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Title
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Emerging Trends: Asset Pricing
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Author
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Campbell, John Y.
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Research Area
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Social Institutions
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Topic
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Work and the Economy
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Abstract
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The modern field of asset pricing is organized around the concept of the stochastic discount factor. This essay uses this framework to discuss the literature on predictability of asset returns in the short and long run, the influence of irrational investor expectations on asset prices, and the cross‐section of stock returns. Future progress will require microeconomic data on investor actions and ideally survey evidence on their risk preferences and beliefs.
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Identifier
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etrds0015
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extracted text
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Emerging Trends: Asset Pricing
JOHN Y. CAMPBELL
Abstract
The modern field of asset pricing is organized around the concept of the stochastic
discount factor. This essay uses this framework to discuss the literature on predictability of asset returns in the short and long run, the influence of irrational
investor expectations on asset prices, and the cross-section of stock returns. Future
progress will require microeconomic data on investor actions and ideally survey
evidence on their risk preferences and beliefs.
INTRODUCTION
During the past 40 years, the field of asset pricing has exploited a standard
conceptual framework to make a series of important empirical findings.
While there remains disagreement about how to interpret these facts, the
disagreement is expressed in a common language. The intellectual coherence of the field was recognized by the award of the 2013 Nobel Memorial
Prize in Economics to Eugene Fama, Lars Peter Hansen, and Robert Shiller
for empirical analysis of asset prices. This review, drawing on Campbell
(2014), shows how emerging trends in asset pricing relate to the standard
framework.
THE STOCHASTIC DISCOUNT FACTOR: THE FRAMEWORK
OF CONTEMPORARY FINANCE
THE SDF IN COMPLETE MARKETS
The modern theory of the SDF can be summarized as follows. Consider a
discrete-state model with two periods, the present and the future, and complete markets. There are S states of nature s = 1 … S, all of which have strictly
positive probability ?(s). Markets are complete, that is, for each state s a contingent claim is available that pays $1 in state s and nothing in any other state.
Write the price of this contingent claim as q(s). All other assets can be defined
by their state-contingent payoffs X(s).
Emerging Trends in the Social and Behavioral Sciences. Edited by Robert Scott and Stephen Kosslyn.
© 2015 John Wiley & Sons, Inc. ISBN 978-1-118-90077-2.
1
2
EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES
The absence of arbitrage implies that all contingent claim prices are strictly
positive and that the price of any asset satisfies
P(X) =
S
∑
q(s)X(s).
(1)
s=1
If we multiply and divide Equation 1 by the objective probability of each
state, ?(s), we obtain
P(X) =
S
∑
s=1
∑
q(s)
?(s)M(s)X(s) = E[MX],
X(s) =
?(s)
s=1
S
?(s)
(2)
where M(s) = q(s)/?(s) is the ratio of state price to probability for state s, the
stochastic discount factor or SDF in state s. As q(s) and ?(s) are strictly positive
for all states s, M(s) is also. The last equality in Equation 2 uses the definition
of an expectation as a probability-weighted average of a random variable to
write the asset price as the expected product of the asset’s payoff and the SDF.
This equation is sometimes given the rather grand title of the Fundamental
Equation of Asset Pricing.
The SDF can be related to the decisions of an investor who chooses consumption today and in each future state to maximize time-separable utility
of consumption. The investor’s first-order conditions imply that
M(s) =
q(s)
?u′ (C(s))
=
.
?(s)
u′ (C0 )
(3)
In words, the SDF is the discounted ratio of marginal utility tomorrow to
marginal utility today. This representation of the SDF is the starting point for
the large literature on equilibrium asset pricing, which seeks to relate asset
prices to the arguments of consumers’ utility and particularly to their measured consumption of goods and services.
The formula given above assumes that all investors have rational expectations and thus assign the same objective probabilities to the different states of
the world. If this is not the case, we must assign investor-specific subscripts
to the probabilities, writing ? j (s) for investor j’s subjective probability of
state s. In general, we must also allow for differences in the utility function
across investors, adding a j subscript to marginal utility as well. Then, we
can show that
(
) ( ?u′ (C (s)) )
?j (s)
j
q(s)
j
(4)
=
M(s) =
′
?(s)
?(s)
uj (Cj0 )
Volatility of the SDF across states may correspond either to volatile deviations of investor j’s subjective probabilities from objective probabilities or to
Emerging Trends: Asset Pricing
3
volatile marginal utility across states. The usual assumption that investors
have homogeneous beliefs rules out the first of these possibilities, but an
emerging trend in asset pricing, following the behavioral finance literature,
is to model belief heterogeneity.
INCOMPLETE MARKETS AND VOLATILITY BOUNDS
The discussion so far assumes complete markets, but the SDF framework is
just as useful when markets are incomplete. Cochrane (2005) offers a textbook
treatment.
In incomplete markets, the existence of a strictly positive SDF is guaranteed by the absence of arbitrage—a result sometimes called the Fundamental
Theorem of Asset Pricing—but the SDF is no longer unique as it is in complete
markets. Intuitively, an SDF can be calculated from the marginal utility of
any investor who can trade assets freely, but with incomplete markets, each
investor can have idiosyncratic variation in his or her marginal utility and
hence there are many possible SDFs.
There is however a unique SDF that can be written as a linear combination
of asset payoffs and that satisfies the fundamental equation of asset pricing
(Equation 2). This unique random variable is the projection of any SDF onto
the space of asset payoffs, and thus any other SDF must have a higher variance. Shiller (1982), a comment by Hansen (1982a), and Hansen and Jagannathan (1991) used this insight to place lower bounds on the volatility of the
SDF, based only on the properties of asset returns.
The idea of using asset return data to restrict the properties of the SDF
remains a fruitful one. More recent work by Stutzer (1995), Bansal and
Lehmann (1997), Alvarez and Jermann (2005), and Backus, Chernov, and
Zin (2011), for example, shows how asset returns place lower bounds on
the entropy of the SDF. Entropy, an alternative to variance as a measure
of randomness, is playing an increasingly important role in asset pricing
theory.
PREDICTING ASSET RETURNS IN THE SHORT AND LONG RUN
THE INFORMATION IN ASSET PRICES
Predictive regressions extract information in asset prices. During the 1980s,
a series of papers studied fixed-income securities and found that their prices
(equivalently, their yields) predicted their returns. For example, the interest differential between two currencies should predict depreciation of the
high-interest-rate currency if expected rates of return are equal in the two
currencies; in fact, it predicts appreciation of the high-interest-rate currency,
implying large excess returns in that currency, a phenomenon that is the
4
EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES
basis for the currency carry trade (Fama, 1984b). Similarly, the yield spread
between two interest rates of different maturities should predict increases
in both short-term and long-term interest rates if expected returns are equal
across maturities; in fact, as shown by Shiller, Campbell, and Schoenholtz
(1983) and Campbell and Shiller (1991) as well as Fama (1984a) and Fama
and Bliss (1987), long-term rates tend to decline after yield spreads become
unusually wide.
LeRoy and Porter (1979) and Shiller (1981) shifted attention to the stock
market and argued that aggregate stock prices were too volatile to be
consistent with a model in which prices equal expected future dividends,
discounted at a constant rate. After a period of controversy, Campbell and
Shiller (1988a) introduced a framework within which the contributions of
time-varying expected dividends and discount rates could be separately
quantified. By taking a Taylor approximation of the nonlinear equation
relating log returns to log prices and log dividends, around the mean of
the log dividend-price ratio, and solving forward the resulting loglinear
difference equation, Campbell and Shiller found that
∑
−k
d t − pt ≈
?j [−Δdt+1+j + rt+1+j ],
+
1 − ? j=0
∞
(5)
where lower-case letters denote logs, k and ? are parameters of loglinearization, and the asset-specific subscript i has been dropped for notational simplicity.
This approximate equation holds ex post, as an accounting identity. It
should therefore hold ex ante, not only for rational expectations but also for
any expectations that satisfy identities. As the dividend-price ratio at time
t is known at time t, it follows that the dividend-price ratio can be written
as a discounted sum of expected future dividend growth and returns. These
two components can be thought of as the “cash flow” and “discount-rate”
components of the dividend-price ratio. Campbell and Shiller (1988a, 1988b)
estimated these components using vector autoregressions forecasting returns
(or dividend growth) with other variables including the log dividend-price
ratio. As they calculated expected returns from an econometric forecasting
model, they were estimating the discount rates that would be applied to
cash flows by an investor with rational expectations.
In the late 1980s, a consensus developed about facts if not interpretations.
The methods developed by Campbell and Shiller found a large contribution
of time-varying discount rates to the volatility of the log dividend-price ratio,
using VAR forecasts of long-run discounted stock returns. Fama and French
(1988a) ran direct regressions of long-horizon returns onto the dividend-price
ratio and found high explanatory power for these regressions. Fama and
Emerging Trends: Asset Pricing
5
French (1988b) and Poterba and Summers (1988), in related work, reported
evidence for negative serial correlation of stock returns at annual and lower
frequencies. All these results implied that time-varying discount rates—that
is, rational expectations of future returns—are important for understanding
the variability of the aggregate stock market.
FINANCE THEORY AND RETURN PREDICTABILITY
The literature on return predictability has remained active over the past
25 years. Variance decompositions can be calculated not only for the log
dividend-price ratio but also for other valuation ratios such as the log ratio
of prices to smoothed earnings (Campbell & Shiller, 1988b) and the log
ratio of enterprise value to total payout (Larrain & Yogo 2008), or even for
the log ratio of consumption to total wealth (Lettau & Ludvigson, 2001a).
Each of these ratios may have a different decomposition, as transitory
variation in a payout measure implies variability in expected future payout
growth, which in turn contributes to the volatility of the corresponding
valuation ratio. As an alternative approach, Campbell (1991) suggested
calculating variance decompositions for returns rather than for valuation
ratios. Variance decompositions can also be calculated for individual stocks
and style portfolios, although this requires confronting nonstationarities
in firm-level payout policy that are less important at the aggregate level
(Vuolteenaho, 2002, Cohen, Polk, & Vuolteenaho, 2009).
There has been considerable concern about the small-sample properties of
regressions predicting stock returns from valuation ratios. Stambaugh (1999)
pointed out that when the explanatory variables in return-predicting regressions are persistent and have innovations that are correlated with returns (as
is certainly the case for valuation ratios), the coefficients are biased upward. A
similar problem afflicts t-statistics as shown by Cavanagh, Elliott, and Stock
(1995).
During the 2000s, a number of papers proposed to use theoretical restrictions to improve the power of tests of return predictability. Lewellen (2004)
showed that when theory tells us that the log dividend-price ratio cannot
be explosive, it is possible to compute a test statistic under the worst-case
assumption that this ratio has a unit root. In samples where the valuation
ratio appears to have a root very close to unity, Lewellen’s test can reject
more strongly than the standard test—and this is exactly what happens
in an application to US data. Campbell and Yogo (2006) propose a related
procedure.
Cochrane (2008) emphasizes the inability of the log dividend-price ratio
to predict dividend growth. Using the Campbell–Shiller approximation, he
notes that if the dividend-price ratio fails to predict stock returns positively,
6
EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES
it will be explosive unless it predicts dividend growth negatively. Appealing
like Lewellen (2004) to a theoretical presumption that the dividend-price
ratio cannot be explosive, Cochrane argues that the absence of predictable
dividend growth strengthens the evidence for predictable returns.
Several other recent papers also use restrictions from finance theory to
improve forecasts of stock returns. Goyal and Welch (2008) direct attention
to the poor out-of-sample performance of return predictions based on
regressions that include both a constant and time-varying explanatory variables such as the dividend-price ratio. The difficulty with these regressions
is partly that they must estimate the unconditional mean of stock returns
using noisy historical data. In response, Campbell and Thompson (2008)
show that out-of-sample performance is improved by imposing theoretically
motivated sign restrictions on the regression coefficient and the fitted value
and improved further using a version of a Gordon growth model to avoid
directly estimating the unconditional mean of stock returns. Similarly, Fama
and French (2002) argue that the unconditional mean stock return can be
better estimated by correcting historical average returns for the historical
change in valuation ratios because finance theory implies that such changes
cannot be expected to continue. Avdis and Wachter (2013) make a similar
correction in a formal maximum likelihood framework.
During the same quarter-century, finance theorists have built increasingly
sophisticated equilibrium models that are consistent with the econometric
evidence for return predictability. The focus has been on mechanisms that
generate time-variation in risk premia, often related to the business cycle
given the evidence for cyclical variation in risk premia presented in Fama
and French (1989), and sometimes capturing acyclical variation that has been
highlighted in more recent research.
One class of models, following Merton (1980), has time-varying volatility
of aggregate stock returns and an equity premium that moves in proportion to either the standard deviation of returns (implying a constant Sharpe
ratio) or the variance of returns (implying a constant allocation to stocks in
a static portfolio choice model). A vast empirical literature shows that stock
market volatility moves over time. However, while there is some evidence
that the equity premium is positively related to equity volatility, it does not
seem to move in proportion with either the standard deviation or variance
of aggregate stock returns (Campbell, 1987; French, Schwert, & Stambaugh,
1987; Ghysels, Santa-Clara, & Valkanov, 2005; Harvey, 1989). Hence, the literature has sought to model time-variation in the reward that investors require
for bearing equity risk or equivalently time-variation in the volatility of the
SDF. This can be achieved, while maintaining the assumption that investors
have rational expectations, through time-variation in the volatility of aggregate consumption growth (as in the long-run risk literature following Kandel
Emerging Trends: Asset Pricing
7
& Stambaugh, 1991; Bansal & Yaron, 2004; and Hansen, Heaton, & Li, 2008;
or in the model of Martin, 2013), tail risk in the consumption distribution (as
in Gabaix, 2012 and Wachter, 2013), curvature of the utility function (as in
the habit formation model of Campbell & Cochrane, 1999), or uninsurable
idiosyncratic risk (as in Constantinides & Duffie, 1996; Mankiw, 1986; and
Storesletten, Telmer, & Yaron, 2008).
BEYOND RATIONAL EXPECTATIONS: BEHAVIORAL FINANCE
AND AMBIGUITY AVERSION
BEYOND RATIONAL EXPECTATIONS
Since the high tide of rational expectations macroeconomics in the early
1980s, economists have explored alternatives to the assumption that economic agents’ subjective expectations always equal objective expectations.
Asset pricing is a natural context in which to consider irrational expectations
because the theory of the stochastic discount factor tells us exactly how to
find subjective expectations that can rationalize asset prices without any risk
aversion on the part of investors. These so-called risk-neutral expectations
can be found as follows. Starting from the relation between asset prices and
∑
state prices, P(X) = q(s) X(s), multiply and divide by the sum of the state
∑
prices, q(s) = Pf = 1 / (1 + Rf ), to obtain
S
∑
1
1
P(X) =
? ∗ (s)X(s) =
E∗ [X],
1 + Rf s=1
1 + Rf
(6)
∑
where ?*(s) = q(s)/ q(s) is a “pseudo-probability” of state s. It has the necessary properties to be a probability, namely that it is positive and sums to one
across all possible states. However, it is derived only from state prices and
need not correspond to the objective probability of state s. Equation 6 says
that the asset price equals the pseudo-probability-weighted average payoff,
or equivalently the pseudo-expectation of the payoff, discounted at the
riskless interest rate. For this reason, ?*(s) is also known as a risk-neutral
probability.
If all investors have subjective probabilities ?*(s), then observed asset prices
can be reconciled with risk-neutral preferences. Put another way, any asset
pricing model that relies on utility curvature and rational expectations can
be matched by a model with linear utility and particular irrational expectations. A similar procedure can be used to reconcile asset prices with an
arbitrary risk-averse utility function. Thus, asset price data alone cannot tell
us whether investors have rational expectations; we need some external evidence on either preferences or expectations to resolve the issue.
8
EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES
BEHAVIORAL FINANCE
Shiller (1984) motivates an irrational expectations asset pricing model by
arguing that the true model of the economy is unknowable to economists
and investors alike. In this context, subjective views about future economic
prospects and asset payoffs spread among investors in a manner analogous
to the spread of infections in epidemiological models. Shiller writes down a
simple model of equilibrium in a stock market with both rational and irrational investors. The equity demand function of risk-averse rational investors
is linear in the expected return. Irrational investors demand an exogenous
value of shares; equivalently, their equity demand function has unit price
elasticity. In this model, stock prices can be written as a discounted present
value of future dividends and exogenous irrational investor demands. Irrational investors have a larger effect on stock prices when they are more persistent and when the risk-bearing capacity of rational investors is smaller.
The subsequent literature on behavioral finance addresses a number of
important questions about equilibrium with both rational and irrational
investors. First, what determines the asset demands of irrational investors?
Shiller (1984) modeled these demands as an exogenous stochastic process,
but it is appealing to derive them from assumptions about irrational
investors’ expectations and perhaps also their preferences. For example, Barberis, Shleifer, and Vishny (1998) model expectations as switching between
two false models in a way that generates both short-run momentum and
long-run mean reversion. Other behavioral research has emphasized overconfidence in private information (Daniel, Hirshleifer, & Subrahmanyam,
1998, 2001), direct utility from optimistic anticipation of the future (Bénabou,
2013; Brunnermeier & Parker, 2005), and time-varying risk aversion driven
by nonstandard prospect theory preferences (Barberis, Huang, & Santos,
2001; Benartzi & Thaler, 1995; Kahneman & Tversky, 1979; Thaler & Johnson,
1990). Shiller (1988) and Shiller and Pound (1989) used surveys around the
time of the 1987 stock market crash to understand investor belief formation,
and survey data have also been used by Barberis, Greenwood, Jin, and
Shleifer (2013), Froot and Frankel (1989), Froot (1989), and Greenwood and
Shleifer (2013) among others.
Second, what prevents rational investors at a point in time from arbitraging away the effects of irrational investors on asset prices? The most obvious
answer, and the one discussed by Shiller (1984), is that rational investors are
risk-averse. As rational investors trade with irrational investors, they take on
more or less stock market exposure and the covariance of their marginal utility with stock returns varies accordingly, justifying a time-varying expected
stock return. This logic implies that even within a behavioral model, the risk
assessments of rational investors remain relevant.
Emerging Trends: Asset Pricing
9
The behavioral literature explores other answers to this question. For
example, there may be short-sales constraints so that pessimistic rational
investors cannot offset the demands of optimistic irrational investors (Harrison & Kreps, 1978; Miller 1977; Scheinkman & Xiong, 2003). In addition,
rational investors may be financial intermediaries whose clients pull their
capital after losses have been incurred (Brunnermeier & Pedersen, 2009;
Shleifer & Vishny, 1997).
Third, why don’t rational investors become richer than irrational investors
over the long run, ultimately minimizing the price impacts of irrational
investors as conjectured by Friedman (1953) and Shiller (1984) answers
this objection by pointing out that wealthy rational investors eventually
die and leave their money to less rational descendants. DeLong, Shleifer,
Summers, and Waldmann (1990a, 1990b) argue that rational investors may
be more risk-averse than irrational investors, whose willingness to earn a
risk premium may outweigh their poor market timing and allow them to
accumulate wealth. Kogan, Ross, Wang, and Westerfield (2006) show that
irrational investors can have a significant impact on asset prices even when
their wealth is small relative to that of rational investors.
AMBIGUITY AVERSION
The view that investors do not know the true model of the economy,
which motivated Shiller’s (1984) development of behavioral finance, is
also the starting point for a theoretical literature on ambiguity aversion.
Building on the insights of Knight (1921) and the experimental evidence
of Ellsberg (1961), this literature argues that investors handle uncertainty
about models differently from uncertainty about outcomes within a model.
Within a model, outcomes can be described by a probability distribution, but
investors do not behave as if they have a subjective probability distribution
over alternative models and therefore cannot be described as Bayesians
in their response to model uncertainty or “ambiguity.” Instead, authors
such as Gilboa and Schmeidler (1989), Epstein and Wang (1994), Klibanoff,
Marinacci, and Mukerji (2005), and Hansen and Sargent (2008) have argued
that investors behave conservatively with respect to ambiguity, acting as if
a worst-reasonable-case model is true. Hansen and Sargent use an entropy
penalty to determine the worst reasonable model investors consider. Epstein
and Schneider (2010) offer a recent review.
The literature on ambiguity aversion blurs the distinctions between
positive and normative economics and between rational and irrational
decision-making. Conservative pessimism can be treated as a positive
prediction about investor behavior, but it can also be defended as a normatively justifiable (robust) response to model uncertainty. This contrasts with
10
EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES
the behavioral finance literature, in which some investors are regarded as
having beliefs or behaviors that do objective damage to their interests.
Both the literature on ambiguity and the behavioral finance literature
model asset prices using assumed deviations from rational expectations.
However, the literature on ambiguity assumes that investors are always
more pessimistic than a rational agent would be (although the degree of
pessimism may vary over time). Some behavioral finance models, such as
the model by Benartzi and Thaler (1995) of myopic loss aversion, are similar
in spirit, but others, such as the model by Brunnermeier and Parker (2005) of
anticipation utility, or Shiller’s (2000) discussion of “irrational exuberance,”
emphasize optimistic belief distortions.
THE CROSS-SECTION OF STOCK RETURNS
Since the early 1990s, the asset pricing literature has moved decisively
beyond the Capital Asset Pricing Model (CAPM), which explains the
cross-section of stock returns using the return on an aggregate stock index,
a proxy for the market portfolio of aggregate wealth. The modern literature
starts from the famous Fama–French three-factor model (Fama & French,
1993), which adds two new factors to the market factor, long-short portfolios
that go long value stocks with high book-market ratios and short growth
stocks with low book-market ratios (HML) and go long small stocks and
short large or “big” stocks (SMB). The Fama–French approach of adding
factors related to average stock returns has been an effective way to simplify
and unify the vast literature on the cross-section of stock returns (Cochrane,
2011).
Academics following Fama and French have suggested additional factors,
such as the momentum factor of Carhart (1997), and have asked why risk
factors such as HML and SMB should have nonzero prices. Why should
investors care about exposure to the common movements of value stocks
or small stocks? The rational asset pricing literature offers several answers.
First, the empirical proxy for the market portfolio used in standard tests
of the CAPM may omit components of wealth, most importantly human
capital (Fama & French, 1996). Second, a single-factor model conditional
on the information of investors may imply a multifactor unconditional
model, a point first made by Hansen and Richard (1987). Campbell and
Cochrane (1999) and Lettau and Ludvigson (2001b) propose conditional
consumption-based models, whereas Lewellen and Nagel (2006) and Roussanov (2014) argue that neither the conditional CAPM nor a conditional
consumption CAPM can explain the cross-section of stock returns. Third,
the intertemporal model of Merton (1973) implies that investors care not
only about shocks to wealth but also about shocks to the rates of return that
Emerging Trends: Asset Pricing
11
can be earned when wealth is reinvested. Campbell and Vuolteenaho (2004)
and Lettau and Wachter (2007) have argued that growth stocks are good
hedges against declines in expected stock returns, and Campbell, Giglio,
Polk, and Turley (2013) have argued that they also hedge against increases in
stock return volatility. Fourth, researchers working with consumption-based
asset pricing models have argued that value stocks may covary with future
consumption growth in a way that is relevant for investors with Epstein–Zin
(Epstein & Zin, 1989) preferences (Hansen, Heaton, & Li, 2008; Parker &
Julliard, 2005), and with the stock of durable goods in a way that is relevant
for investors with nonseparable preferences over durables and nondurables
(Yogo, 2006).
In parallel with the rational asset pricing literature, the behavioral finance
literature has also explored asset pricing patterns within the cross-section of
asset returns. There is a particularly active debate over the rationality or otherwise of the value premium, the anomalously high returns to value stocks,
and the related phenomenon of long-run mean-reversion in individual stock
returns highlighted by De Bondt and Thaler (1985). Momentum, the tendency of high returns over the past year (excluding the past 1–3 months)
to predict high future returns, is also a favorite target for behavioral modeling and is much more difficult for rational models to explain. Selected papers
from this literature include Baker and Wurgler (2006), Chan, Jegadeesh, and
Lakonishok (1996), Hong, Lim, and Stein (2000), Hong and Stein (1999), and
LaPorta, Lakonishok, Shleifer, and Vishny (1997).
FUTURE DIRECTIONS
Empirical economists working in asset pricing, including the 2013 Nobel laureates and their students, have documented a rich variety of facts about asset
prices. For example, news events typically move asset prices in a manner
that scales appropriately with the fundamental impacts of the events, allowing asset prices to be used to indirectly measure such fundamental impacts.
Asset prices sometimes drift in the aftermath of events, most famously corporate earnings announcements, but these drifts typically weaken over time
as arbitrageurs exploit them. Aggregate fluctuations in asset prices appear
to reflect variation in discount rates, and specifically risk premia, so that valuation ratios can be used to predict returns. Time-varying risk premia do
not move in proportion with return volatility, and they tend to be countercyclical, rising when the economy deteriorates. In the cross-section of stock
returns, extremely modest predictability in the returns of individual stocks
can be amplified by sorting stocks with similar characteristics into portfolios. Portfolios of value stocks and momentum stocks, to take the two most
12
EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES
famous examples, have high average returns. High returns to these characteristics are found in other asset classes as well, and rewards for asset-class
risk exposures are typically higher when the risk is taken by leveraging an
index than when it is taken by buying individual assets with high betas to the
index. These facts are reviewed and summarized in papers such as Asness,
Moskowitz, and Pedersen (2013) and Cochrane (2011), academic books such
as Campbell, Lo, and MacKinlay (1997) and Cochrane (2005), and trade books
such as Ilmanen (2011). Taken together, they make asset pricing one of the
most successful empirical fields in economics.
Emerging trends in the field involve modeling the interaction of heterogeneous investors who may have different preferences, beliefs, and constraints
on their financial positions. Economists need to understand the markets from
the perspectives of these different investors, including rational investors who
likely have time-varying risk exposures. Direct measurement of investors’
financial holdings, ideally linked to survey data about their risk preferences
and beliefs, will be important to make further progress.
REFERENCES
Alvarez, F., & Jermann, U. (2005). Using asset prices to measure the persistence of
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JOHN Y. CAMPBELL SHORT BIOGRAPHY
John Y. Campbell is the Morton L. and Carole S. Olshan Professor of
Economics at Harvard University. He is a Research Associate and former
Director of the Program in Asset Pricing at the National Bureau of Economic
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