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lar revision in the consensus target price leads to a market price adjustment of 1.6
dollars. In this case, the new ratio (1.25) di¡ers from its long-term value of 1.23.
The regression analysis indicates that the consensus target price is adjusted by
17 percent of (127 À 1.23 n 101.6), or 35 cents in the ¢rst week. Since amarket is insig-
ni¢cantly di¡erent from zero, it can be seen that in IBM™s case, once the system of
prices has been shocked away from its long-term relation, it is predominantly the
analysts, rather than market participants, who tend to revise their target prices
toward the long-run relation.
1958 The Journal of Finance

Cointegration Regression Results
The table provides regression results of the cointegration analysis. Panel A presents results for
IBM™s 151 weekly observations of target and market prices spanning the period January 1997
through December 1999. The target price series is formed by equal-weighting all outstanding
target prices that were issued within the previous 90 days. The regression setup is given in Sec-
tion III and we estimate the parameter b, which captures the long-run ratio of target prices-to-
market prices, and the vector a, which contains the response coe„cients of each price variable
to deviations from the long-run equilibrium.The ¢rst row contains the parameter estimates and
the second contains the corresponding p-values. We do not report standard errors associated
with b, as these are based on asymptotic theory whose ¢nite sample properties are undeter-
mined. Panel B provides results for the full sample of 900 ¢rms that have at least one year of
continuous record of weekly target and market prices. For each parameter we calculate the
mean, median, 25th, 50th, and 75th percentile, as well as the standard deviation across the 900
¢rm regression estimate. Panel C provides regression results for subsamples of ¢rms sorted by
market capitalization (size). Size terciles are formed on the basis of NYSE capitalization cuto¡s
and are adjusted quarterly. For each size sort, we report the average of the regression estimates.
For example, for the smallest 300 ¢rms in this sample the average long-run ratio b is 1.37.

Long-run Ratio Target Prices Response Market Prices Response
b atp (%) amarket (%)

Panel A: IBM

1.23 0.17 0.04
F 0.00 0.35

Panel B: Full Sample

À 0.02
Mean 1.28 0.09
À 0.04
25th percentile 1.20 0.07
À 0.01
Median 1.26 0.09
75th percentile 1.33 0.11 0.01
Standard deviation 0.11 0.04 0.04

Panel C: Size-sorted Results

Portfolio Number of Long-run Target Prices Market Prices
Firms Ratio b Response atp (%) Response amarket (%)

À 0.02
Small 126 1.37 0.09
À 0.02
Medium 347 1.29 0.09
À 0.01
Large 427 1.23 0.10

C. Full-Sample Implementation of the Cointegration Analysis
In this subsection, we implement the cointegration analysis for the full sample
of 900 ¢rms and estimate, for each ¢rm, the parameters that capture the long-run
ratio of target prices relative to market prices, b, as well as the (2 ‚ 1) vector of
response coe„cients, a, that captures how each price variable responds to devia-
tions from the long-run relation. We report the results in Panel B of Table VI.
An Empirical Analysis of Analysts™ Target Prices 1959

Since the regression analysis results in 900 sets of parameter estimates, we re-
port summary statistics only.23
Consider ¢rst the full sample results of the long-run ratio of target-to-market
prices, b, given in the ¢rst row. The ¢rst column indicates that the grand-average
(median) of the 900 estimates equals 1.28 (1.26). That is, conditional on at least
two years of continuous consensus coverage, the average ¢rm in this sample is
expected to earn 28 percent annually. Furthermore, with the 25th and 75th per-
centiles equal to 1.20 and 1.33, we learn that the distribution of these estimates is
quite disperse, with a slight skew to the right.24
Next, consider the full-sample estimates of the response coe„cients, atp and
amarket. The second column provides the grand average (median) of atp, which
equal 9.0 (9.0) percent. From this we learn that for the average ¢rm, the analysts™
weekly response to a one-dollar shock in either target price or stock price that
causes a deviation from the long-term relation is to revise the target price by nine
percent of the resulting deviation. As discussed earlier, a positive response co-
e„cient is consistent with analysts revising their target prices toward the long-
term relation once the system has been perturbed away from it.The market price
response coe„cient, amarket, is smaller by one order of magnitude, as can be seen
from the third column.The grand-average (median) of the market™s response coef-
¢cient is only À 0.02 ( À 0.01) percent, suggesting a two percent weekly correction
in response to a one-dollar deviation from the price system™s long-term ratio. The
above evidence is consistent with the interpretation that analysts revise their
targets toward the long-term relation once the system has been shocked away
from it. The same statistics for market prices, amarket, indicate a much smaller re-
action to deviations from the long-term relation by market participants.
The evidence regarding the estimates of the response coe„cients may seem in-
consistent with the results reported in Section II.C, in which we detect abnormal
return drifts subsequent to the target price revision.We argue, however, that the
two empirical ¢ndings are, in fact, not inconsistent with each other. While the
event-study approach allows us to isolate investor reactions to extreme target
price revisions by conditioning both on the magnitude of the target price revision
and the type of recommendation change, the cointegration approach provides
lower frequency evidence in which unconditional estimates of target and market
price are calculated. Hence, abrupt target price revisions are averaged with less

Because we estimate the regressions on a ¢rm-by-¢rm basis, the results do not account for
possible cross-correlation in the regression errors. While it is beyond the scope of this paper
to estimate a large variance-covariance matrix or price errors, we note that the individual
¢rm parameter estimates are, however, consistent and, in our setup, estimated quite precisely.
Our analysis leaves open the question of whether the estimates of ex ante returns are
consistent with those elicited from an asset-pricing model such as the CAPM or the Fama
and French three-factor model. We note here that, much like the high estimates that we re-
port, the geometric average annual market return over our sample period is quite high at 19.9
percent. Furthermore, in unreported analysis we have contrasted the cointegration estimates
of b with expected returns from the Fama and French model, allowing for the possibility of
mispricing as in Pastor and Stambaugh (1999). We ¢nd that allowing for mispricing uncer-
tainty regarding the Fama and French three-factor model can indeed account for the cross-
sectional dispersion in the reported estimates of b.
1960 The Journal of Finance

extreme changes. It is therefore not surprising that our estimates of amarket, the
market price response coe„cient, are extremely small.
We conclude this section by presenting, in Table VI, Panel C, parameter
estimates sorted by the ¢rms™ market capitalization (size). To the extent that size
di¡erences are associated with cross-sectional di¡erences in information asym-
metry or risk, our analysis sheds light on the dynamics of target prices as they
relate to this characteristic of ¢rms.We form size terciles based on NYSE capita-
lization cuto¡s and adjust these quarterly. Event ¢rms are then classi¢ed based
on the market value of their equity at the end of the preceding quarter. Consider
¢rst the estimates of the long-run ratio of target-to-market prices, b. Beginning
with ¢rms in the smallest tercile, the average estimate of b is 1.37 and declines
monotonically to 1.23 for the ¢rms in the largest tercile. This pattern indicates
that analysts expect a higher annual price appreciation for small stocks, which
is consistent with asset pricing models, such as the Fama and French three-factor
model, that include size as a risk factor. Finally, the information regarding the
response coe„cients atp and amarket indicates that there are no cross-sectional dif-
ferences in either analyst or the market reactions to deviations from the long-
term relationship across the size terciles. As with the IBM results, the overall
evidence supports the interpretation that market prices react to the information
conveyed in analyst reports but that any correction to the long-term relation be-
tween target and market prices is predominantly made by analysts.

IV. Linking the Evidence from the Short- and Long-term Analyses
The preceding section provides evidence as to the dynamics of target and mar-
ket prices as well as to their common long-term relation.We now seek to build on
these results and link them to the short-term event study conducted in Section II.
Speci¢cally, we construct an estimate of the expected one-week-ahead consensus
target price and then examine whether investors understand the long-term dy-
namics of the price series. We do this by testing whether event-day abnormal re-
turns are correlated with the unexpected part of the target price revision (for a
similar approach, see Lowry and Schwert (2002)). Toward this end, we ¢rst esti-
mate for each of the 900 ¢rms a one-week-ahead forecast of the consensus target
prices, using the sample information that would have been available to investors
prior to the release of the analyst reports. We require a minimum of 10 weekly
observations to ¢t the cointegration regression. Using the expected consensus
target price, we construct two variables. One is the expected target price revision,
which equals the scaled di¡erence between preannouncement consensus target
price and the forecasted one. The second is the unexpected target price revision,
which equals the di¡erence between the announced and the expected target
price. We scale both the expected and unexpected variables by the event-¢rm™s
market price two days prior to the event.
Next, we estimate a regression similar to the one conducted in Section II but
by employing the expected and unexpected target price proxies rather than the
scaled change in the individual brokerage house target price, DTP/P. If the coin-
An Empirical Analysis of Analysts™ Target Prices 1961

tegration setup provides an adequate description of the evolution of market and
target prices, we expect that only the unexpected component of the target price
revision would be related to event-time abnormal returns. The regression takes
the following form:

þb þ g Expected þ Z Unexpected þe °5Þ

The regression results are reported in TableVII. Consider the results for Model
I ¢rst. Consistent with our prediction, we ¢nd that, controlling for the recom-
mendation and earnings forecast revisions, average abnormal returns are signif-
icantly associated with the proxy for unexpected revision in target price (slope
coe„cient ¼ 2.671 with t-statistic ¼ 27.0). We also ¢nd no reliable relationship be-
tween abnormal returns and the expected revision in target price.This ¢nding is
consistent with the view that investors understand the long-term dynamics docu-
mented in Section III and thus are able to anticipate some of the analysts™ revi-
The association that we estimate between unexpected target price revisions
and event-day abnormal returns implicitly imposes the restriction that market
participants react symmetrically to unexpected revisions in target prices, irre-
spective of the current levels of target and market prices. We now relax this re-
striction by constructing four alternative measures of unexpected target price
revisions that condition on the magnitude of the pre-event ratio of target-to-mar-
ket price relative to the estimate of the ¢rm™s long-term relation. Speci¢cally, sup-
pose that at the time of the announcement, the pre-event target price to market
price ratio is 1.25 and the current estimate of the long-term ratio is 1.20. If the
target price revision was away from the long-term ratio of 1.20, we classify it as
˜˜Unexpected DTP/P above/away.™™A target price revision toward the long-term ra-
tio is classi¢ed as ˜˜Unexpected DTP/P above/towards.™™ The remaining two vari-
ables, unexpected target price revisions when the pre-event target price to
market price ratio is lower than the estimate of long-term ratio, are de¢ned simi-
The column labeled ˜˜Model II™™ in Table VII provides the regression results. We
¢nd that in cases where the pre-event ratio of target-to-market prices is below the
long-term relation, unexpected target price revisions away from the long-term
relation are associated with larger negative abnormal returns than in any of
the other cases (Z3 ¼ 6.864 with t-statistic ¼ 20.1). Indeed, the latter abnormal re-
turn is nearly twice as high as in the case in which the pre-event ratio of target-to-
market prices is above the long-term relation and the unexpected target price re-
vision is away from the long-term relation cases (Z1 ¼ 2.712 with t-statistic ¼ 17.7).
The p-value for the hypothesis that the previous two reactions are equal indicates
that we can reject this null. Finally, it can be seen that unlike market responses
to unexpected target price revisions away from the long-term relation, target
price revisions toward the long-term relation are economically and statistically
1962 The Journal of Finance

Informativeness of Target Prices Based on the Cointegration Regression
The sample contains all target price announcements between January 1997 and December 1999.
The table reports regression results in which the dependent variable is the market-adjusted buy-
and-hold abnormal returns around target price announcements. The independent variables are
indicator variables for analysts™ recommendation revisions, earnings forecast revisions, and ex-
pected and unexpected target price revisions.The indicator variables assume the value 1 for the
relevant recommendation revision and 0 otherwise. The recommendation categories are up-
grades, downgrades, and reiterations. Abnormal returns are computed as the di¡erence be-
tween the ¢rm buy-and-hold return and the buy-and-hold return on the NYSE/AMEX/Nasdaq
value-weighted index over the period beginning two days prior to and ending two days subse-
quent to the target price announcement. Earnings forecast revision, denoted DF/P, is computed
as the percentage change in the brokerage house current and prior annual earnings forecast
scaled by preannouncement stock price. Expected target price revisions for each ¢rm and event
are constructed as follows.We ¢rst estimate a one-week-ahead forecast of the consensus target
prices using the sample information that would have been available to investors prior to the
release of the analyst report. We require a minimum of 10 weekly observations to ¢t the cointe-
gration regression. The di¡erence between the regression forecast and the preannouncement
consensus target price, denoted (Expected DTP/P), serves as a proxy for the expected consensus
target price revision. Unexpected target price revision, denoted (Unexpected DTP/P), is the dif-
ference between the announced and expected target price. Both the expected and unexpected
target price variables are scaled by the event-¢rm™s market price two days prior to the event.We
winsorize all variables at the 1st and 99th percentiles to mitigate the possible e¡ect of extreme
observations. In addition, we verify that regression results are not sensitive to in£uential ob-
servations. The number of observations in each regression is 43,660.

Variable Model I Model II

a1 (Recommendation upgrades) 2.120 2.171
17.8 18.5
À 2.188 À 1.887
a2 (Recommendation downgrades)
À 14.0 À 12.9
À 0.070
a3 (Recommendation reiterations) 0.162
1.9 3.6
b (DF/P) 2.153 2.173
29.4 30.7
g (Expected DTP/P) 2.326
Z (Unexpected DTP/P) 2.671
Z1 (Unexpected DTP/P above/away) 2.712
Z2 (Unexpected DTP/P above/towards) 1.288
Z3 (Unexpected DTP/P below/away) 6.864
Z4 (Unexpected DTP/P below/towards) 1.494


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