Stock Price Reactions to the Information and Bias in Analyst-Expected Returns

134 Pages·2017·1 MB·English

Stock Price Reactions to the Information and Bias in
Analyst-Expected Returns∗
Johnathan A. Loudis†
September 12, 2018
Preliminary and incomplete (please do not distribute without the author’s permission)
Abstract
I use analyst price targets to compute analyst-expected returns, from which I extract an
information component and a bias component. I find that prices underreact to the information
component and overreact to the bias component. Prices react positively to both components
during price target announcement months. The initial positive reaction to the information
component is incomplete and prices continue to drift in the direction of the initial reaction
duringsubsequentmonths. Theinitialpositivereactiontothebiascomponentistransitoryand
reversesinsubsequentmonths. Additionally,pricetargetannouncementwindowreturnsforecast
returnsinsubsequentmonths. ThisalleviatestheconcernthatthepredictabilityIobserveisthe
result of model misspecification. It also implies that drift related to information underreaction
dominates reversal related to bias overreaction in subsequent months. Taken together, these
results provide evidence that the market fails to efficiently incorporate information in analyst-
expected returns into prices during the announcement month.
∗Iwouldliketothankmycommitteemembers,BryanKelly(co-chair),StefanNagel(co-chair),LubosPastor,and
Larry Schmidt, for continued advice and support. I would also like to thank Chuck Boyer, George Constantinides,
Stefano Giglio, Niels Gormsen, Sam Hartzmark, Paymon Khorrami, Christopher Malloy, Pietro Veronesi, Michael
Weber, and Yiyao Wang for very helpful comments. I thank Lu Zhang and Seth Pruitt for providing data used in
someofmyanalyses. ThispaperisbeingwrittenaspartofmydissertationattheUniversityofChicagoBoothSchool
of Business, during which time I received support from the Drumheller Family Foundation Fellowship. Comments
are welcome, and all errors are my own.
†Booth School of Business, University of Chicago, Chicago, IL 60615. Tel.: [phone]. E-mail:
[email protected]
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JEL Classification: G12, G14, G24, G41
Keywords: Expected returns, Cross section, Analysts, Price targets, Market efficiency, Price drift, Return
reversal
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1 Introduction
Understanding what information is price-relevant and the efficiency with which it is incorporated
into prices is a first-order concern in asset pricing (Fama (1965), Hayek (1945)). Early work typi-
callyconcludedthatthemarketisrelativelyefficientatincorporatinginformationintoprices(Fama
(1970), Fama (1991)), however, as time passed examples of economically significant apparent inef-
ficiencies such as post-earnings announcement drift (Bernard and Thomas (1989)) and momentum
(Jegadeesh and Titman (1993)) have arisen. The debate over whether such phenomena are due
to inefficient information incorporation or rational risk premia has received considerable attention
in the literature and remains an active area of research. I contribute to this debate by providing
evidence that the market inefficiently incorporates sell-side analyst-expected return information
into prices. My results also provide an explanation for recent results in the literature that show
analyst-expected returns positively forecast returns in the cross section (Da et al. (2016), Gleason
et al. (2013), and Da and Schaumburg (2011)), yet are negatively related to net anomaly exposure
(Engelberg et al. (2018)).
I construct analyst-expected returns from analyst price targets, which have become a standard
forecast included in many sell-side analyst reports.1 I begin by decomposing analyst-expected
returns into three components: 1. A component that consists of expected returns conditional on
the market’s information set, 2. A bias component conditional on the market’s information set,
and 3. A component orthogonal to the market’s information set, which I interpret as a noisy
information signal.2 I then develop a framework to evaluate the efficiency with which the market
incorporates these components into prices and derive related empirical tests.
Ifindthatthemarketinefficientlyincorporatestheinformationandbiascomponentsintoprices
inthefollowingtwoways. First,themarketunderreactstotheinformationcomponent. Pricesreact
positively to the information component during the announcement month, and continue to drift in
1I use “sell-side analyst” and “analyst” interchangeably. All results and commentary are related to sell-side
analysts and not buy-side analysts.
2I construct the bias component as the difference between analyst-expected returns projected onto the market’s
information set and expected returns conditional on the market’s information set. This component should not
contain price-relevant information beyond that already in the market’s information set. I construct the information
componentastheresidualinthedecompositiondescribedabove,givenproxiesforanalyst-expectedreturns,expected
returns conditional on the market’s information set, and the bias component. To the extent that analyst-expected
returns contain information orthogonal to the market’s information set, the information component is a (noisy)
signalofprice-relevantinformation. Iprovidemoreprecisedefinitionsforeachofthesecomponentsinmyanalytical
framework.
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thedirectionoftheinitialreactionduringsubsequentmonths. Second, themarketoverreactstothe
bias component. Prices react positively to the bias component during price target announcement
months, but the initial reaction reverses during subsequent months.
To illustrate these phenomena, I provide plots of coefficients estimated by regressing realized
cumulative returns (including announcement month returns) on the information and bias compo-
nents that I extract from analyst-expected returns in Figure 1 below. The figure shows results
when the information component is computed using two different methods. The first depends on
assumptionsrelatedtothemarket’sinformationsetandthemodelofexpectedreturns. Thesecond
is model-independent. Both methods are described more fully in Sections 3 and 4. In both cases,
prices react positively to the information and bias components during the announcement month
(month zero). Both coefficients are positive and statistically significant. In subsequent months,
information component coefficients continue to drift upward, whereas bias component coefficients
driftdownward. Inallsubsequentmonths, Icanrejectthenullthatthetheinformationcomponent
coefficients are equal to zero, however, I cannot reject the null that the the bias component coef-
ficients are equal to zero after two or three months (depending on the specification). This implies
that price reactions to the information component, although partially delayed, are permanent. It
alsoimpliestheinitialpricereactionstothebiascomponentaretransitory. Theseresultsarerobust
to a number of modifications to my main specification, which I summarize in a series of robustness
checks.
To the extent that market expectations are influenced by published analyst expectations, the
returnpredictabilityIdocumentistheresultofpricingerrorsdirectlyrelatedtoexpectationalerrors
and inefficient information incorporation rather than risk premia. Additionally, price reactions to
thebiascomponentaresignificantlyweakerthanreactionstotheinformationcomponent. Although
I find evidence consistent with Engelberg et al. (2018) that analyst-expected returns are negatively
related to anomaly-implied returns, my results show that the market partially corrects for this bias
when forming expectations so that biased analyst expectations may not be a significant driver of
anomaly returns. My results show that simply assuming biased analyst-expected returns reflect
market expectations of returns is incorrect. When assessing the information content in analyst-
expected returns, the market is wise enough to correct for much of the bias.
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Figure 1: Forecasting Coefficients for Realized Cumulative Returns on Information and Bias
This figure plots regression coefficients when realized cumulative returns (including those from the
price target announcement month) are regressed on cross-sectionally z-transformed measures of the
information and bias components in analyst-expected returns using the Fama and MacBeth (1973)
procedure. The left panel uses my main method for computing the information component (based
on equation (18)), and the right panel uses an alternative method (based on equation (14)). The
bias component is computed according to equation (23) for results in both panels. These plots are
based on regression results presented and described more fully in Table 6, and error bars are based
on standard errors computed for that table.
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To establish my main results, I begin with a characterization of analyst-expected returns. They
are positively biased, which is in line with previous studies (Bradshaw et al. (2013), Bonini et al.
(2010), andBrav and Lehavy (2003)). Median analyst-expected returns are 1.29% per month in my
sample, whereas median realized returns are only 0.91% per month. They are also cross-sectionally
biased. Using a standard set of characteristics models,3 I find that analyst-expected returns imply
characteristicriskpremia(“analyst-impliedriskpremia”)thatarebothofthewrongmagnitudeand
signwhencomparedtoriskpremiaestimatedusingrealizedreturnsforamajorityofcharacteristics
I investigate.
For instance, the analyst-implied value premium is negative. In my main specification, the
analyst-implied value premium is -0.22 whereas that estimated using realized returns is 0.93. In my
most broad specification, which includes 15 characteristics, 11 of the 15 analyst-implied risk premia
have the opposite sign as compared to those estimated using realized returns. I also formally reject
the null hypothesis that analyst-implied risk premia are jointly equivalent to those estimated using
realized returns in all three characteristics models I investigate. These results are consistent with
results in Engelberg et al. (2018), who find that analyst-expected returns are negatively related to
an aggregated anomaly exposure variable.
Despitebeingbiasedinlevelandinthecrosssection, analyst-expectedreturnshavestatistically
and economically significant forecasting power over returns in the cross section. This is true in a
univariate sense, and strengthens when controlling for observable characteristics. In a univariate
monthly cross-sectional regression of realized returns on ex ante analyst-expected returns, analyst-
expected returns have a risk premium of 0.13 with a t-statistic of 2.31. For every 1% increase
in analyst-expected returns, next month’s expected return increases by 0.13%. When adding 7
characteristic controls from Model 2 (Lewellen (2015)), the analyst-expected return risk premium
increases to 0.15 with a t-statistic of 3.70.4
Motivatedbythefindingthatanalyst-expectedreturnsarebiasedinthecrosssectionyetcontain
cross-sectional forecasting power, I develop a framework to model how the market incorporates the
3I use the three sets of characteristics from Lewellen (2015) as conditioning information. I then estimate charac-
teristic risk premia from realized returns and those implied by analyst-expected returns using Fama and MacBeth
(1973) regressions. The three models use either 3, 7, or 15 characteristics. My main specification is that with 7
characteristics (“Model 2”).
4My measure of analyst-expected returns has a cross-sectional standard deviation of 1.51% (see Table 5). This
impliesthataonestandarddeviationincreaseinanalyst-expectedreturnincreasesnextmonth’sexpectedreturnby
0.20% based on the univariate regression results, or 0.23% when also controlling for Model 2 characteristics.
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information in analyst-expected returns into prices and expectations about future prices. This
framework takes seriously the idea that analyst forecasts are signals that the market incorporates
into its own expectations about future prices rather than reflections of actual market expectations,
which distinguishes this paper from others that either explicitly or implicitly assume that market
expectations are facsimiles of analyst expectations.5 Under a Bayesian learning paradigm, the
market should update expectations about future prices by choosing optimal weights to apply to
prior beliefs about expected returns and debiased analyst-expected returns. I consider cases where
the market makes two potential mistakes relative to this ideal when updating beliefs. First, it may
apply non-optimal weights to debiased analyst-expected returns. Second, it may fail to correct for
the bias in analyst-expected returns.
Next, I develop an asset pricing framework that I use to derive expressions for expected returns
whenthemarketmakeseitherofthetwoupdatingmistakesdescribedabove. Theframeworkyields
five related Empirical Tests. The first tests whether the market reacts to the bias component in
analyst-expected returns when updating expectations over future prices. The second tests whether
the market eventually corrects errors associated with the potential initial reaction to the bias com-
ponent. The third tests whether the market initially reacts to the information component. The
fourth tests whether this initial reaction to the information component is transitory or perma-
nent. Finally, the fifth tests whether the market initially misweights the information component in
analyst-expected returns.
As discussed above and illustrated in Figure 1, I find evidence that the market inefficiently
incorporatesinformationinanalyst-expectedreturnsintoprices. Pricesreactpositivelytoboththe
information and bias components during the announcement month. In subsequent months, prices
continue to drift in the direction of the information component, but reverse their initial reaction to
thebiascomponent. Theseresultsreconcilemyfindingthatanalyst-expectedreturnsarebiasedyet
they forecast returns, and that the forecasting power strengthens when controlling for observable
characteristics. The positive forecasting power originates from the partially delayed response to
the information component in analyst-expected returns, and the component in analyst-expected
returns related to expected returns conditional on the market’s information set. Controlling for
other characteristics strengthens the forecasting power since it effectively corrects for the bias
5See, for instance, Bordalo et al. (2017), Bali et al. (2017), or Brav et al. (2005).
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component in analyst-expected returns. I also find that announcement window returns cumulated
over the five days surrounding price target announcements forecast returns in subsequent months.
This alleviates concerns that the predictability I observe from the information component is the
result of model misspecification. It also implies that drift related to information underreaction
dominates the reversal related to bias overreaction in subsequent months.
A long-short trading strategy based on the information component earns a statistically signifi-
cantaveragemonthlyreturnof1.14%withanannualizedSharperatioof1.08,whereasonebasedon
raw analyst-expected returns earns an average monthly return of 0.92% with an annualized Sharpe
ratio of 0.66. The strategy based on the extracted information component yields higher average
returns than that based on raw analyst-expected returns and does so with lower volatility. Both
strategies survive risk-adjustment using many standard empirical factor models, suggesting that
analyst-expected return forecasting power is not simply the result of analysts using known factor
models to generate their price targets. To the extent that factor models explain risk exposures, this
result implies that the expected return patterns generated by this strategy cannot be explained by
exposure to standard risk factors.
Finally, I test whether the market is more efficient at incorporating analyst-expected return
information into prices for certain subsets of stocks relative to others. Intuitively, it is a reasonable
hypothesis that the market incorporates analyst-expected return information into prices of stocks
in low information environments less efficiently than for those in high information environments,
yet the evidence on this remains mixed. For instance, So (2013) finds that the market overreacts to
informationinanalystearningsforecastsrelativelymoreforstocksinlowinformationenvironments
when using firm size as a proxy for firm-level information environment. Contrarily, Hong et al.
(2000) find evidence that the market underreacts to information relatively more for stocks in low
informationenvironmentswhenusingresidualanalystcoverage(controllingforfirmsize)asaproxy
for firm-level information environment.
I find evidence that the market underweights the information component in analyst-expected
returns for small stocks and stocks with low residual analyst coverage, but cannot reject the null
thatitcorrectlyweightsinformationforlargestocksandstockswithhighresidualanalystcoverage.
These results are consistent with results in Ben-Rephael et al. (2017) and Hong et al. (2000), who
show the market is slow to incorporate news about neglected or low attention stocks into prices.
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I also find evidence that that analyst-expected returns contain more precise information about
the value of high-volatility and high turnover stocks relative to low-volatility low-turnover stocks.
This is consistent with results in Ljungqvist et al. (2007) and Frankel et al. (2006), who find
that analyst forecasts are more accurate for stocks with high institutional visibility (such as those
with high volatility and high turnover). The fact that I find cross-sectional delayed response and
impliedprecisionpatternsconsistentwiththoseobservedinothersettingsprovidesmoreconfidence
that my information measure captures price-relevant news associated with analyst price target
announcements.
The remainder of this paper is organized as follows. In Section 2, I briefly review related
literature and my contributions. I present my analytical framework in Section 3, describe my data
in Section 4, and present my main empirical results in Section 5. Section 7 concludes.
2 Literature Review
There is a large literature related to analyst reports and forecasts that I do not attempt to fully
summarize here.6 I instead focus on studies related to analyst price targets, which have received
relatively little attention in the literature compared to studies of analyst earnings forecasts and
recommendations.7
Therelatedanalystpricetargetliteraturefallsbroadlyintotwocategories. Thefirstusesanalyst
price targets to compute analyst-expected returns and compares these to known anomalies. Brav
et al. (2005) and Bali et al. (2017) indicate that analyst-implied risk premia are typically in line
with evidence from realized returns for the characteristics they consider, however, Engelberg et al.
(2018)findthatanalyst-expectedreturnsarenegativelyrelatedtoanaggregatedanomalyexposure
variable in the cross-section. The Engelberg et al. (2018) is puzzling since (rational) expectations
of returns should be positively correlated with their anomaly exposure variable. Analysts appear
to be correctly predicting the signs of some risk premia, but on average get the relationship for a
6SeeKotharietal.(2016)forasurveyoftheanalystforecastingliteraturewithaparticularfocusonassetpricing.
See Bradshaw (2011) and Ramnath et al. (2008) for more general surveys of this literature.
7Recent papers that study analyst price targets including Bali et al. (2017), Bradshaw et al. (2013), andBonini
et al. (2010) explicitly mention a paucity of studies on analyst price targets relative to those on earnings forecasts
andrecommendations,despitethepricetarget’sabilitytoparsimoniouslydescribeanalyst-expectedreturns. Infact,
there are a number of studies that use analyst earnings forecasts to estimate expected returns via an implied cost
of capital calculation rather than using price targets directly (see Hughes et al. (2009), Pástor et al. (2008), and
Gebhardt et al. (2001), among others).
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broad set of anomalies incorrect. In my study, I consider a broader set of characteristics than in
either Brav et al. (2005) or Bali et al. (2017). I also estimate analyst-implied risk premia for each
characteristic as opposed to the premium associated with an aggregated anomaly exposure variable
as in Engelberg et al. (2018), and find evidence consistent with that in Engelberg et al. (2018)
in the sense that my analyst-implied risk premia have the opposite sign as most of the realized
return-implied risk premia I study.
Thesecondstrandofthisliteraturedocumentstheabilityofanalyst-expectedreturnstoforecast
realized returns. Da and Schaumburg (2011) show that industry-adjusted analyst-expected returns
positivelyforecastrealizedreturnsinthecross-section. Theyarguethatthisistheresultofanalysts’
tendency to specialize in specific industries, and their ability to accurately assess within-industry
relative valuation. Da et al. (2016) decompose price targets into components related to short-
term earnings forecasts and price-to-earnings forecasts. They find that analyst-expected returns
(also adjusted for industry) forecast returns in the cross-section, and that the forecasting power is
related to both components in the decomposition. Gleason et al. (2013) try to infer which of two
valuation methods (residual income model or PEG-ratio-based) were used to generate the price
targets they study. They find that analyst-expected returns forecast abnormal returns independent
oftheinferredvaluationmodel. Theyalsofindthatanalyst-expectedreturnsbasedonpricetargets
generatedusingaresidualincomemodelarebetterabletopredictfuturereturns. Finally,Engelberg
etal.(2018)findthatlog-analyst-expectedreturnscomputedusingmedianpricetargetsissuedover
the past 11 months negatively forecast returns in the cross-section. They attribute this result to
thenegativecross-sectionalcorrelationbetweentheirlog-analyst-expectedreturnmeasureandtheir
aggregateanomalyexposurevariable. Myresultshelpexplaintheapparentlycontradictoryfindings
that analyst-expected returns are negatively related to anomalies yet positively forecast returns in
the cross section. Namely, my analysis indicates that the predictability is generated primarily by
the components of analyst-expected returns related to expected returns conditional on observable
market information and drift related to the information component, but not the bias component.8
8DechowandYou(2017)studythecomponentsoferrorinanalyst-expectedreturns,settingupareturndecomposi-
tionframeworksimilartomine. Theytostudythreecomponentsofbiasinanalyst-expectedreturns: fundamentals-
related bias (i.e. caused by errors in earnings forecasts), firm characteristics-related bias, and bias from analyst
incentives issues. The authors find that the bias is dominated by the former two components, and the latter con-
tributes relatively little to overall bias. Although their focus is on understanding the relative contributions of these
components to overall bias, they also find that a “purified” analyst-expected return measure (i.e. one without their
estimatedbias)positivelyforecastsfuturereturns. Theyinterpretthisasevidencethatthemarketdoesnotfullyin-
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