rft). This implies that “Jensen’s alpha,” the intercept term

in the time-series regression,

�Time-Series Regression� Rit � Rft � �i � �iM �RMt � Rft � � �it ,

is zero for each asset.

The early tests firmly reject the Sharpe-Lintner version of the CAPM. There is

a positive relation between beta and average return, but it is too “flat.” Recall that,

in cross-section regressions, the Sharpe-Lintner model predicts that the intercept is

the risk-free rate and the coefficient on beta is the expected market return in excess

of the risk-free rate, E(RM) � Rf. The regressions consistently find that the

intercept is greater than the average risk-free rate (typically proxied as the return

on a one-month Treasury bill), and the coefficient on beta is less than the average

excess market return (proxied as the average return on a portfolio of U.S. common

stocks minus the Treasury bill rate). This is true in the early tests, such as Douglas

(1968), Black, Jensen and Scholes (1972), Miller and Scholes (1972), Blume and

Friend (1973) and Fama and MacBeth (1973), as well as in more recent cross-

section regression tests, like Fama and French (1992).

The evidence that the relation between beta and average return is too flat is

confirmed in time-series tests, such as Friend and Blume (1970), Black, Jensen and

Scholes (1972) and Stambaugh (1982). The intercepts in time-series regressions of

excess asset returns on the excess market return are positive for assets with low betas

and negative for assets with high betas.

Figure 2 provides an updated example of the evidence. In December of each

year, we estimate a preranking beta for every NYSE (1928–2003), AMEX (1963–

2003) and NASDAQ (1972–2003) stock in the CRSP (Center for Research in

Security Prices of the University of Chicago) database, using two to five years (as

available) of prior monthly returns.5 We then form ten value-weight portfolios

based on these preranking betas and compute their returns for the next twelve

months. We repeat this process for each year from 1928 to 2003. The result is

912 monthly returns on ten beta-sorted portfolios. Figure 2 plots each portfolio’s

average return against its postranking beta, estimated by regressing its monthly

returns for 1928–2003 on the return on the CRSP value-weight portfolio of U.S.

common stocks.

The Sharpe-Lintner CAPM predicts that the portfolios plot along a straight

5 To be included in the sample for year t, a security must have market equity data (price times shares

outstanding) for December of t � 1, and CRSP must classify it as ordinary common equity. Thus, we

exclude securities such as American Depository Receipts (ADRs) and Real Estate Investment Trusts

(REITs).

The Capital Asset Pricing Model: Theory and Evidence 33

line, with an intercept equal to the risk-free rate, Rf , and a slope equal to the

expected excess return on the market, E(RM) � Rf. We use the average one-month

Treasury bill rate and the average excess CRSP market return for 1928–2003 to

estimate the predicted line in Figure 2. Confirming earlier evidence, the relation

between beta and average return for the ten portfolios is much flatter than the

Sharpe-Lintner CAPM predicts. The returns on the low beta portfolios are too high,

and the returns on the high beta portfolios are too low. For example, the predicted

return on the portfolio with the lowest beta is 8.3 percent per year; the actual return

is 11.1 percent. The predicted return on the portfolio with the highest beta is

16.8 percent per year; the actual is 13.7 percent.

Although the observed premium per unit of beta is lower than the Sharpe-

Lintner model predicts, the relation between average return and beta in Figure 2

is roughly linear. This is consistent with the Black version of the CAPM, which

predicts only that the beta premium is positive. Even this less restrictive model,

however, eventually succumbs to the data.

Testing Whether Market Betas Explain Expected Returns

The Sharpe-Lintner and Black versions of the CAPM share the prediction that

the market portfolio is mean-variance-efficient. This implies that differences in

expected return across securities and portfolios are entirely explained by differ-

ences in market beta; other variables should add nothing to the explanation of

expected return. This prediction plays a prominent role in tests of the CAPM. In

the early work, the weapon of choice is cross-section regressions.

In the framework of Fama and MacBeth (1973), one simply adds predeter-

mined explanatory variables to the month-by-month cross-section regressions of

Figure 2

Average Annualized Monthly Return versus Beta for Value Weight Portfolios

Formed on Prior Beta, 1928–2003

Average returns

predicted by the

CAPM

0.5

0.7 0.9 1.1 1.3 1.5 1.7 1.9

liz

(

)

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https://doi.org/10.1016/S1569-3732(07)12014-4

The Capital Asset Pricing Model: Theory and Evidence

The Logic of the CAPM

Early Empirical Tests

Tests on Risk Premiums

Testing Whether Market Betas Explain Expected Returns

Recent Tests

Explanations: Irrational Pricing or Risk

The Market Proxy Problem

Conclusions

References

Perspectives

returns on beta. If all differences in expected return are explained by beta, the

average slopes on the additional variables should not be reliably different from

zero. Clearly, the trick in the cross-section regression approach is to choose specific

additional variables likely to expose any problems of the CAPM prediction that,

because the market portfolio is efficient, market betas suffice to explain expected

asset returns.

For example, in Fama and MacBeth (1973) the additional variables are

squared market betas (to test the prediction that the relation between expected

return and beta is linear) and residual variances from regressions of returns on the

market return (to test the prediction that market beta is the only measure of risk

needed to explain expected returns). These variables do not add to the explanation

of average returns provided by beta. Thus, the results of Fama and MacBeth (1973)

are consistent with the hypothesis that their market proxy—an equal-weight port-

folio of NYSE stocks—is on the minimum variance frontier.

The hypothesis that market betas completely explain expected returns can also

be tested using time-series regressions. In the time-series regression described

above (the excess return on asset i regressed on the excess market return), the

intercept is the difference between the asset’s average excess return and the excess

return predicted by the Sharpe-Lintner model, that is, beta times the average excess

market return. If the model holds, there is no way to group assets into portfolios

whose intercepts are reliably different from zero. For example, the intercepts for a

portfolio of stocks with high ratios of earnings to price and a portfolio of stocks with

low earning-price ratios should both be zero. Thus, to test the hypothesis that

market betas suffice to explain expected returns, one estimates the time-series

regression for a set of assets (or portfolios) and then jointly tests the vector of

regression intercepts against zero. The trick in this approach is to choose the

left-hand-side assets (or portfolios) in a way likely to expose any shortcoming of the

CAPM prediction that market betas suffice to explain expected asset returns.

In early applications, researchers use a variety of tests to determine whether

the intercepts in a set of time-series regressions are all zero. The tests have the same

asymptotic properties, but there is controversy about which has the best small

sample properties. Gibbons, Ross and Shanken (1989) settle the debate by provid-

ing an F-test on the intercepts that has exact small-sample properties. They also

show that the test has a simple economic interpretation. In effect, the test con-

structs a candidate for the tangency portfolio T in Figure 1 by optimally combining

the market proxy and the left-hand-side assets of the time-series regressions. The

estimator then tests whether the efficient set provided by the combination of this

tangency portfolio and the risk-free asset is reliably superior to the one obtained by

combining the risk-free asset with the market proxy alone. In other words, the

Gibbons, Ross and Shanken statistic tests whether the market proxy is the tangency

portfolio in the set of portfolios that can be constructed by combining the market

portfolio with the specific assets used as dependent variables in the time-series

regressions.

Enlightened by this insight of Gibbons, Ross and Shanken (1989), one can see

Eugene F. Fama and Kenneth R. French 35

a similar interpretation of the cross-section regression test of whether market betas

suffice to explain expected returns. In this case, the test is whether the additional

explanatory variables in a cross-section regression identify patterns in the returns

on the left-hand-side assets that are not explained by the assets’ market betas. This

amounts to testing whether the market proxy is on the minimum variance frontier

that can be constructed using the market proxy and the left-hand-side assets

included in the tests.

An important lesson from this discussion is that time-series and cross-section

regressions do not, strictly speaking, test the CAPM. What is literally tested is

whether a specific proxy for the market portfolio (typically a portfolio of U.S.

common stocks) is efficient in the set of portfolios that can be constructed from it

and the left-hand-side assets used in the test. One might conclude from this that the

CAPM has never been tested, and prospects for testing it are not good because

1) the set of left-hand-side assets does not include all marketable assets, and 2) data

for the true market portfolio of all assets are likely beyond reach (Roll, 1977; more

on this later). But this criticism can be leveled at tests of any economic model when

the tests are less than exhaustive or when they use proxies for the variables called

for by the model.

The bottom line from the early cross-section regression tests of the CAPM,

such as Fama and MacBeth (1973), and the early time-series regression tests, like

Gibbons (1982) and Stambaugh (1982), is that standard market proxies seem to be

on the minimum variance frontier. That is, the central predictions of the Black

version of the CAPM, that market betas suffice to explain expected returns and that

the risk premium for beta is positive, seem to hold. But the more specific prediction

of the Sharpe-Lintner CAPM that the premium per unit of beta is the expected

market return minus the risk-free interest rate is consistently rejected.

The success of the Black version of the CAPM in early tests produced a

consensus that the model is a good description of expected returns. These early

results, coupled with the model’s simplicity and intuitive appeal, pushed the CAPM

to the forefront of finance.

Recent Tests

Starting in the late 1970s, empirical work appears that challenges even the

Black version of the CAPM. Specifically, evidence mounts that much of the varia-

tion in expected return is unrelated to market beta.

The first blow is Basu’s (1977) evidence that when common stocks are sorted

on earnings-price ratios, future returns on high E/P stocks are higher than pre-

dicted by the CAPM. Banz (1981) documents a size effect: when stocks are sorted

on market capitalization (price times shares outstanding), average returns on small

stocks are higher than predicted by the CAPM. Bhandari (1988) finds that high

debt-equity ratios (book value of debt over the market value of equity, a measure of

leverage) are associated with returns that are too high relative to their market betas.

36 Journal of Economic Perspectives

Finally, Statman (1980) and Rosenberg, Reid and Lanstein (1985) document that

stocks with high book-to-market equity ratios (B/M, the ratio of the book value of

a common stock to its market value) have high average returns that are not

captured by their betas.

There is a theme in the contradictions of the CAPM summarized above. Ratios

involving stock prices have information about expected returns missed by market

betas. On reflection, this is not surprising. A stock’s price depends not only on the

expected cash flows it will provide, but also on the expected returns that discount

expected cash flows back to the present. Thus, in principle, the cross-section of

prices has information about the cross-section of expected returns. (A high ex-

pected return implies a high discount rate and a low price.) The cross-section of

stock prices is, however, arbitrarily affected by differences in scale (or units). But

with a judicious choice of scaling variable X, the ratio X/P can reveal differences

in the cross-section of expected stock returns. Such ratios are thus prime candidates

to expose shortcomings of asset pricing models—in the case of the CAPM, short-

comings of the prediction that market betas suffice to explain expected returns

(Ball, 1978). The contradictions of the CAPM summarized above suggest that

earnings-price, debt-equity and book-to-market ratios indeed play this role.

Fama and French (1992)

update and synthesize the evidence on the empirical

failures of the CAPM. Using the cross-section regression approach, they confirm

that size, earnings-price, debt-equity and book-to-market ratios add to the explana-

tion of expected stock returns provided by market beta. Fama and French (1996)

reach the same conclusion using the time-series regression approach applied to

portfolios of stocks sorted on price ratios. They also find that different price ratios

have much the same information about expected returns. This is not surprising

given that price is the common driving force in the price ratios, and the numerators

are just scaling variables used to extract the information in price about expected

returns.

Fama and French (1992) also confirm the evidence (Reinganum, 1981; Stam-

baugh, 1982; Lakonishok and Shapiro, 1986) that the relation between average

return and beta for common stocks is even flatter after the sample periods used in

the early empirical work on the CAPM. The estimate of the beta premium is,

however, clouded by statistical uncertainty (a large standard error). Kothari, Shan-

ken and Sloan (1995) try to resuscitate the Sharpe-Lintner CAPM by arguing that

the weak relation between average return and beta is just a chance result. But the

strong evidence that other variables capture variation in expected return missed by

beta makes this argument irrelevant. If betas do not suffice to explain expected

returns, the market portfolio is not efficient, and the CAPM is dead in its tracks.

Evidence on the size of the market premium can neither save the model nor further

doom it.

The synthesis of the evidence on the empirical problems of the CAPM pro-

vided by Fama and French (1992) serves as a catalyst, marking the point when it is

generally acknowledged that the CAPM has potentially fatal problems. Research

then turns to explanations.

The Capital Asset Pricing Model: Theory and Evidence 37

One possibility is that the CAPM’s problems are spurious, the result of data

dredging—publication-hungry researchers scouring the data and unearthing con-

tradictions that occur in specific samples as a result of chance. A standard response

to this concern is to test for similar findings in other samples. Chan, Hamao and

Lakonishok (1991) find a strong relation between book-to-market equity (B/M)

and average return for Japanese stocks. Capaul, Rowley and Sharpe (1993) observe

a similar B/M effect in four European stock markets and in Japan. Fama and

French (1998) find that the price ratios that produce problems for the CAPM in

U.S. data show up in the same way in the stock returns of twelve non-U.S. major

markets, and they are present in emerging market returns. This evidence suggests

that the contradictions of the CAPM associated with price ratios are not sample

specific.

Explanations: Irrational Pricing or Risk

Among those who conclude that the empirical failures of the CAPM are fatal,

two stories emerge. On one side are the behavioralists. Their view is based on

evidence that stocks with high ratios of book value to market price are typically

firms that have fallen on bad times, while low B/M is associated with growth firms

(Lakonishok, Shleifer and Vishny, 1994; Fama and French, 1995). The behavior-

alists argue that sorting firms on book-to-market ratios exposes investor overreac-

tion to good and bad times. Investors overextrapolate past performance, resulting

in stock prices that are too high for growth (low B/M) firms and too low for

distressed (high B/M, so-called value) firms. When the overreaction is eventually

corrected, the result is high returns for value stocks and low returns for growth

stocks. Proponents of this view include DeBondt and Thaler (1987), Lakonishok,

Shleifer and Vishny (1994) and Haugen (1995).

The second story for explaining the empirical contradictions of the CAPM is

that they point to the need for a more complicated asset pricing model. The CAPM

is based on many unrealistic assumptions. For example, the assumption that

investors care only about the mean and variance of one-period portfolio returns is

extreme. It is reasonable that investors also care about how their portfolio return

covaries with labor income and future investment opportunities, so a portfolio’s

return variance misses important dimensions of risk. If so, market beta is not a

complete description of an asset’s risk, and we should not be surprised to find that

differences in expected return are not completely explained by differences in beta.

In this view, the search should turn to asset pricing models that do a better job

explaining average returns.

Merton’s (1973) intertemporal capital asset pricing model (ICAPM) is a

natural extension of the CAPM. The ICAPM begins with a different assumption

about investor objectives. In the CAPM, investors care only about the wealth their

portfolio produces at the end of the current period. In the ICAPM, investors are

concerned not only with their end-of-period payoff, but also with the opportunities

38 Journal of Economic Perspectives

they will have to consume or invest the payoff. Thus, when choosing a portfolio at

time t � 1, ICAPM investors consider how their wealth at t might vary with future

state variables, including labor income, the prices of consumption goods and the

nature of portfolio opportunities at t, and expectations about the labor income,

consumption and investment opportunities to be available after t.

Like CAPM investors, ICAPM investors prefer high expected return and low

return variance. But ICAPM investors are also concerned with the covariances of

portfolio returns with state variables. As a result, optimal portfolios are “multifactor

efficient,” which means they have the largest possible expected returns, given their

return variances and the covariances of their returns with the relevant state

variables.

Fama (1996) shows that the ICAPM generalizes the logic of the CAPM. That is,

if there is risk-free borrowing and lending or if short sales of risky assets are allowed,

market clearing prices imply that the market portfolio is multifactor efficient.

Moreover, multifactor efficiency implies a relation between expected return and

beta risks, but it requires additional betas, along with a market beta, to explain

expected returns.

An ideal implementation of the ICAPM would specify the state variables that

affect expected returns. Fama and French (1993) take a more indirect approach,

perhaps more in the spirit of Ross’s (1976) arbitrage pricing theory. They argue

that though size and book-to-market equity are not themselves state variables, the

higher average returns on small stocks and high book-to-market stocks reflect

unidentified state variables that produce undiversifiable risks (covariances) in

returns that are not captured by the market return and are priced separately from

market betas. In support of this claim, they show that the returns on the stocks of

small firms covary more with one another than with returns on the stocks of large

firms, and returns on high book-to-market (value) stocks covary more with one

another than with returns on low book-to-market (growth) stocks. Fama and

French (1995) show that there are similar size and book-to-market patterns in the

covariation of fundamentals like earnings and sales.

Based on this evidence, Fama and French (1993, 1996) propose a three-factor

model for expected returns,

�Three-Factor Model� E�Rit � � Rft � �iM �E�RMt � � Rft �

� �isE�SMBt� � �ihE�HMLt�.

In this equation, SMBt (small minus big) is the difference between the returns on

diversified portfolios of small and big stocks, HMLt (high minus low) is the

difference between the returns on diversified portfolios of high and low B/M

stocks, and the betas are slopes in the multiple regression of Rit � Rft on RMt � Rft,

SMBt and HMLt.

For perspective, the average value of the market premium RMt � Rft for

1927–2003 is 8.3 percent per year, which is 3.5 standard errors from zero. The

Eugene F. Fama and Kenneth R. French

average values of SMBt, and HMLt are 3.6 percent and 5.0 percent per year, and

they are 2.1 and 3.1 standard errors from zero. All three premiums are volatile, with

annual standard deviations of 21.0 percent (RMt � Rft), 14.6 percent (SMBt) and

14.2 percent (HMLt) per year. Although the average values of the premiums are

large, high volatility implies substantial uncertainty about the true expected

premiums.

One implication of the expected return equation of the three-factor model is

that the intercept �i in the time-series regression,

Rit � Rft � �i � �iM�RMt � Rft� � �isSMBt � �ihHMLt � �it ,

is zero for all assets i. Using this criterion, Fama and French (1993, 1996) find that

the model captures much of the variation in average return for portfolios formed

on size, book-to-market equity and other price ratios that cause problems for the

CAPM. Fama and French (1998) show that an international version of the model

performs better than an international CAPM in describing average returns on

portfolios formed on scaled price variables for stocks in 13 major markets.

The three-factor model is now widely used in empirical research that requires

a model of expected returns. Estimates of �i from the time-series regression above

are used to calibrate how rapidly stock prices respond to new information (for

example, Loughran and Ritter, 1995; Mitchell and Stafford, 2000). They are also

used to measure the special information of portfolio managers, for example, in

Carhart’s (1997) study of mutual fund performance. Among practitioners like

Ibbotson Associates, the model is offered as an alternative to the CAPM for

estimating the cost of equity capital.

From a theoretical perspective, the main shortcoming of the three-factor

model is its empirical motivation. The small-minus-big (SMB) and high-minus-low

(HML) explanatory returns are not motivated by predictions about state variables

of concern to investors. Instead they are brute force constructs meant to capture

the patterns uncovered by previous work on how average stock returns vary with size

and the book-to-market equity ratio.

But this concern is not fatal. The ICAPM does not require that the additional

portfolios used along with the market portfolio to explain expected returns

“mimic” the relevant state variables. In both the ICAPM and the arbitrage pricing

theory, it suffices that the additional portfolios are well diversified (in the termi-

nology of Fama, 1996, they are multifactor minimum variance) and that they are

sufficiently different from the market portfolio to capture covariation in returns

and variation in expected returns missed by the market portfolio. Thus, adding

diversified portfolios that capture covariation in returns and variation in average

returns left unexplained by the market is in the spirit of both the ICAPM and the

Ross’s arbitrage pricing theory.

The behavioralists are not impressed by the evidence for a risk-based expla-

nation of the failures of the CAPM. They typically concede that the three-factor

model captures covariation in returns missed by the market return and that it picks

40 Journal of Economic Perspectives

up much of the size and value effects in average returns left unexplained by the

CAPM. But their view is that the average return premium associated with the

model’s book-to-market factor—which does the heavy lifting in the improvements

to the CAPM—is itself the result of investor overreaction that happens to be

correlated across firms in a way that just looks like a risk story. In short, in the

behavioral view, the market tries to set CAPM prices, and violations of the CAPM

are due to mispricing.

The conflict between the behavioral irrational pricing story and the rational

risk story for the empirical failures of the CAPM leaves us at a timeworn impasse.

Fama (1970) emphasizes that the hypothesis that prices properly reflect available

information must be tested in the context of a model of expected returns, like the

CAPM. Intuitively, to test whether prices are rational, one must take a stand on what

the market is trying to do in setting prices—that is, what is risk and what is the

relation between expected return and risk? When tests reject the CAPM, one

cannot say whether the problem is its assumption that prices are rational (the

behavioral view) or violations of other assumptions that are also necessary to

produce the CAPM (our position).

Fortunately, for some applications, the way one uses the three-factor model

does not depend on one’s view about whether its average return premiums are the

rational result of underlying state variable risks, the result of irrational investor

behavior or sample specific results of chance. For example, when measuring the

response of stock prices to new information or when evaluating the performance of

managed portfolios, one wants to account for known patterns in returns and

average returns for the period examined, whatever their source. Similarly, when

estimating the cost of equity capital, one might be unconcerned with whether

expected return premiums are rational or irrational since they are in either case

part of the opportunity cost of equity capital (Stein, 1996). But the cost of capital

is forward looking, so if the premiums are sample specific they are irrelevant.

The three-factor model is hardly a panacea. Its most serious problem is the

momentum effect of Jegadeesh and Titman (1993). Stocks that do well relative to

the market over the last three to twelve months tend to continue to do well for the

next few months, and stocks that do poorly continue to do poorly. This momentum

effect is distinct from the value effect captured by book-to-market equity and other

price ratios. Moreover, the momentum effect is left unexplained by the three-factor

model, as well as by the CAPM. Following Carhart (1997), one response is to add

a momentum factor (the difference between the returns on diversified portfolios of

short-term winners and losers) to the three-factor model. This step is again legiti-

mate in applications where the goal is to abstract from known patterns in average

returns to uncover information-specific or manager-specific effects. But since the

momentum effect is short-lived, it is largely irrelevant for estimates of the cost of

equity capital.

Another strand of research points to problems in both the three-factor model

and the CAPM. Frankel and Lee (1998), Dechow, Hutton and Sloan (1999),

Piotroski (2000) and others show that in portfolios formed on price ratios like

The Capital Asset Pricing Model: Theory and Evidence 41

book-to-market equity, stocks with higher expected cash flows have higher average

returns that are not captured by the three-factor model or the CAPM. The authors

interpret their results as evidence that stock prices are irrational, in the sense that

they do not reflect available information about expected profitability.

In truth, however, one can’t tell whether the problem is bad pricing or a bad

asset pricing model. A stock’s price can always be expressed as the present value of

expected future cash flows discounted at the expected return on the stock (Camp-

bell and Shiller, 1989; Vuolteenaho, 2002). It follows that if two stocks have the

same price, the one with higher expected cash flows must have a higher expected

return. This holds true whether pricing is rational or irrational. Thus, when one

observes a positive relation between expected cash flows and expected returns that

is left unexplained by the CAPM or the three-factor model, one can’t tell whether

it is the result of irrational pricing or a misspecified asset pricing model.

The Market Proxy Problem

Roll (1977) argues that the CAPM has never been tested and probably never

will be. The problem is that the market portfolio at the heart of the model is

theoretically and empirically elusive. It is not theoretically clear which assets (for

example, human capital) can legitimately be excluded from the market portfolio,

and data availability substantially limits

the assets that are included. As a result, tests

of the CAPM are forced to use proxies for the market portfolio, in effect testing

whether the proxies are on the minimum variance frontier. Roll argues that

because the tests use proxies, not the true market portfolio, we learn nothing about

the CAPM.

We are more pragmatic. The relation between expected return and market

beta of the CAPM is just the minimum variance condition that holds in any efficient

portfolio, applied to the market portfolio. Thus, if we can find a market proxy that

is on the minimum variance frontier, it can be used to describe differences in

expected returns, and we would be happy to use it for this purpose. The strong

rejections of the CAPM described above, however, say that researchers have not

uncovered a reasonable market proxy that is close to the minimum variance

frontier. If researchers are constrained to reasonable proxies, we doubt they

ever will.

Our pessimism is fueled by several empirical results. Stambaugh (1982) tests

the CAPM using a range of market portfolios that include, in addition to U.S.

common stocks, corporate and government bonds, preferred stocks, real estate and

other consumer durables. He finds that tests of the CAPM are not sensitive to

expanding the market proxy beyond common stocks, basically because the volatility

of expanded market returns is dominated by the volatility of stock returns.

One need not be convinced by Stambaugh’s (1982) results since his market

proxies are limited to U.S. assets. If international capital markets are open and asset

prices conform to an international version of the CAPM, the market portfolio

42 Journal of Economic Perspectives

should include international assets. Fama and French (1998) find, however, that

betas for a global stock market portfolio cannot explain the high average returns

observed around the world on stocks with high book-to-market or high earnings-

price ratios.

A major problem for the CAPM is that portfolios formed by sorting stocks on

price ratios produce a wide range of average returns, but the average returns are

not positively related to market betas (Lakonishok, Shleifer and Vishny, 1994; Fama

and French, 1996, 1998). The problem is illustrated in Figure 3, which shows

average returns and betas (calculated with respect to the CRSP value-weight port-

folio of NYSE, AMEX and NASDAQ stocks) for July 1963 to December 2003 for ten

portfolios of U.S. stocks formed annually on sorted values of the book-to-market

equity ratio (B/M).6

Average returns on the B/M portfolios increase almost monotonically, from

10.1 percent per year for the lowest B/M group (portfolio 1) to an impressive

16.7 percent for the highest (portfolio 10). But the positive relation between beta

and average return predicted by the CAPM is notably absent. For example, the

portfolio with the lowest book-to-market ratio has the highest beta but the lowest

average return. The estimated beta for the portfolio with the highest book-to-

market ratio and the highest average return is only 0.98. With an average annual-

ized value of the riskfree interest rate, Rf , of 5.8 percent and an average annualized

market premium, RM � Rf , of 11.3 percent, the Sharpe-Lintner CAPM predicts an

average return of 11.8 percent for the lowest B/M portfolio and 11.2 percent for

the highest, far from the observed values, 10.1 and 16.7 percent. For the Sharpe-

Lintner model to “work” on these portfolios, their market betas must change

dramatically, from 1.09 to 0.78 for the lowest B/M portfolio and from 0.98 to 1.98

for the highest. We judge it unlikely that alternative proxies for the market

portfolio will produce betas and a market premium that can explain the average

returns on these portfolios.

It is always possible that researchers will redeem the CAPM by finding a

reasonable proxy for the market portfolio that is on the minimum variance frontier.

We emphasize, however, that this possibility cannot be used to justify the way the

CAPM is currently applied. The problem is that applications typically use the same

6 Stock return data are from CRSP, and book equity data are from Compustat and the Moody’s

Industrials, Transportation, Utilities and Financials manuals. Stocks are allocated to ten portfolios at the

end of June of each year t (1963 to 2003) using the ratio of book equity for the fiscal year ending in

calendar year t � 1, divided by market equity at the end of December of t � 1. Book equity is the book

value of stockholders’ equity, plus balance sheet deferred taxes and investment tax credit (if available),

minus the book value of preferred stock. Depending on availability, we use the redemption, liquidation

or par value (in that order) to estimate the book value of preferred stock. Stockholders’ equity is the

value reported by Moody’s or Compustat, if it is available. If not, we measure stockholders’ equity as the

book value of common equity plus the par value of preferred stock or the book value of assets minus

total liabilities (in that order). The portfolios for year t include NYSE (1963–2003), AMEX (1963–2003)

and NASDAQ (1972–2003) stocks with positive book equity in t � 1 and market equity (from CRSP) for

December of t � 1 and June of t. The portfolios exclude securities CRSP does not classify as ordinary

common equity. The breakpoints for year t use only securities that are on the NYSE in June of year t.

Eugene F. Fama and Kenneth R. French 43

market proxies, like the value-weight portfolio of U.S. stocks, that lead to rejections

of the model in empirical tests. The contradictions of the CAPM observed when

such proxies are used in tests of the model show up as bad estimates of expected

returns in applications; for example, estimates of the cost of equity capital that are

too low (relative to historical average returns) for small stocks and for stocks with

high book-to-market equity ratios. In short, if a market proxy does not work in tests

of the CAPM, it does not work in applications.

Conclusions

The version of the CAPM developed by Sharpe (1964) and Lintner (1965) has

never been an empirical success. In the early empirical work, the Black (1972)

version of the model, which can accommodate a flatter tradeoff of average return

for market beta, has some success. But in the late 1970s, research begins to uncover

variables like size, various price ratios and momentum that add to the explanation

of average returns provided by beta. The problems are serious enough to invalidate

most applications of the CAPM.

For example, finance textbooks often recommend using the Sharpe-Lintner

CAPM risk-return relation to estimate the cost of equity capital. The prescription is

to estimate a stock’s market beta and combine it with the risk-free interest rate and

the average market risk premium to produce an estimate of the cost of equity. The

typical market portfolio in these exercises includes just U.S. common stocks. But

empirical work, old and new, tells us that the relation between beta and average

return is flatter than predicted by the Sharpe-Lintner version of the CAPM. As a

Figure 3

Average Annualized Monthly Return versus Beta for Value Weight Portfolios

Formed on B/M, 1963–2003

Average returns

predicted by

the CAPM

0.7

0.8

10 (highest B/M)

1 (lowest B/M)

5 4

0.9 1 1.1 1.2

liz

(

)

44 Journal of Economic Perspectives

result, CAPM estimates of the cost of equity for high beta stocks are too high

(relative to historical average returns) and estimates for low beta stocks are too low

(Friend and Blume, 1970). Similarly, if the high average returns on value stocks

(with high book-to-market ratios) imply high expected returns, CAPM cost of

equity estimates for such stocks are too low.7

The CAPM is also often used to measure the performance of mutual funds and

other managed portfolios. The approach, dating to Jensen (1968), is to estimate

the CAPM time-series regression

for a portfolio and use the intercept (Jensen’s

alpha) to measure abnormal performance. The problem is that, because of the

empirical failings of the CAPM, even passively managed stock portfolios produce

abnormal returns if their investment strategies involve tilts toward CAPM problems

(Elton, Gruber, Das and Hlavka, 1993). For example, funds that concentrate on low

beta stocks, small stocks or value stocks will tend to produce positive abnormal

returns relative to the predictions of the Sharpe-Lintner CAPM, even when the

fund managers have no special talent for picking winners.

The CAPM, like Markowitz’s (1952, 1959) portfolio model on which it is built,

is nevertheless a theoretical tour de force. We continue to teach the CAPM as an

introduction to the fundamental concepts of portfolio theory and asset pricing, to

be built on by more complicated models like Merton’s (1973) ICAPM. But we also

warn students that despite its seductive simplicity, the CAPM’s empirical problems

probably invalidate its use in applications.

y We gratefully acknowledge the comments of John Cochrane, George Constantinides, Richard

Leftwich, Andrei Shleifer, René Stulz and Timothy Taylor.

7 The problems are compounded by the large standard errors of estimates of the market premium and

of betas for individual stocks, which probably suffice to make CAPM estimates of the cost of equity rather

meaningless, even if the CAPM holds (Fama and French, 1997; Pastor and Stambaugh, 1999). For

example, using the U.S. Treasury bill rate as the risk-free interest rate and the CRSP value-weight

portfolio of publicly traded U.S. common stocks, the average value of the equity premium RMt � Rft for

1927–2003 is 8.3 percent per year, with a standard error of 2.4 percent. The two standard error range

thus runs from 3.5 percent to 13.1 percent, which is sufficient to make most projects appear either

profitable or unprofitable. This problem is, however, hardly special to the CAPM. For example, expected

returns in all versions of Merton’s (1973) ICAPM include a market beta and the expected market

premium. Also, as noted earlier the expected values of the size and book-to-market premiums in the

Fama-French three-factor model are also estimated with substantial error.

The Capital Asset Pricing Model: Theory and Evidence 45

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