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The Ostrich Effect: Selective Attention to Information

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We develop and test a model which links information acquisition decisions to the hedonic utility of information. Acquiring and attending to information increases the psychological impact of information (an impact effect), increases the speed of adjustment for a utility reference-point (a reference-point updating effect), and affects the degree of risk aversion towards randomness in news (a risk aversion effect). Given plausible parameter values, the model predicts asymmetric preferences for the timing of resolution of uncertainty: Individuals should monitor and attend to information more actively given preliminary good news but “put their heads in the sand” by avoiding additional information given adverse prior news. We test for such an “ostrich effect” in a finance context, examining the account monitoring behavior of Scandinavian and American investors in two datasets. In both datasets, investors monitor their portfolios more frequently in rising markets than when markets are flat or falling.
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The ostrich effect: Selective attention to information
Niklas Karlsson & George Loewenstein &
Duane Seppi
Published online: 11 February 2009
#
Springer Science + Business Media, LLC 2009
Abstract We develop and test a model which links information acquisition
decisions to the hedonic utility of information. Acquiring and attending to
information increases the psychological impact of information (an impact effect),
increases the speed of adjustment for a utility reference-point (a reference-point
updating effect), and affects the degree of risk aversion towards randomness in news
(a risk aversion effect). Given plausible parameter values, the model predicts
asymmetric preferences for the timing of resolution of uncertainty: Individuals
should monitor and attend to information more actively given preliminary good
news but put their heads in the sand by avoiding additional information given
adverse prior news. We test for such an ostrich effect in a finance context,
examining the account monitoring behavior of Scandinavian and American investors
in two datasets. In both datasets, investors monitor thei r portfolios more frequently
in rising markets than when markets are flat or falling.
Keywords Selective exposure
.
Attention
.
Investor behavior
JEL D81
.
D83
The observation that people derive utility from information and beliefs, though once
heretical in economics, is now commonplace and relatively uncontroversial. A
novel, and potentially more controversial, ramification of the idea that people derive
utility directly from beliefs is, however, that they may have an incentive to control or
J Risk Uncertain (2009) 38:95115
DOI 10.1007/s11166-009-9060-6
N. Karlsson
Health Economics and Outcomes Research, AstraZeneca, SE-43183 Mölndal, Sweden
e-mail: Niklas.x.Karlsson@astrazeneca.com
G. Loewenstein (*)
Department of Social & Decision Sciences, Carnegie Mellon University, Pittsburgh, PA 15213, USA
e-mail: gl20@andrew.cmu.edu
D. Seppi
Tepper School of Business, Carnegie Mellon University, Pittsburgh, PA 15213, USA
e-mail: ds64@andrew.cmu.edu
regulate those beliefs. One specific way in which individuals can contr ol their beliefs
is via decisions about whether or not to acquire information. We argue that
information acquisition decisions are l ikely to be linked with the internal
psychological processing of information and the hedonic impact of information on
utility.
An extensive body of empirical research in psychology supports the idea that
people have some capacity to attend to or not to attend toi.e., ignore
information. This is sometimes called the selective exposure hypothesis. Selective
exposure has made its way into economics. Caplin (2003), building on earlier ideas
in Witte (1992), develops a model in which people respond to health warnings either
by adopting behaviors consistent with those beliefs, or, if the warnings are too
threatening, by willfully ignoring them. We take Caplins analysis a step further by
examining the degree to which people choose to expose themselves differentially to
additional information after conditioning on prior positive and negative news.
We develop a model of selective a ttention in which individuals rec eive
preliminary but incomplete information and then decide whet her to acquire and
attend to definitive information. The intuition is that individua ls regulate the impact
of good and bad news on their utility by how intently they attend to the news. If
knowing definitively that an outcome is negative is worse than merely suspecting it
is likely to be bad, then people may try to shield themselves from receiving
definitive information when they suspect the news may be adverse. For reasonable
parameter values, our model predicts that people will exhibit an ostrich effecta
term coined by Galai and Sade (2003). They defined the ostrich effect as avoiding
apparently risky financial situations by pretending they do not exist. We use the
term in a related, but expanded sense, as avoiding exposing oneself to information
that one fears will cause psychological discomfort. Given preliminary bad newsor,
as it turns out in our model, ambiguous newspeople may optimally choose to
avoid collecting additional information: They put their heads in the sand to shield
themselves from further news.
1
In contrast, given favorable news, individuals seek
out definitive information.
The exposition of our model and the associated empirical work are in a finance
context. When the aggregate market is down, investors may reasonably forecast that
their personal portfolios are likely to have declined in value, but it is still possible
that the specific stocks they own may have risen even when the overall market has
declined. The ostrich effect predicts that investor accoun t monitoring decisions may,
therefore, be asymmetric in up and down markets. Our empirical work finds support
for such an asymmetry in information monitoring using Scandinavian and American
data on investor logins to personal portfolio accounts.
Our intuitions about the psychology of information and the possibility of an
ostrich effect in information acquisition decisions, altho ugh tested in a financial
context, have far broader applications. They can apply, for example, to peoples
decisions about when to seek formal medical diagnoses for worrisome health
1
The idea that ostriches hide their head in the sand is a myth. According to the Canadian Museum of
Nature (http://www.nature.ca/notebooks/english/ostrich.htm): If threatened while sitting on the nest,
which is simply a cavity scooped in the earth, the hen presses her long neck flat along the ground,
blending with the background. Ostriches, contrary to popular belief, do not bury their heads in the sand.
96 J Risk Uncertain (2009) 38:95115
symptoms, to when parents will seek testing for a child who is having trouble in
school, to an academics decision of when and whether to pursue doubts about the
integrity of a student, or to a business executives decision to investigatei.e.,
perform due diligencewhen there are warning signs relating to a prospective deal.
In particular, the ostrich effect predicts that people may delay acquiring information,
even when doing so degrades the quality of decision making, if knowing the
information forces them to confront and internalize possible disappointments they
would mentally prefer to avoid.
The paper is organized as follows. Section 1 briefly reviews the related literature.
In Section 2 we present a model of selective attention that predicts an asymmetry in
the attention paid to bad or ambiguous news compared to good news. Section 3
validates this predicted asymmetry using data on Scandinavian and American
investors decisions to check the value of their portfolios on-line. Consistent with the
predictions of our model, investors check their portfolio value less frequently in
falling or flat markets than in rising markets. Section 4 considers alternative
explanations of the observed ostrich effect. In Sections 5 and 6 we discuss additional
implications of the ostr ich effect and the rationality of selective attention. Section 7
concludes.
1 Related literature
Economic models commonly assume that information affects utility indirectly as an
input in decision making. Recent economic models also incorporate information and
beliefs directly in utility via anticipation (Köszegi and Rabin 2007; Caplin and Leahy
2001;Loewenstein1987), self-image or ego (Benabou and Tirole 2006;Bodnerand
Prelec 2001;Köszegi1999), and recursive preferences that depend on beliefs about
future utility (Epstein and Zin 1989; Kreps and Porteus 1978). Incorporating beliefs
into the utility function has ramifications for time discounting, the effective level of
risk-aversion, and preferences about the timing of the resolution of uncertainty.
2
The insight that people derive utility from information has also enriched finance.
Traditional finance theory assumes that investors only derive utility from their assets
at the time they liquidate and consume theme.g., upon retirementbut people
clearly derive pleasure and pain directly from changes in their wealth prior to
consuming the underlying cash flows. Barberis, Huang, and Santos (2001) show that
a model in which investor utility depends directly on the value of their financial
wealth can explain the equity premium puzzle as well as the low correlation between
stock market returns and consumption growth (for earlier treatments, see Gneezy and
Potter 1997; Benartzi and Thaler 1995).
Research in psychology bolsters the work in eco nomics by showing that people
who hold optimistic beliefs about the future and positive views of themselves are
happier (Scheier, Carver and Bridges 2001; Diener and Diener 1995) and healthier
(Baumeister, Campbell, Krueger and Vohs 2003; Peterson and Bossio 2001), if not
necessarily wiser (Alloy and Abramson 1979). There is also ample evidence from
2
Benabou and Tirole (2006); Bodner and Prelec (2001); Geanakoplos, Pearce and Stacchetti (1989); and
Rabin (1993) provide other examples of how information-dependent utility changes peoples behavior.
J Risk Uncertain (2009) 38:95115 97
psychology that desires exert a powerful influence on beliefs, a phenomenon that
psychologists call motivated reasoning (Kruglanski 1996; Babad 1995; Babad and
Katz 1991; Kunda 1990). Economists, too, have been interested in motivated
formation of beliefs, but have focused more on modeling the phenomenon than on
studying it empirically.
3
Empirical support for the selective exposure hypothesis can be found in diverse
research conducted by psychologists. Ehrilch, Guttman, Schonbach and Mills (1957)
found that new car owners pay more attention to advertisements for the model they
purchased than for models they had considered but did not buy. Brock and Balloun
(1967) observed that smokers attend more to pro-smoking messages and that non-
smokers attended more to anti-smoking messages. Although some studies have
equivocal findings (Cotton 1985; Festinger 1964; Freedman and Sears 1965), the most
recent research provides quite strong support for the selective exposure hypothesis
(e.g., Jonas, Schulz-Hardt, Frey and Thelen 2001; Frey and Stahlberg 1986).
While not focusing specifically on selective exposure, research in behavioral
finance, like that of psychologists, highlights the importance of attention for investor
behavior. DellaVigna and Pollet (2005), for example, show that earnings announce -
ments have a more gradual impact on stock prices when they occur on a Friday
(when investors are likely to be inattentive) than when they occur on other days of
the week. Barber and Odean (2008) predict and find that individual investors, as
compared with institutional investors, tend to be net buyers of attention-grabbing
stockse.g., those that receive special news coverage.
2 Model of selective attention
We propose a stylized decision-theoretic model to de velop predictions about
selective attention. The model applies generically to situations in which an
individual derives utility from information and has some control over the timing
of information acquisition. For purposes of exposition, we focus on a financial
application in which an investor decides the timing of information she receives about
her portfolio. The investor decides whether or not to acquire and attend to
information about her wealth, conditional on prior public information.
Our definition of attention encompasses both external behavior and internal
psychological processes. The most obvious external manifestation of attention is actively
seeking additional information. In the context of investing, a natural first step when
acquiring additional information is to check the current value of ones portfolio. This is
psychologically important because having definitive information about a portfoliosexact
3
In Akerlof and Dickens (1982), workers in dangerous work environments downplay the severity of
unavoidable risks. In Köszegi (1999), Bodner and Prelec (2001), and Benabou and Tirole (2006), people
take actions to persuade themselves, as well as others, that they have desirable personal characteristics that
they may not have. In Benabou and Tirole (2002), people exaggerate their likelihood of succeeding at a
task to counteract inertia-inducing effects of hyperbolic time discounting. In Brunnermeier and Parker
(2002) and Loewenstein (1985, chapter 3), agents maximize total well-being by balancing the benefits of
holding optimistic beliefs and the costs of basing actions on distorted expectations. In Rabin and Shrag
(1999), people interpret evidence in a biased fashion that responds more strongly to information consistent
with what they are motivated to believe.
98 J Risk Uncertain (2009) 38:95115
value is likely to be more salient than having an estimate of its value (with estimation
error) based on public index returns (if the portfolio is not fully indexed).
Our model allows for three pathways through which attention can affect utility.
The first is an impact effect. Previous research suggests that the impact of news on
utility depends not just on the objective content of the information (i.e., good or bad)
but also on psychological and context factors. For example, people appear to derive
greater utility from positive outcomes, and greater disutility from negative outcomes,
when they feel personally responsible for the outcomes (Kahneman and Tversky
1982; Shefrin and Statman 1984; Loewenstein and Issacharoff 1994), when the
outcomes are unexpected (Kahneman and Miller 1986; Loomes and Sugden 1986;
Mellers et al. 1997; Delquié and Cillo 2006), and when the outcomes are not traded
in markets, as is true of health (Horowitz and McConnell 2 002). We add attention to
the short list of facto rs that influence the steepness of the utility function. We posit,
hopefully uncontroversially, that definitive knowledge has a greater psychological
impact on utility than simply suspecting something (in an expected value sense).
The second consequence of attention is a reference point updating effect. Prospect
theory and models of loss aversion and habit formation all posit that utility depends
on how outcomes deviate from pre-specified reference points (see Zeelenberg et al.
2000, Gul 1991; Constantinides 1990; Loomes and Sugden 1986; Bell 1985). Our
model recognizes that attention and information acquisition can affect the dynamics
of future reference points. We assume that attention to definitive information
accelerates the updating of ones reference point. In contrast, inattention causes the
reference point to adjust more slowly. This is consistent with empirical evidence that
reference points are less responsive to probabilistic than to deterministic information
(see Loewenstein and Adler 1995 ).
The third consequence of attention is a risk aversion effect. Risk aversion and
prospect theory both suggest that negative departures from a reference point have a
greater negative impact on utility than the positive impact of positive departures
(Kahneman and Tversky 1982; Bro oks a nd Zan k 200 5). The intensity of
informational risk (or loss) aversion depends, in principle, on where the probability
distribution of informational shocks is located along the utility function. Since
attention moves the location of the reference point around which the utility function
is centered, this can affect the relevant utility curvature over time. The resulting
predictable intertemporal variation in risk aversion can affect the preferred timing of
when individuals want to learn information and be exposed to the associated
informational risk.
By linking discretionary information acquisition with the hedonic impact of attention
on utility , we implicitly assume certain inherent constraints on investor psychology. For
example, investors cannot distract themselves from bad news (or celebrate good news)
independently of how much they pay attention to news. Given these linkages, selective
attention is consistent with investor rationality if it maximizes utility subject to the
operative psychological impact, updating and risk aversion effects.
Consider the decision problem of an investor who potentially receives
information at two points in time, t=1 and 2, about the past realized return r ¼
r
p
þ r
d
on her portfolio over some prior holding period. The inves tor learns the first
component r
p
automatically at date 1 but can decide whether to learn the second
component r
d
either at date 1 or 2. We call r
p
pr eliminary information. In an investments
J Risk Uncertain (2009) 38:95115 99
context, this might be an investors estimated return based on a market index that is
widely reported in the public news media (e.g., the Dow). We call the second
component, r
d
, discretionary information. This could be the investorspersonal
idiosyncratic return given the specific holdings in her portfolio. Information about r
d
is discretionary in the sense that it cannot be inferred from the public index return, but
must be actively sought out. We assume that, conditional on the preliminary information
r
p
, the expected value of the discretionary part, Er
d
r
p

, is mean zero. In other words,
the investors ex ante expectations about r
d
are rational.
There is no direct cost to the investor if she chooses to learn r
d
at date 1. Investors
in our empirical data, for example, can log on to a web page and review their
account balances at no cost except for a trivial amount of time. However, the
investor does have the option of burying her head in the sand at date 1 by delaying
learning r
d
until date 2. This is the ostrich effect. We use an indicator A
1
=1 to denote
attention and A
1
=0 to de note inat tention at date 1.
A key assumption is that the investo r can condition her de cision at date 1 about whe ther
to pay attention to her total return r after first learning the preliminary component r
p
.
Given her decision, her perceived return r
*
1
at date 1 is either r
*
1
¼ r ¼ r
p
þ r
d
(if she is
attentive) or r
*
1
¼ E rr
p

¼r
p
(if she is inattentive). At date 2, the investor automatically
learns any remaining information. This is not a choice. The investor can only decide the
timing of when she learns r
d
, not her final knowledge at date 2.
We model the investor as having preferences over information about her return. At date
1, her utility is a function 1 þ a A
1
ðÞðÞur
*
1
b
0

of the deviation of her perceived return
r
*
1
from a previously determined benchmark reference point b
0
.Atdate2,weassume
her informational utility is just u(r b
1
). We assume the function u( ) is increasing,
concave, and continuous in perceived performance relative to the benchmark. We
normalize utility so that u(0)=0. In the special case in which the investor is loss
averse, there is a kink at 0. Otherwise, u is twice continuously differentiable.
Attention affects both the current impact of information on utility at date 1 and the
dynamics of the future benchmark at date 2. The term α(A
1
) denotes a boost in the
utility impact when the investor actively attends to information, a A
1
¼ 1ðÞ¼a 0,
relative to when she is inattentive, a A
1
¼ 0ðÞ¼0. For simplicity, we assume the
initial benchmark at date 1 is b
0
=0. The reference point at date 2 depends on both
the investors prior perceived return and on how attentive she was at date 1:
b
1
ðr
*
1
; A
1
Þ¼
r
p
þ r
d
ð1 qÞr
p
þ qb
0
¼ð1 qÞr
p
if A
1
¼ 1
if A
1
¼ 0
ð1Þ
The param eter θ, where 0 q 1, represents the reference-point updating effect.
It allows the reference point to respond more slowly to changes in wealth when the
investor is inattentive. A higher value of θ thus means that an inattentive investor
updates her reference point more slowly.
The investor decides whether to be attentive at date 1 so as to maximize the
cumulative utility from the flow of information about her return over dates 1 and 2.
max
A
1
(
0;1
fg
JA
1
; r
p

¼ E 1 þ a A
1
ðÞðÞur
*
1
b
0

þ ur b
1
r
*
1
; A
1

r
p

ð2Þ
In particular, this means choosing whether to learn the discretionary (investor-
specific) return r
d
at time 1 (by being attentive) or to wait until time 2 (by being
100 J Risk Uncertain (2009) 38:95115
inattentive at time 1) and accepting the psychological consequences for her date 1
marginal utility and the reference point dynamics that accompany this decision. The
investor conditions her decision on whatever she automatically learns at time 1 about
the preliminary (public) information r
p
about her return.
If full information about the past return r is useful in making future investment
decisions, this simply biases the investors decision towards information acquisition
and attention. We consider this aspect of the problem more fully after first analyzing
the purely psychological consequences of attention.
Given a preliminary news realization r
p
=p, the expected informational utility
from attending is 1þaðÞE upþ r
d
ðÞ½þu 0ðÞ. The expected utility from being p assive
and not attending is upðÞþE u qp þ r
d
ðÞ½. Comparing the two expected utilities
gives the differential utility from attention conditional on the preliminary news p
ΔJpðÞ¼JA
1
¼ 1; pðÞJA
1
¼ 0; pðÞ
¼ 1 þ aðÞE upþ r
d
ðÞ½þu 0ðÞupðÞþE u qp þ r
d
ðÞ½½
ð3Þ
The concavity of u and the fact that the random return r
d
is mean-zero implies that
E upþ r
d
ðÞ½< upðÞand u 0ðÞ> E ur
d
ðÞ½. Thus, the optimal decision depends on
two considerations: First, there is a trade-off between the impact boost to utility α
from attending at date 1 given that the expected deviation is Er
j
r
p

b
0
¼ p versus
the additional expected utility experienced at date 2 given incomplet e reference point
updating following inattention at date 1. Second, predictable differences in the
investors risk/loss aversion towards a random deviation r b
0
with an expected
value p at date 1 (in the case of attention) versus a random deviation r b
1
centered
at θp at date 2 (in the case of inattention) can create incentives to shift the
informational risk from learning r
d
between the two dates. In combination, these two
effects lead to the following result.
Proposition 1 Attention to positive news p>0 (and inattention to negative news p<0)
is optimal at date 1 given a sufficiently large impact effect α>0, sufficiently rapid
inattentive reference point updating (i.e., θ close enough to 0), and utility curvature
that is not too large and that decreases sufficiently quickly in expected wealth.
Proof If SD(r
d
)=0, then ΔJ(p) given p>0 is increasing in α and decreasing in θ.As
long as the SD(r
d
)>0 is not too large given the curvature in u so that E upþ r
d
ðÞ½>
0 when p>0, the differential ΔJ(p) is again increasing in α and decreasing in θ.
Greater curvature in u around the expected value θp at date 2 than around the
expected value p>0 at date 1 increases ΔJ(p). The arguments when p<0 are similar
except that Δ(p) will be decreasing in α and increasing in θ.
To see the curvature intuition more explicitly, suppose there is no kink at 0 so that
u is everywhere twice conti nuously differentiable (i.e., risk aversion but no loss
aversion). Using Taylor representations for E upþ r
d
ðÞ½, u(p), and E u qp þ r
d
ðÞ½we
can rewrite the utility differential as
ΔJpðÞ¼u 0ðÞa qðÞp þ 1
=
2E 1þaðÞu¶¶ x
1
ðÞu¶¶ x
2
ðÞu¶¶ x
3
ðÞq
2

p
2
þ1
=
2E 1þaðÞu¶¶ x
1
ðÞu¶¶ x
3
ðÞ½r
2
d

ð4Þ
J Risk Uncertain (2009) 38:95115 101
where x
1
¼ x
1
r
p

e 0; p þ r
d
½, x
2
¼ x
2
r
p

e 0; p½, and x
3
¼ x
3
r
p

e 0; qp þ r
d
½
are functions giving the residual coefficients for the exact second-order Taylor
representations of E upþ r
d
ðÞ½, u(p), and E ur
d
þ qpðÞ½for each possible realization
of r
d
. The first term on the RHS of Eq. (4) captures the direct trade-off between the
impact and updating effects. The second term captures the effect of differential
curvature on the utility experienced from the preliminary news p at date 1 (given
attention) and at dates 1 and 2 (given inattention). The third term of Eq. (4) reflects
differential risk aversion towards bearing the informational risk associated with the
news r
d
given the utility functions curvature around an expected deviation E r
j
r
p

b
0
¼ p at date 1 (given attention) versus the risk when the deviation is centered at
E r
j
r
p

b
1
¼ qp at date 2 (given inattention).
A few special cases convey some intuition for the preference parameter
combinations that lead to ostrich effect behavi or. First, consider the case of risk
neutrality. Since all of the us in Eq. (4) are 0, the optimal attention decision is
driven solely by the sign o f α θ (i.e., on whether the impact or delayed updating
effect is dominant) and the sign of p.Ifα θ >0, then attention is optimal given good
preliminary news, p>0, and inattent ion is optimal given bad news, p<0. This is an
example of the ostrich effect. If, however, α θ <0, we would have an anti-ostrich
effect with the opposite decisions. To see the differential risk aversion effect as it
relates to the third term in Eq. (4), consider the special case of neutral preliminary
news, p=0. In this case, x
1
=x
3
so that ΔJ 0ðÞ¼1
=
2E au¶¶ x
1
ðÞr
d
2
½< 0. Inattention is
unambiguously optimal in flat markets given any impact effect α >0 and any
concavity u
00
< 0.
4
The reason is that the impact effect magnifies the curvature at
date 1, thereby making it preferable to defer learning r
d
until date 2 when the
investor is less sensitive to news.
Thus, ostrich effect behavior is optimal if the imp act effect α is large relative to
the delayed updating effect θ and if the curvature of the investors utility function u
is sufficiently decreasing in the realized deviation (e.g., if u(x) is a small enough
negative number as x increases). It is the asymmetry of the preference for the timing
of resolution of uncertainty conditional on past information which distinguishes the
ostrich effect and our theory of selective attention from the timing preferences in
recursive utility models (see Epstein and Zin 1989.; Kreps and Porteus 1978). Of
course, whether investor preferences about the timing of the resolution of uncertainty
in the real world are asymmetric is an empirical question. We document this
asymmetry empirically in the next section.
The model is admittedly stylized in its assumptions of only two periods, no time
discounting, and that the investor automatically learns all remaining information in
the second period rather than being able to defer information acquisition for an
extended period of time. However, the basic intuitions for how attention affects the
4
Our intuition a priori is that investors are more likely to monitor their portfolios when the market is
neutral than when it is sharply down. As will be evident in the following section, the data provide mixed
support for this prediction. The prior analysis may seem to be at odds with this intuition since the model
says that people will never monitor their portfolios when the market is flat but that, for some parameter
values, investors will monitor when the market news is negative. However, the magnitude of the
disincentive for attention can be greater when the market is down than when it is flat if α is large and θ is
close to 0.
102 J Risk Uncertain (2009) 38:95115
information acquisition decision are likely to be robust. For example, if investors
discount future utility, this only increases the attractiveness of attention to positive
news at date 1 and deferring attention to negative news to date 2. Moreover,
introducing an additional mean-zero shock to returns between dates 1 and 2
complicates the differential risk aversion effect but does not alter the models basic
predictions.
5
Other motives for information In the standard economic model of information
acquisition, investors have an indirect demand for information as an input into their
trading decisions. They need to know thei r current finan cial situation in order to
make informed decisions about whether to trade. Our ana lysis easily accommodates
an indirect demand for information. Let vA
1
¼ 1; rðÞ0 denote the expected
option value of potential trades that the investor may identify at time 1 provided
that she is actively attending to her portfolio. The investor then compares the
combined direct and indirect expected utility from being informed, JA
1
¼ 1; r
p

þ
E vA
1
¼ 1 ; rðÞr
p

, with the expect ed utility from being less informed and forgoing
any potential trading opportunities, JA
1
¼ 0 ; r
p

when deciding whether to monitor
her portfolio at date 1.
If investors only have a transactional demand for information then, since
EvA
1
¼ 1; rðÞr
p

0, it is plausible to conjecture that they are equally likely
to collect information in up markets as in down markets. Moreover, the potential
trading benefit from information is likely to increase as market conditions
become more extreme in either direction. If so, investors should monitor their
portfolios most actively following extreme up-markets and also following
extreme market downturns. For investors not to attend to their portfolios, there
must be some cost to attention. A direct hedonic disutility from negative
information endogenously provides such a cost.
Empirical hypothesis The empirical tests for ostrich effect behavior in Section 3 use
data about information monitoring decisions for cross-sections of investors. In doing
so, we interpret the ostrich effect to mean that investors are simply less likely to
check their portfolios in down and flat mark ets than in up mark ets; not that no
investor will check. The possibility of an indirect demand for information as an input
to trading justifies this interpretation. Whether investors attend to their financial
situation in down and flat markets depends on the relative magnitude of the disutility
of attending to bad and neutral news and the positive option value of trading. If
investors are heterogeneous, then some may attend while others may not. In up
markets, however, investors will have a stronger incentive to attend both because of
the direct utility from good news and also because of the option value of possible
trades.
5
If r
2
is an independently distributed, mean-zero shock that is realized and automatically learned at date 2,
then the attention differential is ΔJpðÞ¼1 þ aðÞE upþ r
d
ðÞ½þE ur
2
ðÞ½upðÞþE u qp þ r
d
þ r
2
ðÞ½½.
In this case,
E upþ r
d
ðÞ½< upðÞand E ur
2
ðÞ½> E ur
d
þ r
2
ðÞ½where the later inequality follows
because r
2
second-order stochastic dominates r
d
+ r
2
.
J Risk Uncertain (2009) 38:95115 103
3 Empirical investigation of the ostrich effect
If investors exhibit ostrich effect behavior, they will monitor their portfolios more
frequently when the aggregate stock market is up than when it is down. We test for
this asymmetry in two different data sets, one from Sweden and the other from the US,
each containing data about investor decisions to monitor the value of their personal
portfolios on-line. Table 1 presents some basic information about these data sets.
The first data set is from the Swedish Premium Pension Authority. Beginning in
2000, the Swedish premium pension system allows Swedish citizens to choose how
to invest 2.5% of their before-tax income in equity and interest-bearing funds as part
of their state pension. By 2004, 5.3 million of Swedens 9 million citizens were in
this new pension system. Our data include the total number of people who logged in
to check the value of their portfolio on each day between January 7, 2002 and
October 13, 2004. In addition, the data include the number of reallocations
(transactions) made to investor portfolios each day (either on the web or through an
automatic telephone service). The average number of logins each day is 10,903. Of
these, 1,142 involved changes to investment allocations. Since people only log in
either to check the value of their premium pension portfolio or to reallocat e their
portfolio holdings, we can use the number of account logins less the number of
portfolio reallocations (on the web or via an automatic telephone service) to measure
the daily number of informational account look-ups.
The second data set is from the Vanguard Group. The data give the daily number
of times Vanguard clients accessed their Vanguard accounts on-line between January
2, 2006 and June 30, 2008. In 2007, approximately 21 million investors had
accounts at Vanguard. Since the Vanguard data do not include the number of
transactions, we use aggregate S&P 500 trading volume as a proxy to control in our
regression analysis for transactional, as opposed to informational, logins.
We follow the same estimation strategy for both datasets: We regress the daily
number of account LOOKUPS
t
(or LOGINS
t
) on several control variables and on an
averaged prior log return, RETURN
t
, computed as ln(INDEX
t
/LAGAVERAGE
t
)
Table 1 Descriptive statistics
Swedish Premium Pension Authority Vanguard
Sample period Jan. 7, 2002Oct. 13, 2004 Jan. 2, 2006June 30, 2008
Mean SD Mean SD
LOGINS
t
10,903 7,055 416,916 81,229
Number of transactions per day 1,142 878 na na
LOOKUPS
t
9,761 6,392 na na
Closing index level 184 28.87 1,387 88
RETURN
t
0.0004 0.0203 0.00034 0.011
VOLUME
t
(in billions) na na 3.074 0.933
LOOKUPS
t
in the Swedish pension data are the daily number of account LOGINS
t
less the daily number
of portfolio rebalancings. The Swedish stock index is the Stockholm All Shares (OMXSPI) index. The US
index is the S&P 500. RETURN
t
is the averaged prior return defined as the log change in the index relative
to the average index level over the previous 4 days. VOLUME
t
in the US data is the S&P 500 trading
volume
104 J Risk Uncertain (2009) 38:95115
where INDEX
t
is the index level on day t and LAGAVERAGE
t
is the average of
lagged index levels from day t-4 through day t-1. Our central prediction is that the
coefficient on RETURN
t
should be positive if investors exhibit the ostrich effect.
That is to say, more investors should check the value of their portfolio in up markets
than in down markets. We also allow for other factors that may affect account
monitoring activity. In all specifications, we control for day-of-the-week effects via
daily dummy variables (DAY
i,t
). In some specifications we include a linear time trend
and in others we include the lagged number of look-ups (or logins) from the prior
day. Finally, we include the number of transactions, TRANSACTIONS
t
, (in the
Swedish regressions) and the aggregate market volume, VOLUME
t
, (in the Vanguard
regressions) to distinguish the ostrich effect from a transactional demand for account
information. For example, people may transact more when the market is up than
when it is down (i.e., as predicted by the disposition effect) and may check the value
of their portfolio as an input into trading.
Results for Swedish Premium Pension Authority data Figure 1 plots the standardized
daily level of the Stockholm All Shares stock index (OMXSPI) and the daily number of
investor non-transactional account look-ups after controlling for day-of-the-week effects,
trends, and also (for look-ups) the number of transactions.
6
As can be seen, the number of
look-ups is generally higher when the OMXSPI index is higher , and vice versa.
Strictly speaking, however, the ostrich effect makes predictions about account
look-ups and prior changes in the market index, not the level of the index per se.
Figure 2 shows daily changes in the number of account look-ups for each of seven
quantiles (heptiles) based on the corresponding prior averaged returns for the
OMXSPI index. Both the look-up changes and prior returns are again residuals from
regressions controlling for day-of-the-week effects, a time trend, and (for the look-
ups) the number of transactions. Here the positive relation is even more apparent.
Clearly the number of look-ups is substantially higher following good news that the
market index increased and lower after bad news.
To examine the specific impact of prior index returns on account look-ups, we
estimate two regressions. The first column of Table 2 regresses account look-ups on
the prior OMXSPI returns with day-of-the-week dummy variables, a linear time
trend, and the daily number of portfolio reallocations (transactions) as additional
control variables. The second column presents a parallel regression in which the
trend variable is replaced with the one-day lagged look-u ps.
In both regressions, the number of account look-ups is increasing in the prior
index change. An additional 1% increase (i.e., 0.01) in RETURN
t
leads to 120 to 140
additional informational look-ups (i.e., just over 1% of the mean number of daily
look-ups). The RETURN
t
coefficient is significant at the 10% level (in the trend
specification) and at the 1% level (in the lagged variable specificat ion). Although not
reported in the table, our regression resul ts are robust, and sometimes even stronger,
in alternative specifications using simple lagged retur ns over 1-day and 5-day
6
Although LOOKUPS
t
is defined as the number of logins less the number of portfolio rebalancing
transactions, there may be look-ups motivated by a potential interest in trading which did not ultimately
result in trades.
J Risk Uncertain (2009) 38:95115 105
horizons in place of the RETURN
t
variable with its averaged denominator. Thus, the
Swedish investor data set strongly supports the ostrich effect.
The control variables are all statistically significant. In p articular, the positive
coefficient on contemporaneous TRANSACTIONS
t
is consistent with a transactional
demand for account information. Time trends and positive autocorrelation in look-
ups ar e also important. The Durbi n Watson statistics indicate some residual
autocorrelation in the time trend specification which argues for the lagged look-up
specification. The R
2
s indicate that our model explains a substantial part of daily
variation in investors portfolio monitoring decisions.
Results for Vanguard data Figures 3 and 4 are time series and heptile plots for daily
Vanguard logins and the S&P 500. The positive relation between the number of logins
and the aggregate market is even stronger than in the Swedish data. Investors log in to
their Vanguard accounts much more frequently in rising rather than in falling markets.
Table 3 reports the regression results for the Vanguard data. Once again, account
logins are strongly increasing in prior returns. The positive coefficient on RETURN
t
indicates that a 1% increase in the prior averaged return (i.e., 0.01) is associated
with between 18,000 and 23,000 additional account logins (i.e., 56% of the daily
mean number of logins). In both specifications, the RETURN
t
coefficients are
significant at the 1% level. This evidence clearly supports an ostrich effect in US
investor behavior. The magnitudes of the coefficients using US data are different
from those for the Swedish data, in part, because the number of Vanguard accounts
Fig. 1 Stockholm All Shares stock index and Swedish pension account look-ups. The figure plots
standardized residuals from the following two regressions: LOOKUPS
t
¼ a
0
þ Σ
i¼TWRF
a
1;i
DAY
i;t
þ
a
2
TREND
t
þ a
3
TRANSACTIONS
t
þ e
t
and INDEX
t
¼ b
0
þ Σ
i¼TWRF
b
1;i
DAY
i;t
þ b
2
TREND
t
þ e
t
where
LOOKUPS
t
is the daily number of Swedish Premium Pension investor account logins less the daily
number of account rebalancings, DAY
i,t
are day-of-the-week dummy variables, TREND
t
is a linear time
trend, TRANSACTIONS
t
is the daily total number of Swedish pension investor transactions, and INDEX
t
is
the level of the Stockholm All Shares (OMXSPI) stock index. The sample period is January 7, 2002 to
October 13, 2004
106 J Risk Uncertain (2009) 38:95115
Table 2 Regression results for Swedish Pension Authority data
Model 1 Model 2
Intercept 2,656 (5.91) 2,010 (13.69)
RETURN
t
13,696 (1.76) 11,990 (4.34)
Tuesday 133 (0.27) 1,514 (8.59)
Wednesday 865 (1.76) 2,280 (12.90)
Thursday 1,437 (2.89) 2,075 (11.71)
Friday 1,449 (2.93) 2,908 (16.36)
TREND
t
8.4084 (9.42)
Lagged LOOKUPS 0.9313 (73.56)
TRANSACTIONS
t
4.3501 (21.42) 0.3786 (4.11)
Adj. R
2
0.5880 0.9479
DurbinWatson 0.5038 2.1564
Model 1:
LOOKUPS
t
¼ a
0
þ a
1
RETURN
t
þ Σ
i¼TWRF
a
2;i
DAY
i;t
þ a
3
TREND
t
þ a
4
TRANSACTIONS
t
þ e
t
Model 2: LOOKUPS
t
¼ b
0
þ b
1
RETURN
t
þ Σ
i¼TWRF
b
2;i
DAY
i;t
þ b
3
LOOKUPS
t1
þ b
4
TRANSACTIONS
t
þ e
t
LOOKUPS
t
is the daily number of Swedish Premium Pension investor account logins less the daily
number of account rebalancings, DAY
i,t
are day-of-the-week dummy variables, TREND
t
is a linear time
trend, TRANSACTIONS
t
is the daily total number of Swedish pension account rebalancings, and RETURN
t
is the percentage change in the Stockholm All Shares (OMXSPI) index relative to the mean index level
over the previous 4 days. t-statistics are in parentheses. The sample period is January 7, 2002 to October
13, 2004
Fig. 2 Stockholm All Shares return heptiles and Swedish pension account look-ups. The figure plots
average changes in Swedish investor pension account look-ups for heptiles of prior Stockholm All Shares
index changes where both variables are residuals from regressions: Δ ln LOOKUPS
t
ðÞ¼a
0
þ
Σ
i¼TWRF
a
1;i
DAY
i;t
þ a
2
TREND
t
þ a
3
TRANSACTIONS
t
þ e
t
and RETURN
t
¼ b
0
þ Σ
i¼TWRF
b
1;i
DAY
i;t
þb
2
TREND
t
þ e
t
where LOOKUPS
t
is the daily number of Swedish Premium Pension investor account
logins less the daily number of account rebalancings, Δln(LOOKUPS
t
) is the corresponding daily log
change, DAY
i,t
are day-of-the-week dummy variables, TREND
t
is a linear time trend, TRANSACTIONS
t
is
the daily total number of Swedish pension investor transactions, and RETURN
t
is the percentage change in
the Stockholm All Shares (OMXSPI) stock index relative to the mean index level over the previous
4 days. The sample period is January 7, 2002 to October 13, 2004
J Risk Uncertain (2009) 38:95115 107
is much larger. Once again, the control variables are all statistically significant. The
volume coefficient is strongly positive which i s agai n consistent with the
transactions input hypothesis for account monitoring. The DurbinWatson statistic
again suggests that the model is better specified including lagged logins rather than
the time trend.
4 Alternative explanations
The results from both data sets strongly support the ostrich effect. There might, however,
be alternative explanations. First, it could be that media coverage is asymmetric in a way
that makes investors pay more attention to their portfolios in bull markets. If the media
talks more about the stock market when the market is up than when it is down, this could
stimulate attention and portfolio monitoring during up-markets. However, we know of
no evidence that media coverage of the stock market is asymmetric in up and down
markets. Furthermore, even if differences in media coverage could explain part of our
empirical results, one would still need to explain why the media pays more attention
when the market is up. One possible explanation is that, consistent with an ostrich
effect, the demand for media coverage is greater in bull marketsi.e., people want
more information when the market is up.
A second possible explanation for the observed asymmetry in portfolio
monitoring and prior index returns builds on an inverse reasoning about the
direction of causation. Suppose that retail inves tors desire to transact depends on
Fig. 3 S&P 500 index and Vanguard account logins. The figure plots standardized residuals from the
following two regressions: LOGINS
t
¼ a
0
þ Σ
i¼TWRF
a
1;i
DAY
i;t
þ a
2
TREND
t
þ a
3
VOLUME
t
þ e
t
and
INDEX
t
¼ b
0
þ Σ
i¼TWRF
b
1;i
DAY
i;t
þ b
2
TREND
t
þ e
t
where LOGINS
t
is the daily number of Vanguard
investor account logins, DAY
i,t
are day-of-the-week dummy variables, TREND
t
is a linear time trend,
VOLUME
t
is the S&P 500 trading volume, and INDEX
t
is the level of the S&P 500 stock index. The
sample period is January 2, 2006 to June 30, 2008
108 J Risk Uncertain (2009) 38:95115
Table 3 Regression results for Vanguard data
Model 1 Model 2
Intercept (in 000s) 281 (28.00) 79 (6.57)
RETURN
t
(in millions) 2.32 (10.03) 1.81 (9.82)
Tuesday (in 000s) 56 (6.90) 78 (11.97)
Wednesday (in 000s) 56 (6.83) 43 (6.60)
Thursday (in 000s) 44 (5.41) 28 (4.41)
Friday (in 000s) 35 (4.33) 25 (3.94)
TREND
t
118 (5.82)
Lagged LOGINS 0.585 (20.91)
VOLUME
t
0.000019 (4.58) 0.000019 (8.06)
Adj. R
2
0.3567 0.5950
DurbinWatson 0.7631 1.8546
Model 1: LOGINS
t
¼ a
0
þ a
1
RETURN
t
þ Σ
i¼TWRF
a
2;i
DAY
i;t
þ a
3
TREND
t
þ a
4
VOLUME
t
þ e
t
Model 2: LOGINS
t
¼ b
0
þ b
1
RETURN
t
þ Σ
i¼TWRF
b
2;i
DAY
i;t
þ b
3
LOGINS
t1
þ b
4
VOLUME
t
þ e
t
LOGINS
t
is the daily number of Vanguard account logins, DAY
i,t
are day-of-the-week dummy variables,
TREND
t
is a linear time trend, VOLUME
t
is the daily S&P 500 trading volume, and RETURN
t
is the
percentage change in the S&P 500 index relative to the mean index level over the previous 4 days. t-
statistics are in parentheses. The sample period is January 2, 2006 to June 30, 2008
Fig. 4 S&P 500 return heptiles and Vanguard account logins. The figure plots average changes in
Vanguard investor account logins for heptiles of lagged S&P 500 index changes where both variables are
residuals from regressions: Δ ln LOGINS
t
ðÞ
¼ a
0
þ Σ
i¼TWRF
a
1;i
DAY
i;t
þ a
2
TREND
t
þ a
3
VOLUME
t
þ
e
t
and RETURN
t
¼ b
0
þ Σ
i¼TWRF
b
1;i
DAY
i;t
þ b
2
TREND
t
þ e
t
where LOGINS
t
is the daily number of
Vanguard investor account logins, Δ ln(LOGINS
t
) is the corresponding daily log change, DAY
i,t
are day-of-
the-week dummy variables, TREND
t
is a linear time trend, VOLUME
t
is the S&P 500 trading volume, and
RETURN
t
is the percentage change in the S&P 500 stock index on day t relative to the mean index level
over the previous 4 days. The sample period is January 2, 2006 to June 30, 2008
J Risk Uncertain (2009) 38:95115 109
exogenous variables, including information gleaned from their own portfolios value.
If investors look at their portfolios to transact and if this willingness to transact is
disproportionately an expression of a higher demand for stocks (perhaps due to the
disposition effect), then market prices could go up when more investors log in to
check the value of their funds. Although we control for the number of transactions
and market volume to rule out this possibility, our controls may be insufficient
because we do not know if a single investor logs in one or several times when
transacting. However, note, first, that our return variable temporally precedes the
account logins whereas the reverse causation story has them in the opposite order.
Second, another contraindication to the inverse causal reasoning emerges from
examining partial correlations in the Swedish premium pension sample in which we
have the number of transacti ons registered. If lookups are driven by a willingness to
transact, the partial correlation between transactions and the market index controlling
for look-ups should be greater than the partial correlation between look-ups and the
market index controlling for transactions, but this is not the case. When controlling
for look-ups, the correlation between transactions and the OMXSPI index is weak
and non significant (correlation=0.04, p-value=0.34). However, when controlling
for transactions, the partial correlation between look-ups and the OMXSPI index is
much greater and significant (correlation=0.35, p-value<0.001).
5 Additional implications of the ostrich effect
We suspect most readers, who introspect about their own behavior during the bull
market of the late 1990s and the subsequent meltdown, or on the behavior of those
around them, will not be surprised by these results. An attraction of our model,
however, is that it links observable behavior (i.e., information collection) with internal
preferences. In particular, our empirical evidence of an ostrich effect implies that,
consistent with Proposition 1, the impact effect of attention is large, that the lag in
reference point updating given inattention is small, and that risk aversion is not too
high and is decreasing (or not increasing too quickly) in the expected level of the
informational shocks. Portfolio monitoring decisions are, thus, a window into
investors preferences for the timing of the resolution of uncertainty. In contrast,
earlier models with similar psychological considerations (e.g., Backus, Routledge and
Zin 2004; Barberis, Huang and Santos 2001) have only been tested with price data.
Our model also suggests other new testable restrictions. First, the ostrich effect
implies that the loss aversi on reference point should increase faster in bull markets
than it falls in down markets. Since the kink in loss-averse utility functions
induces first-order risk aversion at the reference point, the asymmetric reference
point updating dynamics will lead to asymmetric dynamics in the market risk
premium. In contrast, most prior models assume symmetric dynamics for risk premia
associated with loss aversion in up- and down-markets.
Second, the ostrich effect has implications for trading volumes and market
liquidity. For example, it may help explain the well-documented relationship
between trading volume and market returns. Griffin, Nardari and Stulz (2004)
examined market-wide trading activity and lagged returns in 46 markets and found
that positive returns led to significant subsequent increases in volume 10 weeks later
110 J Risk Uncertain (2009) 38:95115
in 24 of 46 countries. In no country was there a significant decrease. After exploring
liquidity effects, participation costs, over-confidence, disposition effects, and a
variety of other possible explan ations, the c onclusion is that no single theory is
consistent with all of the patterns observed in the data. The ostrich effect may play at
least a contributory role since positive lagged returns reduce the cost of attending to
the market and, thereby, reduce the cost of being available for trading.
Third, the ostrich effect may also induce differential returns to liquid and illiquid
fixed-income investments, a hypothesis tested by Galai and Sade (2003) in their
paper on a related type of ostrich effect. They argue that average returns on liquid
fixed-income investments (such as treasury bills) are greater than on illiquid fixed-
income investments (such as certificates of deposit) because investors are less likely
to attend to the day-to-day fluctuations in the value of illiquid investments.
Fourth, it is a commonplace that liquidity dries up during major market downturns
such as the Asian crisis of 1997, the Russian debt default in 1998, and the credit crunch
of 2008. This is, again, consistent with retail investors temporarily ignoring their
portfolios in downturnsso as to avoid coming to terms mentally with painful losses
and, thus, being unavailable to supply liquidity. During market rallies, the ostrich effect
improves liquidity as more investors actively follow the market.
Fifth, the ostrich effect has social consequences for the transmission of information.
As Robert Shiller documents in Irrational Exuberance, social factors play a critical role
in financial markets, pumping up values when rising markets create a buzz. If
people do not pay attention to the market when prices fall, this could easily suppress
such social transmission, exacerbating downturns. If investors obsessively track the
value of their portfolios when market values are rising, it is likely that this would
facilitate interpersonal communication and positive feedback effects.
Our model can, and should, be expanded to more than two periods. With multiple
periods, investors have a richer decision about when to monitor and attend. In an up-
market, attending early is, on the one hand, likely to provide an immediate burst of
positive utility, but is also likely to diminish future utility. The reverse is true in a
down-market. Not attending over a prolonged period of time in a down-market is the
financial equivalent of death by a thousand cuts. Failing to attend and come to
terms wi th her losses after a market downturn means that the investor repeatedly
evaluates her utility using an inflated benchmark due to slow benchmark updating.
This suggests that people with high discount rates would be more likely to look
frequently in up markets and infrequent ly in down markets.
6 Selective attention an d ration ality
Selective attention is fully rational given the premise that investors are psycholog-
ically affected by information about the world around them. There is no self-
deception in the sense of simultaneously knowing something and willfully not
knowing it (see, e.g., Sartre 1953). In our model , investors correctly interpret
whatever information they have. Our argument that investors can regulate the impact
of information on their utility instead relies on the idea that there are multiple ways
to experience information. Recent work by psychologists (e.g., Sloman 1996;
Epstein et al. 1992 ) suggests that people may hold beliefs at different levels. Prior
J Risk Uncertain (2009) 38:95115 111
research also shows that knowledge that is fuzzy”—i.e., lacking in precisionis
perceived as less salient or vivid and has greater leeway for self-manipulation of
expectations in relation to knowledge (Schneider 2001). Thus, whether the ostrich
effect is rational depends on the accuracy of peoples assessments of how potential
information will make them f eel. Our model is exposited assuming these
assessments are accurate, so our story does not require irrationality.
7
Selective exposure may also play an evolutionary role in helping people live with
risk and, thereby, obtain the potential long-term benefits of risk-taking. Thus, in a
finance context, the ostrich effect may lower, to some extent, the required market
equity premium. Prior work in behavioral economics has also shown, consistent with
the theory of second best, that the introduction of new biases can have beneficial
effects when they counteract the neg ative effects of existing biases. For example,
overconfidence can mitigate extreme risk aversion induced by loss aversion
(Kahneman and Lovallo 1993). However, the ostrich effect can also induce costs
due to delays in information acquisition in adverse environmen ts.
7 Conclusions
This paper has presented a decision-theoretic model in which information acquisition
decisions are linked to investor psychology. For a range of plausible parameter
values, the model predicts that individuals may collect additional information
conditional on favorable news and avoid information following neutral or bad news.
Empirical evidence from two large datasets for Swedish and American investor
account login activity supports the existence of an ostrich effect in financial
markets.
Possible applications of the ostrich effect are much broader than finance.
Ostrich-like behavior s hould be observed in any situation in which people are
emotionally invested in information and havesomeabilitytoshieldthemselves
from it. For example, our two period model can be applied to the situation of
parents of children with chronic problems, such as autism or mental retardation. In
period 1 the parents receive public information (observations of the childs
behavior) and must decide whether to seek early definitive medical tests. By period
2thechilds condition is clear regardless of whether they obtained the test results
in period 1. Similar intuitions could apply in emotionally charged medical
situations such as HIV testing.
The core ideas in this paperthat people derive direct utility from information
and that, as a result, they pay selective attention to informationjoin an expanding
body of research that can be labeled the new new economics of information
(Loewenstein 2006). Whereas the new economics of information adhered to standard
economic assumptions about the individual but showed how market-level informa-
tion asymmetries can produce suboptimalities, the new new economics of
information focuses on characteristics of how emotionally invested and computa-
7
If ex ante utility forecasts are erroneous (see Loewenstein, ODonoghue and Rabin 2003), then the
ostrich effect could cause investors to pay attention too little or too much.
112 J Risk Uncertain (2009) 38:95115
tionally bounded individuals process information. This work ranges from evidence
that people do not use Bayes Rul e when updating expectations (e.g., Camerer 1987)
to violations of the law of iterated expectations (Camerer, Loewenstein and Weber
1989) to demonstrations that personal experience is weighted more heavily than
vicarious experience, even when both have equal information value (Simonsohn et
al. 2008). Our observation that people derive utility directly from informationand
are, therefore, motivated to attend to it selectively as part of utility maximizationis
just the latest in an ongoing effort to map out a more realistic account of how people
mentally process and respond to information.
Acknowledgments We thank the Swedish Foundation for International Cooperation in Research and
Higher Education (STINT) and the Bank of Sweden Tercentenary Foundation (grant K2001-0306) for
supporting Karlsson and Loewensteins collaboration, Bjorn Andenas of the DnB Norway group, SEB,
and the Swedish Premium Pension Fund for providing data. We are very grateful to Daniel McDonald for
able research assistance. We thank Kip Viscusi (editor), two anonymous referees, and also On Amir, Nick
Barberis, Roland Benabou, Stefano DellaVigna, John Griffin, Gur Huberman, John Leahy, Robert Shiller,
Peter Thompson, Jason Zweig and seminar participants at the 2004 Yale International Center for Finance
Behavioral Science Conference and at Case Western University for comments and suggestions.
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