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1.231 0.429 88.0%
À 0.31 3.22 À 0.76
15.54 4.42
Recommendation reiterations
0.221 À 0.110
All target price revisions 0.094 1.129 0.686 98.7%
5.34 À 3.30
0.70 38.62 18.86
0.780 À 0.152 À 0.009
Most favorable target price revisions 0.478 1.161 99.0%
17.33 À 3.08 À 0.28
3.07 33.07
Least favorable target price revisions À 0.080 0.216 À 0.311
1.232 0.690 95.4%
À 0.31 2.74 À 4.87
22.26 9.48
Recommendation downgrades
À 0.325 0.275 À 0.132
All target price revisions 1.179 0.471 93.7%
À 1.32 3.69 À 2.35
21.44 6.95
Most favorable target price revisions À 0.297 1.141 0.442 0.244 0.103 91.1%
À 0.99 17.16 5.36 2.71 1.48
Least favorable target price revisions À 0.354 0.219 À 0.538
1.332 0.614 90.7%
À 0.94 1.93 À 6.29
15.86 5.94



results reported in Table IV, we ¢nd no evidence of abnormal postevent return for
all three recommendation revision categories.
Next, within each recommendation category, we present regression results for
portfolios in which we condition on the magnitude of the target price revision at
the time of the portfolio formation. Consider ¢rst recommendation upgrades. It
can be seen that ¢rms with the highest target price revision tend to comove with
1954 The Journal of Finance

the returns of small growth ¢rms, and that the portfolio intercept, 0.799 percent,
is large both economically and statistically.When we consider ¢rms in the lowest
tercile of target price revisions, we ¢nd that the portfolio covaries with the re-
turns of small, value ¢rms but ¢nd no evidence of abnormal performance. This
evidence is consistent with the view that over our sample period small growth
¢rms exhibited strong price appreciation whereas value stocks in general per-
formed poorly.
In the case of recommendation reiterations, we still ¢nd strong evidence of ab-
normal performance for the high target price revision.The other portfolios in this
case yield insigni¢cant estimates of abnormal returns. Much like with recom-
mendation upgrades, it can be seen that the high target price revision portfolio
tends to comove with the returns of small, low book-to-market ¢rms, while the
lowest target price revision portfolio returns covary with the returns of small
value ¢rms. Finally, the evidence within recommendation downgrades is consis-
tent with the event-time analysis, with insigni¢cant intercepts for both highest
and lowest target price revisions. Interestingly, the estimated factor loadings for
¢rms with a recommendation downgrade indicate that the key di¡erence
between ¢rms with the lowest and highest target price is their exposure to the
momentum factor. While both sets of ¢rms comove with the returns of small
growth ¢rms, those that receive an upward (downwards) revision to their target
price behave like other ˜˜winner™™ (˜˜loser™™) ¢rms.19
The abnormal return evidence presented in this section is consistent both with
an irrational ˜˜underreaction™™ interpretation (e.g., Jegadeesh and Titman (1993))
and with rational learning (Brav and Heaton (2002)). We caution, however, that
our three-year sample period coincides with the highs of the bull market in the
United States, which might be viewed historically as an unusual period. There-
fore, an alternative viable view is that the evidence of postevent returns is unique
to this period and unlikely to persist as more data becomes available.We leave for
future research a detailed study of these explanations.



III. Modeling the Long-term Relation between Market and Target Prices
We extend the analysis in Section II with an investigation of the long-term dy-
namics of the stock and target prices. Our objectives are to further investigate
the extent to which analyst price targets are systematically related to ¢rms™ fun-
damental values and to quantify the low-frequency dynamics of these price series.
Because both sets of prices are nonstationary, we employ a cointegration
framework and estimate the linear combination of the price series that is station-
ary. This linear combination is termed as the price system™s ˜˜long-term relation™™
19
In additional unreported tests, we repeat the regression analysis but with value-weighted
portfolio returns. Speci¢cally, we construct our portfolios as explained above but weigh the
component monthly returns with the lagged market capitalization of the constituent ¢rms. We
¢nd that across the three recommendation classi¢cations, the pattern of positive (negative)
performance subsequent to a high (low) revision in the target price is qualitatively the same
as in Table V.
An Empirical Analysis of Analysts™ Target Prices 1955

and is parameterized as the ratio of target and market prices.20 Since target
prices are predominantly one-year-ahead prices, this ratio can be interpreted as
the analysts™estimate of the ¢rm™s ex ante return.
Cointegration also implies that any price deviations from the long-term ratio
are stationary as well.That is, if on a given date the ratio of the two prices is equal
to the long-term relation, then a shock to any of the variables will lead to a price
path that will settle back to the long-term relation.We analyze the price system™s
reaction to deviations from this long-term ratio by examining which price series
corrects to the long-term relation, once the system has been perturbed away.That
is, are analysts the ones reacting to a deviation from the long-term relation by
adjusting their target prices, or, do stock prices contribute towards most of the
adjustment?
Our empirical analysis is conducted on a time series of individual ¢rms™ weekly
stock prices and consensus target prices. Consensus target price is calculated as
the average target price across all brokerage houses. Target prices outstanding
for more than 90 days are excluded from the consensus.We choose a weekly inter-
val to avoid microstructure problems associated with daily data. Given the long-
term nature of the cointegration analysis, we require each ¢rm to have a mini-
mum of 500 trading days (approximately 104 consecutive weeks) with continuous
stock and target price data.21 The ¢nal sample consists of 900 ¢rms.
To set the stage for our methodological approach and to build intuition for the
full-sample analysis, we begin by examining the joint price behavior of a single
¢rm, IBM.

A. Basic Setup and Application to IBM™s Stock and Target Price Behavior
Figure 2 depicts the time series of weekly market prices for IBM over our sam-
ple period, January 1997 through December 1999.We also plot the weekly consen-
sus target price and the ratio of target to market price, denoted TP/P. Inspection
of this ¢gure provides a key insight. While both price series are nonstationary
(we are unable to reject the null of nonstationarity with an augmented Dickey^
Fuller test), it is evident that market and target prices share a long-run common
20
Our use of the term ˜˜long-term relation™™ di¡ers from that used in the traditional cointe-
gration literature (Engle and Granger (1987)) in which the term ˜˜long-term equilibrium™™ has
been employed. The motivation for the use of the latter term is the idea that when two coin-
tegrated economic variables drift apart from this equilibrium, economic forces eventually
drive them back to it (e.g., Lee, Myers, and Swaminathan (1999), Hasbrouck (2002)). Finally,
as Campbell and Shiller (1988) argue, such an ˜˜equilibrium™™ can occur simply because one
variable is a rational expectation of the future value of another variable that follows an inte-
grated process. Since target prices are, by de¢nition, analysts™ forecasts of future prices, we
have therefore chosen to avoid using the term ˜˜long-term equilibrium™™ in our analysis in favor
of ˜˜long-term relation.™™
21
While the choice of a two-year minimum period reduces the number of ¢rms that we can
study, it is a necessary requirement, as we are interested in studying the long-term dynamics
of target and market prices. We have replicated the cointegration analysis using a minimum of
250 trading days and obtained similar results. In addition, the analysis was conducted on
size-sorted portfolios containing all ¢rms in our database. Our conclusions regarding the dy-
namics of market and target prices remain unchanged.
1956 The Journal of Finance

160 2

1.8
140
1.6
Stock Price and Target Price ($)




IBM's Target Price/ Price Ratio
120
1.4




Target Price/Price
100
1.2
IBM's Consensus Target Price
80 1

0.8
60
0.6
40
0.4
IBM's Stock Price
20
0.2

0 0
970106
970218
970331
970512
970623
970804
970915
971027
971208
980120
980302
980413
980526
980706
980817
980928
981109
981221
990201
990315
990426
990607
990719
990830
991011
991122
Date

Figure 2. IBM™s weekly stock price, target price, and target price-to-price ratio.
This ¢gure depicts weekly market price and consensus target price for IBM. The market
prices are from CRSP and the weekly consensus target price is the average of all outstand-
ing target prices issued over the preceding 90 days. All target prices are from the First Call
database. We also plot the ratio of the target price to market price at each point in time,
denoted by TP/P. The vertical axis on the left-hand side corresponds to the price series
while the axis on the right-hand side corresponds to the TP/P ratio.


relation. This association is captured by our third variable, TP/P. It can be seen
that target prices, which are forward looking, are consistently higher than cur-
rent market prices, but that the ratio of the two price series £uctuates about a
common value at approximately 1.2 (see the right-hand-side vertical axis). When
either price series deviates from this ratio, the system tends to revert back to this
value over time.
Using a cointegration approach (Engle and Granger (1987)), we provide direct
evidence regarding both the long-term relation and the manner with which these
prices correct toward this long-term relation.To do that, we assume that analysts™
ex ante expected return, and therefore the mean of the TP/P ratio, is constant
over our sample period. We denote the time t target price by tpt and the market
price by pt.The (2 ‚ 1) vector xt has elements tpt and pt. Both variables are assumed
integrated of order one, that is, stationary after ¢rst di¡erencing. Cointegration
of the two price series means that there is a (2 ‚ 1) vector b such that the following
linear relationship holds: b 0 xt ¼ 0.22



22
We have conducted Johansen™s cointegration rank test for each of the 900 price series that
we later study in Section III.C and ¢nd that we are able to reject the null of no cointegration
essentially for all ¢rms.
An Empirical Analysis of Analysts™ Target Prices 1957

We begin by writing the price system in an error correction form:
X
p
Dxt ¼ Pi DxtÀi þPxtÀ1 þ et ; 8t ¼ 1; . . . ; T; °3Þ
i¼1

where Dxt À i is a (2 ‚ 1) vector of ¢rst di¡erences lagged i periods and etBN(0,O),
assumed independent over time. The matrices Pi and O are both (2 ‚ 2), and the
latter is assumed positive de¢nite. P is the (2 ‚ 2) long-run impact matrix that con-
tains the cointegrating vector. Inclusion of this term in this vector autoregressive
regression (VAR) follows from the Granger Representation Theorem. P rank is™s
equal to the number of cointegrating relations which in our simple case is just one.
Following earlier literature, we parameterize P as follows:
P ¼ ab0 ; °4Þ
where both a and b are (2 ‚ 1) vectors. The vector b contains the cointegrating
coe„cients, and for identi¢cation we normalize its ¢rst element to À 1, that is,
b( À 1,b). For example, if, on average, analysts forecast that target prices are 20
percent higher than current market prices, then b ¼ [À 1 1.2]. The vector a is the
vector of weights in theVAR regression.This vector can be interpreted as a vector
of ˜˜adjustment coe„cients™™; that is, the elements in a allow us to quantify how
target prices and market prices react to past deviations from the long-term rela-
tion. By obtaining estimates of the elements in a, we can quantify the way in
which the two time series contribute to the correction of the system back to the
long-term relation.

B. Cointegration Results for IBM
We present the cointegration results in Panel A of TableVI.We focus our atten-
tion on b, the parameter capturing the long-run ratio of target prices relative to
market prices, as well as on the (2 ‚ 1) vector a, capturing the response coe„-
cients of each price variable to deviations from the long-run relation. The long-
term relation for IBM is 1.23, indicating that, ex ante, analysts expected that
IBM™s annual return would be 23 percent.
The estimates of the vector of response coe„cients, a, provide interesting evi-
dence on the manner with which target and market prices adjust to the long-term
relation. The target price response, atp, is large and positive (0.17, with a t-
statistic ¼ 8.15), while the market price response, amarket, is statistically indistin-
guishable from zero. This ¢nding can be interpreted as follows. Suppose market
and target prices are currently 100 and 123 dollars, respectively, and that a 4 -dol-

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