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Analyst recommendations, traders’ beliefs, and rational speculation
Karthik Balakrishnan1
Catherine Schrand1
Rahul Vashishtha2
September 2012
Rational speculative trading has been offered as an explanation for stock price bubbles, defined
as a deviation of stock price from expected intrinsic value. When traders anticipate profits based
on higher order beliefs about traders’ stock price valuations, the resulting speculative trading
sustains a bubble. This paper investigates observable signals of such beliefs. For the technology
bubble in 2000, we show a strong positive relation between a concentration in a tech firm’s
analyst buy recommendations and bubble continuation. We extend this result to a broad sample
of firms from 1994-2009. We show that analyst buy recommendation concentration is associated
with out of sample returns that are consistent with a rational speculative “bubble” in the
individual firms’ stock prices.
Keywords: Mispricing, Technology Bubble, Higher Order Beliefs, Disagreement of Opinion, Analyst
recommendation
JEL Classification: G10, G20
1 The Wharton School, University of Pennsylvania, 1300 SH-DH, Philadelphia, PA 19104
2 The Fuqua School of Business, Duke University, 100 Fuqua Drive, Durham, NC 27708
The authors thank Nemit Shroff, C.S. Agnes Cheng, Bill Mayew, audiences at Chicago, Georgetown, Michigan,
Utah, Vanderbilt, Yale, the 2010 FEA conference, the 2011 NYU summer camp, and the 2011 FARS meeting for
helpful comments and Jessica Tung for research assistance. We are especially grateful to Lily Fang and Ayako
Yasuda for sharing their classifications of All-star analysts.
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A stock price “bubble” is defined as a deviation of stock price from the expected intrinsic
value of the equity. Asset pricing models with short-horizon traders can predict bubbles as a
rational equilibrium. Short-horizon traders with preferences over intermediate stock price form
beliefs about the average opinion of next period’s stock price. Expected price is not only a
function of a firm’s expected asset payoffs (i.e., expected intrinsic value), but also a function of
each trader’s beliefs about how other traders will trade the firm’s stock, which in turn depends on
other traders’ beliefs, and so on. When traders believe that the stock price in the next period will
exceed the current stock price, they anticipate expected profits from trading and their collective
trading based on these beliefs will sustain a bubble in the stock price. Intuitively, “[I]f the reason
that the price is high today is only because investors believe that the selling price is high
tomorrow ― when ‘fundamental’ factors do not seem to justify such a price ― then a bubble
exists.” (Stiglitz, 1990). We refer to bubbles of this type – bubbles explained by models of
traders with heterogeneous higher order beliefs about anticipated speculative profits – as
“rational speculative” bubbles.1
Several recent studies document asset trading patterns that support rational speculative
trading as an explanation for bubbles (Brunnermeier and Nagel, 2004; Xiong and Yu, 2011;
Griffin, Harris, Shu and Topaloglu, 2011),2 but none contain tests that aim at identifying
observable mechanisms that are associated with beliefs about anticipated profits, which are at the
heart of these rational speculative trading models. This paper aims to fill the gap in the literature
1 Heterogeneous beliefs are a necessary element of these models such that trading decision rules are not common
knowledge (Biais and Bossaerts, 1998; Banerjee et al., 2009). The notion that disagreement of opinion in traders’
higher order beliefs about stock valuation can explain bubbles is not new (e.g., Harrison and Kreps, 1978).
References to rational speculative trading date back to Keynes’ analogy of stock markets to beauty contests.
However, more recent models have increased the appeal of rational speculation models by identifying specific
market frictions that rationalize disagreement of opinion (e.g., Morris, 1996; Daniel et al., 2001; Abreu and
Brunnermeier, 2002; and Hong, Scheinkman, and Xiong, 2008). 2 Studies using experimental markets also show that bubbles form even when participants have perfect information
about intrinsic or terminal value (e.g., Smith, Suchanek, and Williams, 1988; Noussair, Robin, and Ruffieux, 2001;
Hirota and Sunder, 2007; and Bhojraj, Bloomfield, and Taylor, 2009).
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by investigating the observable mechanisms, if any, associated with beliefs. This investigation is
important given the increased attention on the role of asset-price bubbles in exacerbating
economic instability during the 2008-2009 crisis and the several regulatory debates surrounding
bubbles (e.g., Malkiel, 2010). Further, recent literature provides evidence that stock bubbles can
result in misallocation of capital (Polk and Sapienza, 2009; Gilchrist, Himmelberg, and
Huberman, 2005).
Our approach to identifying observable signals associated with traders’ beliefs is to
identify observable signals associated with bubbles. If we observe a relation between a bubble
and a signal, and the bubble is in fact a rational speculative bubble due to higher order beliefs in
anticipated speculative profits, then we can infer a relation between the signal and beliefs.
Documenting an association between an observable signal and beliefs does not imply a causal
relation. We make some attempts to identify causality, although we recognize that establishing
causality definitively is not possible.
Our study contains two distinct elements. The first analysis is an in-sample investigation
of signals associated with the tech bubble that peaked in March 2000. Within this relatively
homogeneous industry, stock prices for one set of tech firms were flat, while the stock prices of
other tech firms experienced a significant increase followed by a sudden decline (see Figure 1).
The tech bubble has been recognized as a rational speculative bubble based on traders’ beliefs
about anticipated profits (Brunnermeier and Nagel, 2004; Griffin, Harris, Shu and Topaloglu,
2011),3 which is important in our study so that we can infer that a relation between an observable
signal and the bubble implies a relation between the signal and traders’ higher order beliefs about
3 See Lewellen (2003), Abreu and Brunnermeier (2003), Hong, Scheinkman, and Xiong (2008), and Greenwood and
Nagel (2009), for studies that consider the tech bubble to be a “bubble” in that price deviated from expected
fundamental value. Pastor and Veronesi (2006) argue that the tech bubble was not a bubble based on reasonable
estimates of implied uncertainty, but Ofek and Richardson (2003) argue that it was based on unreasonable estimates
of implied growth rates. See also Malkiel (2010) for an assertion that the tech bubble represented a bubble.
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speculative profits. We use a difference-in-differences design to measure changes in the
association between observable signals and the probability that a tech firm has a bubble in its
stock price.
The signal with the strongest association with the tech bubble is the concentration in
analyst buy recommendations (BUY%). We interpret the association between BUY% and the
tech bubble as evidence that analyst recommendation concentration is associated with traders’
higher order beliefs about speculative profits. The results are robust to controls for fundamental
news that could be correlated with analyst recommendations. Other observable signals that we
consider, but that are not associated with the likelihood of bubble development, are concentration
in analyst long term growth (LTG) and earnings forecasts and the incidence of management
guidance.
For several reasons it is not surprising that a higher concentration in analyst
recommendations (BUY%) is associated with the tech bubble, and hence with beliefs about
speculative profits. First, models that predict rational speculative bubbles suggest that traders are
likely to form beliefs about average opinion based on signals that are public and visible because
traders expect that other traders could use these signals as well (Froot, Scharfstein, and Stein,
1992; Abreu and Brunnermeier, 2002; Morris and Shin, 2002; Allen, Morris and Shin, 2006;
Gao, 2008). Analysts’ announcements are public and visible, and empirical evidence suggests
that investors view them as a credible source of information (e.g., Stickel, 1991; Gleason and
Lee, 2003). Second, the analyst’s recommendation reflects the analyst’s expectations about next
period price, which should be a more salient signal to traders about expected trading profits than
other analyst outputs such as earnings forecasts. Traders may view analyst recommendations as
biased, however, which would adversely affect the extent to analyst recommendation
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concentration is associated with beliefs. Finally, the concentration in analyst outputs should
influence traders’ perceptions of the average opinion of other traders more than the dispersion,
which instead measures the variance in analysts’ opinions.
Our second analysis takes what we learn from the tech bubble to examine if analyst
recommendation concentration (BUY%) is associated with bubbles more generally in a broad
sample of firms spanning all industries from 1994 through 2009. We use a traditional portfolio
methodology to investigate the relation between changes in analyst recommendation
concentration in month t and future returns, conditional on the direction of the “news” in month
t, where news is based on analyst earnings forecast revisions. Predictable post-news returns are
generally described as evidence of “mispricing” rather than a “bubble” because of the magnitude,
but the definition is the same: a deviation of price from expected intrinsic value.4
Portfolios that include firms with extreme increases in buy recommendation
concentration in month t have one month ahead (t+1) returns of approximately 4% following bad
news and 4.8% following good news. These returns are significantly higher than the one month
ahead returns of approximately 1% for portfolios with moderate or less extreme increases in
BUY%, which are in line with unconditional levels of post-news mispricing (Zhang, 2006). The
significantly greater returns for portfolios with extreme increases in recommendation
concentration are not explained by differences across portfolios in analyst earnings forecast
dispersion as a measure of information uncertainty, average stock liquidity, or firm
characteristics including size and leverage.
The observed association between the signal – analyst recommendation concentration –
and month t+1 returns is not sufficient to infer an association between the signal and traders’
higher order beliefs. To make this inference, we must also show that the month t+1 returns
4 We use the terms bubble and mispricing interchangeably.
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represent a rational speculative bubble. To this end, we provide evidence that the returns are
temporary, lasting only two months, and that the “bubble” portfolios experience a significant
increase in the incidence of crashes, measured by stock price skewness, in months t+2 and t+3.
We also provide evidence that the differences between the month t+1 returns conditional on bad
news and good news are symmetric and that the results hold across portfolios conditioned on
breadth of ownership as a measure of investor interest in shorting a firm’s stock (Chen, Hong,
and Stein, 2002). These two findings suggest that short sale constraints do not explain the month
t+1 returns, which is the most credible alternative explanation for the mispricing. Taken
together, these four analyses are consistent with an interpretation of the month t+1 returns as a
rational speculative bubble, which in turn supports interpreting the association between the
signal and the mispricing as evidence that the signal is associated with traders’ higher order
beliefs about speculative profits.
The evidence from the broad sample analysis complements the tech sample analysis on
several dimensions. First, it is out of sample evidence that analyst recommendation
concentration predicts next period bubbles. Next, the analyses suggest that the association
between analyst buy recommendation concentration and traders’ beliefs is not restricted to the
tech industry where analyst recommendations may matter more because earnings-related signals
are less informative. Further, an association in the broad sample setting suggests that the rational
speculative profit explanation for bubbles is applicable to less egregious magnitudes of
mispricing as well.
We also explore observable signals associated with the crash of the tech bubble. Short-
window tests of returns for the tech bubble firms during the crash period show that firm-specific
analyst downgrades on day t have explanatory power for day t abnormal returns as well as for the
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portion of a firm’s total market value lost during the crash period on day t. The abnormal returns
associated with a downgrade are approximately twice the magnitude when the downgrade is
accompanied by an earnings forecast. The analyst downgrade has the most significant
explanatory power compared to ten other signals we consider including various forms of analyst
forecasts and recommendation revisions, earnings announcements, management’s earnings
guidance, measures of media coverage, firm-specific insider sales, and industry-level measures
of lockup expirations and insider selling.
The finding that downgrades are associated with a revision in beliefs provides only
indirect evidence related to our primary research question of whether there are observable signals
associated with beliefs during bubble continuation. The signal that causes a crash need not be a
revision in the signal that shapes beliefs during bubble development (Abreu and Brunnermeier,
2002 and 2003; Allen, Morris, and Shin, 2006). Nonetheless, this finding is sensible given the
in-sample evidence of an association between analyst recommendation concentration and the
bubble evolution.
These crash period findings also extend existing broad sample studies that document that
analyst recommendation changes are associated with stock returns on average (e.g., Stickel,
1995; Womack, 1996; Barber, Lehavy, McNichols and Trueman, 2001; Jegadeesh, Kim, Krische
and Lee, 2004) in two ways. First, because we examine only firms with a stock price bubble, our
evidence shows that recommendation downgrades are associated with the crash of a bubble,
while the broad sample evidence cannot distinguish whether the downgrade is a shock to
fundamentals or to beliefs. Second, the relatively small tech sample and the short duration of the
crash period allows us to provide evidence on the relevance of downgrades incremental to a
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variety of potential news signals including earnings announcements, analyst earnings forecasts,
insider sales, management forecasts, and media coverage.
In both the tech bubble setting and the broad sample, we attempt to address the question
of whether the association between analyst recommendation concentration and beliefs implies
that analysts cause traders to anticipate profits or whether the analysts are making
recommendations that reflect traders’ beliefs. In the tech bubble setting, we find that the relation
between downgrades and short-window crash returns is strongest when the downgrade is made
by an All-star analyst and on the same day as media coverage. The All-star results could be
interpreted in two ways. Because All-stars are more credible, their downgrades are more likely
to cause traders to revise their beliefs, thus supporting causality. Or, All-stars are better at
predicting price, including the effects of rational speculative trading, thus supporting reflection.
The media results are more suggestive of causality because it is difficult to argue that the media
successfully chooses to cover the analyst downgrades that are better predictors of average
opinion of price. In the broad sample analysis, conditioning on All-star status and analyst
experience provides some evidence consistent with causality, but the evidence is quite weak as
expected in this low power setting.
2. Tech bubble analysis
2.1 Observable signals and bubble continuation in the tech bubble
For the difference-in-differences analysis, we designate technology firms as either a
bubble firm (treatment group) or a non-bubble firm (control group) based on the firm’s price to
sales ratio (P/S) following the procedures in Brunnermeier and Nagel (2004) and Greenwood and
Nagel (2009). At the end of February 2000, we rank all Nasdaq stocks into quintiles based on
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the P/S ratio using end-of-month market capitalization and sales figures that are lagged at least
six months.5 A firm that is in the highest P/S quintile of Nasdaq firms at the end of February
2000 and that is classified as “High-Technology” in the Fama-French 10 industries is a bubble
firm; a High-Technology firm in the lowest two quintiles is a non-bubble (or control) firm. This
definition results in a sample of 217 bubble firms and 121 non-bubble technology firms.
Between January 1996 and the peak of the tech bubble in March 2001, we distinguish a
pre-bubble period and a bubble period. Figure 1 illustrates the value weighted cumulative return
(rebalanced every month) for the tech firms within each P/S quintile. During the pre-bubble
period from January 1996 through December 1997, the bubble and control firms experienced
similar stock returns. During the bubble period from January 1998 through February 2000, the
bubble firms experienced significant appreciation in their stock prices relative to the control
firms. The primary difference-in-differences test compares the explanatory power of observable
proposed signal for the probability that the tech firm is a bubble firm during the bubble period to
the pre-bubble period. The pre-bubble period serves as a benchmark in the difference-in-
differences analysis to mitigate concerns that our findings are driven by unobserved differences
between bubble and non-bubble firms (Bertrand, Duflo, and Mullainathan, 2004).
We also delineate a post-peak but pre-crash period from March 2000 through August
2000, in which the bubble firms experienced a minor crash and then a rebound (transition
period). A transition period is consistent with the prediction of a partial price adjustment, which
can occur in the presence of liquidity traders because a negative price adjustment is not a de
facto signal that coordinated trading is ending the mispricing (Abreu and Brunnermeier, 2002).
Finally, we delineate a crash period from September 2000 through October 2001 during which
5 February 2000 is the closest month ending prior to March 10, 2000, which is believed to be the date when the tech
bubble reached its peak.
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the bubble firms experienced a significant decline in equity price for use in the short-window
tests of coordinating events. We make no predictions about the observable signals during the
transition and crash periods.
To produce the estimates for the difference-in-differences tests, we estimate the following
probit model of the probability that a tech firm experiences a bubble as a function of a proposed
signal (SIGNAL):
(1) )MonthControlSIGNAL(f)BUBFIRMPr( ittc
ictcp iptpi 4
1
where BUBFIRM equals one if firm i is a bubble firm and zero otherwise and SIGNAL equals the
value of the proposed signal if the observation is during period p and equals zero otherwise. The
coefficient on SIGNAL can vary across the four previously defined periods (p) between January
1996 and October 2001. A positive coefficient estimate during the bubble period indicates an
association between the proposed signal and the likelihood of a bubble in the firm’s stock price.
A significant difference between the coefficient on a signal during the bubble period and the
coefficient during the pre-bubble period indicates an association with the bubble that is unlikely
to be caused by unobservable differences between bubble and control firms.
We consider six candidate signals that could be correlated with traders’ higher order
beliefs about anticipated speculative profits. The primary candidate is analyst recommendation
concentration. The concentration in analyst buy recommendations (BUY%) for month t is
measured as the percentage of recommendations that were “buy” or “strong buy” at the end of
month t, constructed using the I/B/E/S database, which collects the recommendations from
contributors and assigns them one of the following five classifications: (1) strong buy, (2) buy,
(3) hold, (4) sell, or (5) strong sell.
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Analyst recommendation concentration is the primary candidate as a signal because, as
noted in the introduction, analysts’ outputs are visible, the recommendation is about stock price,
and concentration is a reasonable sign of average opinion. Thus, analyst recommendation
concentration exhibits characteristics of precedence and salience, which are important features of
sources of beliefs.6 At the same time, however, analyst recommendations are a function of
analyst incentives, which could adversely affect the extent to which analyst recommendation
concentration is associated with beliefs. If traders believe analysts herd, for example, and
believe other traders share this belief, the salience of concentration in analyst recommendations
would be diminished.7 As another example, if traders believe that sell-side analysts are
optimistically biased to sell stocks to maintain investment banking relations, and if other traders
share this belief, then the importance of analyst recommendations diminishes. Even absent any
explicit incentives on the part of analysts to sell stocks, well-intentioned analysts have incentives
to issue optimistic recommendations. Hong, Scheinkman, and Xiong (2008) argue that analysts
have incentives to issue optimistic forecasts to signal that they are tech-savvy and attract future
advisees. Unfortunately, naive investors do not understand the incentives of advisors to inflate
their forecasts, and consequently asset prices are biased upward.
Table 1, Panel A reports that the bubble firms have a higher mean and median percentage
of buy recommendations (BUY%) in all four periods. The most significant difference is in the
transition period prior to the crash. The median BUY% for a bubble firm is 95% compared to
33% for the non-bubble firms. In the probit analysis, we also separately analyze the percentage
6 See Sunder (2002) for an excellent discussion of common knowledge and factors affecting beliefs including
precedence and salience. 7 Empirical evidence on herding is mixed and context specific. Chevalier and Ellison (1999), Hong, Kubik, and
Solomon (2000), Clement and Tse (2005) and Jegadeesh and Kim (2010) present results that suggest conditions in
which analysts tend to herd. Zitzewitz (2001), Bernhardt, Campello, and Kutsoati (2006), and Chen and Jiang
(2006) find that analysts “antiherd,” or that they issue forecasts that are farther away from the consensus.
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of recommendations that were upgraded or downgraded during month t (UP% and DOWN%) as
related measures of analyst recommendation concentration.
The second and third proposed signals are the concentrations in analysts’ LTG forecasts
and earnings forecasts (HILTGFCST% and HIEARNFCST%), measured as the proportion of
forecasts that lie in the top 40% of the range of the respective forecasts. Because earnings and
growth are inputs to a valuation function and not a direct reflection of the analyst’s opinion of
whether the price is right, ex ante we expect these variables to be less salient about the average
opinion about price than BUY% (Francis and Soffer, 1997), and hence less likely associated with
the bubble. We consider concentrations in earnings forecasts as an alternative, however, because
forecasts are a common analyst output. We consider concentrations in LTG forecasts based on
evidence that recommendations are consistent with valuation models using analysts’ LTG
forecasts (Bradshaw, 2004). In addition, for tech firms, LTG forecasts may capture
fundamentals better than earnings (e.g., Amir and Lev, 1996; Collins, Maydew, and Weiss, 1997;
Lev and Zarowin, 1999; Barron, Byard, Kile, and Riedl, 2002). We require at least five forecasts
(from the I/B/E/S database) to compute a firm-month observation. Table 1, Panels B and C
report that both concentration measures are significantly lower for the bubble firms than for the
non-bubble firms in all four periods, which is opposite to the relation for BUY%. The most
significant differences occur in the transition and crash periods.
The fourth signal we consider is long-horizon earnings guidance issued by management.
CIG_DUM is an indicator variable that equals one if a manager issues long-horizon earnings
guidance during month t. The distinction of this signal is that the guidance emanates from
management rather than analysts, although we make no prediction on whether this feature makes
it more or less likely that this signal will be associated with traders’ beliefs about future stock
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price. Management forecasts are obtained from First Call Company Issued Guidelines database.
Following Bergman and Roychowdhury (2008), any management forecast issued more than 90
days before the estimate period end date is classified as a long-horizon forecast. Table 1, Panel
D shows no significant difference in guidance during the bubble period. However, the bubble
firms are more likely to issue guidance in the crash period. The difference is due to a relative
increase in the issuance of neutral and walkdown guidance.8
The final two signals we consider are the dispersion in analyst LTG and earnings
forecasts, measured as the standard deviation in analysts’ long term growth forecasts (DISPLTG)
and one year ahead earnings forecasts (DISPEARN) scaled by the mean forecast. The mean and
median dispersion in LTG forecasts and earnings forecasts are lower for bubble firms in all four
periods. The most significant differences between bubble and non-bubble firms are during the
crash period (Table 1, Panels E and F).
Table 2 presents the probit analysis of the bubble and non-bubble firms across the four
periods. The column heading specifies the measure of SIGNAL included in the model. We
estimate equation (1) on a panel of firm-month observations with a maximum of 5,060
observations during the pre-bubble period, 7,293 observations during the bubble period, 1,558
observations during the transition period, and 3,017 observations during the crash period.
Standard errors are obtained by clustering at the firm level. The model includes monthly fixed
effects (Month). The model also includes a vector of control variables (Control) that have been
commonly used in prior literature on determinants of the cross-section of stock returns and stock
price anomalies (e.g., Fama and French, 1993; Hong, Lim, and Stein, 2000; and Zhang, 2006).
The control variables include measures of firm size, analyst following, firm age, leverage, capital
8 In order to have comparable samples across all proposed signals, CIG_DUM is set to missing if the firm does not
have an analyst recommendation.
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expenditures, R&D, intangibles-intensity, the book-to-market ratio, and return volatility. Table 2
provides details on the construction of the control variables.
The first row of Table 2 reports the coefficient estimates on each proposed signal in the
pre-bubble period as a benchmark for evaluating the results during the bubble period. The only
evidence that indicates that analysts might have identified the bubble firms during the pre-bubble
period is that the bubble firms experienced significantly fewer downgrades as measured by
DOWN%.
The results for the bubble period show the strongest association between the analyst
recommendation concentration signal and the likelihood that a tech firm has a price bubble
(Table 2, second row, columns (1) through (3)). The bubble firms have a higher percentage of
buy recommendations, and they experience more upgrades and fewer downgrades. Difference-
in-differences estimates in the bottom panel of the table show that relative to the pre-bubble
period, BUY% and UP% are significantly higher during the bubble period for bubble firms
relative to non-bubble firms. Results for the other proposed signals exhibit a less significant
association with the likelihood of a bubble. Column (4) shows no evidence that the
concentration in LTG forecasts during the bubble period was greater for the bubble firms, and
Column (6) shows no evidence that the incidence of management guidance during the bubble
period was greater for the bubble firms. Column (5) indicates that the concentration in high
earnings forecasts was higher for the bubble firms during the bubble period. However, the
bottom panel shows that the difference-in-differences between the pre-bubble and bubble periods
is not significant. Columns (7) and (8) report no association between the dispersion in analysts’
long term growth and earnings forecasts (DISPLTG and DISPEARN, respectively) during the
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bubble period and the probability that a tech firm experiences a bubble.9 Prior literature has
documented an association between dispersion in earnings forecasts and mispricing, albeit
motivated by different reasons (Diether, Malloy and Scherbina, 2002; Garfinkel and Sokobin,
2006; Zhang, 2006). In summary, among the proposed signals we examine, the results are most
consistent with a correlation between analyst buy recommendation concentration and traders’
beliefs of anticipated profits during the bubble period.10
We repeat the analysis in Table 2 estimating models that include analyst recommendation
concentration during the four periods and each of the other proposed signals during the four
periods to test for incremental explanatory power. As in Table 2, the only proposed signal
besides analyst recommendation concentration with explanatory power during the bubble period
is a high concentration in analyst earnings forecasts (untabulated), and it continues to be
insignificant in the difference-in-differences test. The important finding is that the positive
coefficient estimate on analyst recommendation concentration remains significant in all models.
Although the difference-in-differences analysis greatly mitigates concerns regarding
omitted correlated variables, it cannot definitively rule out the possibility that the differences in
BUY% between the bubble and control firms reflect differences in expected intrinsic values
between the two groups. To address this issue, we estimate an augmented version of model (1)
that includes two firm-level proxies for news about intrinsic value: the mean analyst estimate of
long term growth (ESTLTG) and the mean one year ahead analyst forecast of earnings scaled by
sales (ESTEARN1). Both variables are measured separately across the four periods for the
9 Estimating columns (1) through (6) with the smaller number of observations in columns (7) and (8) suggests that
the no-results finding in columns (7) and (8) is not due to lack of power associated with a smaller sample size. 10
One interesting pattern during the transition period (row 3, Table 2) is that the bubble firms have a significantly
greater percentage of downgrades relative to non-bubble firms, while at the same time the differences in the BUY%
and UP% between the bubble and non-bubble firms are even greater than in the bubble period. In addition, bubble
firms experienced a significantly greater decline in concentration in LTG and earnings forecasts from the bubble
period to the transition period. These results suggest that confusing signals about fundamentals can worsen traders’
inferences based on noisy returns about whether selling represents coordinated trading.
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bubble and non-bubble tech firms. Table 3 presents the results. Analyst recommendation
concentration has statistically significant incremental explanatory power in the model after
controlling for analysts’ expectations about intrinsic value.11 The patterns across the periods in
the coefficient estimates on BUY% are similar to the patterns in the corresponding probit models
in Table 2. The BUY% is significant in the bubble, transition, and crash periods, and the
difference-in-differences estimates are significant (untabulated). Thus, analyst recommendation
provides incremental explanatory power for the likelihood that a tech firm experiences a price
bubble over news about fundamentals.12
2.2 Analysis of coordinating events in the tech bubble
Abreu and Brunnermeier (2002) introduce the notion of a “coordinating” event or
“synchronizing” device that causes a rational speculative bubble to end. When an event causes a
revision in traders’ beliefs such that a critical mass of traders believes the stock is mispriced and
believes that a critical mass of traders share this belief and so on, rational speculative trading
models predict that traders will sell and the coordinated trading ends the bubble.
The purpose of our analysis of coordinating events is to provide an additional context for
interpreting the association between analyst recommendation concentration and bubble
development documented in the prior section. While a coordinating event could be payoff
irrelevant, such as a sunspot (Abreu and Brunnermeier, 2002 and 2003; Allen, Morris, and Shin,
2006), it seems reasonable to expect that if analyst recommendations are associated with beliefs,
then the coordinating event that causes a revision in beliefs would bear some relation to changes
in analyst recommendation concentration.
11
Results are similar when we exclude firms with negative earnings. 12
Variation in short sale restrictions across exchanges cannot explain our results as in Zhang, (2006). The sample
includes only Nasdaq stocks.
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We examine daily returns during only the crash period and only for firms that were
identified as bubble firms. We estimate two models of crash period returns that differ in the
specification of the dependent variable. In eqn. (2), we analyze daily abnormal returns:
10
1
it it t
k ikt c ict t it
k c
ABNRET DOWN TOTDOWN DOWN FCST
Coord Control Day
(2)
where itABNRET is the difference between firm i’s stock return for day t and the day t return on
its corresponding size decile benchmark portfolio formed on the basis of year-end market
capitalizations of all Nasdaq stocks. In eqn. (3), we model the fraction of equity market value
lost by firm i on each day t during the crash period:
ittc
ictck
iktk
titit
DayControlCoord
FCSTDOWNTOTDOWNDOWNTFRACMVELOS
10
1 (3)
where FRACMVELOSTit is the equity market value lost by firm i on day t divided by the total
market value lost by firm i over the entire crash period. FRACMVELOST is set to zero for days
the firm experienced an increase in the market value of equity. Model (2) is estimated using
ordinary least squares on a sample of 45,459 firm-day observations and model (3) is estimated
using a tobit specification on a sample of 45,385 firm-day observations for bubble firms during
the crash period. Equations (2) and (3) also include a vector of control variables measured in
levels (defined in Table 2) and daily fixed effects (Day). Standard errors are obtained by
clustering at the firm level.
Eqns. (2) and (3) include three proposed coordinating events related to changes in analyst
recommendations. DOWN is a dummy variable that equals one for firm i if it was downgraded
by at least one analyst on day t. TOTDOWN is the total number of downgrades experienced by
all tech bubble firms on day t. DOWN+FCST is a dummy variable that equals one when a
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downgrade is issued contemporaneously with an earnings forecast. The models also include a
vector of ten proposed coordinating events (Coord) unrelated to analyst forecasts: eight firm-
specific signals including various types of earnings announcements, management’s earnings
guidance, media coverage, and insider sales, and two signals measured at the industry-level:
lockup expirations and insider selling. Appendix A describes the thirteen total proposed
coordinating events. If one of the proposed devices is the coordinating mechanism that triggers
the crash, we expect a negative (positive) relation between the event and ABNRET
(FRACMVELOST).
Table 4 columns (1) and (2) show a positive relation between firm-specific downgrades
(DOWN) and the crash of a bubble firm’s stock. DOWN is associated with a negative 2.6% daily
abnormal return and with 1.9% of the market value lost during the crash period. Our inferences
about the impact of downgrades on the crash remain unaltered if we exclude from the abnormal
return the overnight return measured from the previous day’s closing to the current day’s
opening price, or if we exclude the stock return for the first 30 minutes of the current day from
the daily return (results untabulated). This analysis mitigates concerns that overnight events
caused negative returns in early trading, and the analyst’s downgrade is a reaction to the price
drop.
The abnormal returns are approximately twice the magnitude when the downgrade is
accompanied by an earnings forecast (DOWN+FCST), suggesting that traders believe
recommendations accompanied by a forecast are more reliable (Kecskés, Michaely, and
Womack, 2010) and believe other traders share these beliefs. Bubble firms also experienced
lower abnormal returns on days with a clustering in tech firm downgrades (TOTDOWN),
although TOTDOWN is not significantly associated with the portion of market value lost.
18
Of the ten coordinating devices we consider other than downgrade-related events, three
have a negative and significant relation with abnormal returns. WALKDOWN is a dummy
variable that equals one if the company issued guidance on day t that fell below the market
expectations. While significant in explaining abnormal returns (column 1), WALKDOWN does
not exhibit significant explanatory power for the fraction of market value lost (column 2).
LOWFORECAST is an indicator that equals one if an analyst issued a forecast on day t that fell
below the prevailing median forecast for the firm. The coefficient on LOWFORECAST exhibits
statistical significance in both models, but less economic significance than the downgrade
variables. On average the combined effect of the coefficient estimates on DOWN and
TOTDOWN is approximately three (five) times greater than the estimates on LOWFORECAST in
the model of daily abnormal returns (fraction of market value lost).
NUMLOCKUPS is the number of technology firms that had lockup expirations, which lift
short sale constraints, on day t.13
The effect of NUMLOCKUPS is significantly lower than that
of recommendation downgrades in the model for abnormal returns and NUMLOCKUPS does not
explain the fraction of market value lost during the crash period. This weak evidence is in line
with recent findings that the crash in equity prices of the tech bubble firms was not triggered by
firm-specific lockup expirations (Battalio and Schultz, 2006; Schultz, 2008; Griffin et al., 2011),
in contrast to the conclusions in Ofek and Richardson (2003).
The coefficient estimates on the indicator for an earnings announcement day (EARNANN)
and for an earnings announcement day that represents a negative surprise (NEGSURP) are not
statistically significant. These findings suggest that an earnings announcement is not a
coordinating event, which otherwise is a highly visible signal. We emphasize, however, that not
13
The bubble firms in the analysis do not have lockup expirations during the crash period due to our data
requirements. We consider lockups measured at the industry level (NUMLOCKUPS) to allow for the possibility that
lockup expirations at other tech firms acted as a coordinating event for our bubble firm sample.
19
finding a reaction to earnings announcements may be specific to the tech bubble given that
earnings are less informative about intrinsic value for high intangible firms (e.g., Amir and Lev,
1996; Collins et al., 1997; Lev and Zarowin, 1999; Barron et al., 2002).
In an attempt to provide evidence on causality, we estimate the relation between DOWN
and short-window abnormal returns in eqns. (2) and (3) conditional on the credibility and
visibility of the analyst making the downgrade. As an indication of credibility, we use the
designation of an analyst as an All-star from Fang and Yasuda (2011), who carefully identify
Institutional Investor magazine All-American research team analysts (All-stars) using the most
recent prior rankings published each year in the October issue. To measure visibility, we assume
that more media coverage on the day of the downgrade (as measured by MEDIA) implies a more
visible downgrade. We augment equations (2) and (3) to include indicator variables that equal
one if firm i was downgraded by an All-star analyst on day t (DOWN+STAR) and if firm i had
media coverage on a downgrade day (DOWN+MEDIA). If the analyst downgrade is the causal
coordinating device and not just correlated with another mechanism that causes traders to revise
their beliefs, we expect a stronger relation between the downgrade and ABNRET
(FRACMVELOST) for more credible and visible downgrades.
Columns (3) and (4) show that firms that were downgraded by an All-star analyst
experienced significantly lower abnormal returns (an additional 4.2%) and lost a greater portion
of their market value (an additional 2.7%) relative to firms that were downgraded by a non-All-
star analyst. Downgrades on days of media coverage are associated with significantly lower
abnormal returns (an additional 5.9%) and greater fractional losses in market value (an additional
2.8%) than firms without media coverage. Assuming traders are more likely to form beliefs
about average opinion when an All-star analyst talks, this result is consistent with causality.
20
However, another interpretation of the result is that All-star analysts are better at predicting price
than other analysts, where the predicted price includes the effects of rational speculative trading.
The result that downgrades accompanied by media coverage have a greater impact on returns,
however, is less ambiguous evidence of causality. It is difficult to argue that the media
successfully chooses to cover the analyst downgrades that are better predictors of average
opinion of price.
The finding that downgrades are associated with a revision in beliefs is consistent with
the in-sample evidence of an association between analyst recommendation concentration and the
bubble evolution. This finding also complements and extends prior literature that documents an
association between analyst recommendation changes and stock returns on average (e.g., Stickel,
1995; Womack, 1996; Barber, Lehavy, McNichols and Trueman, 2001; Jegadeesh, Kim, Krische
and Lee, 2004). The broad sample evidence in the existing literature indicates that the analyst
recommendation change is an information shock, but the evidence cannot distinguish whether
the shock is to information about expected intrinsic value or about anticipated speculative profits.
Our analysis, however, includes only firms identified to have a bubble in the stock price, defined
as a deviation of price from expected intrinsic value. Therefore, our analysis suggests that the
change in recommendation is associated with a reversal of mispricing, not just with information
about expected fundamentals. In addition, the tech bubble setting allows us to investigate a
number of covariates other than analyst recommendation changes, which the broad sample
studies do not do. We provide evidence that analyst recommendation changes have an effect on
returns incremental to that of earnings announcements, analyst earnings forecasts, insider sales,
management forecasts and media coverage.
21
3. Broad sample analysis
3.1. Observable signals and mispricing in the broad sample
We use a traditional portfolio methodology to measure the association between analyst
buy recommendation concentration and mispricing. Our measure of mispricing is one month
ahead returns following news (Zhang, 2006). The portfolios are formed by sorting on two
dimensions: the change in analyst recommendation concentration from month t-1 to t and the
direction of the earnings forecast revision in month t as a proxy for news. The change in analyst
recommendation concentration from month t-1 to t is computed by classifying stocks at each date
t as having Low (Medium) {High} analyst recommendation concentration if they have less than
33.3% (between 33.3% and 66.6%) {between 66.6% and 100%} buy or strong buy
recommendations (BUY%). Classifying stocks based on specific cut-offs is appealing as an
absolute measure of the extent of consensus among analysts in their recommendations.14 The
news in month t used to sort the stocks is considered bad news (no news) {good news} if the
change in the median analyst earnings estimate for the current fiscal year from month t-1 to t is
negative (zero) {positive}. For each portfolio, we analyze realized stock returns for month t+1
for equally weighted portfolios of individual stocks formed in month t.15
The sample consists of all stocks in CRSP for which earnings revision data and analyst
recommendation data are available from I/B/E/S. The sample period is January 1994 to
December 2009. Following Zhang (2006), we delete observations for which the absolute value
of the earnings forecast revision exceeds 100% of the prior year-end stock price because these
observations are likely to be erroneous. We exclude stocks with a share price below $5 at the
14
Our results are qualitatively similar when we sort stocks into terciles based on the level of buy percentages. 15
All results throughout the broad sample analysis are similar if instead of realized raw returns, portfolio returns
represent intercepts from a Fama and French (1996) three factor model based on returns and factors in month t+1,
estimated with and without a Carhart (1997) momentum factor.
22
portfolio formation date to reduce the likelihood that the results are driven by small, illiquid
stocks or by bid-ask bounce (Jegadeesh and Titman, 2001). We require that the stock is covered
by at least five analysts to create a meaningful measure of concentration.16
The unconditional
return following good (bad) news for this sample is 0.011 (0.008), significantly different at the
1% level (untabulated). These returns are qualitatively similar to the post-news mispricing of
1.84% and 0.72% following good news and bad news, respectively, in Zhang (2006).
Table 5 reports the portfolio returns. Panels A through C report the returns for portfolios
with increases, no change, and decreases in buy percentages (BUY%), respectively. Increases in
BUY% are associated with significantly higher realized stock returns in month t+1 regardless of
the direction of the news. The first row of Panel A shows returns for the portfolio of firms for
which the fraction of analysts with buy recommendations increases from “Low” in month t-1 to
“High” during month t. In month t+1, returns are 0.040, 0.023, and 0.048.17 The returns for this
extreme increase portfolio are significantly higher than those reported in the subsequent two
rows for portfolios with less dramatic increases in BUY% (Medium to High and Low to
Medium), which are around 0.01 and consistent with levels of unconditional portfolio returns.
Panels B and C report results for firms with no change in analyst recommendation concentration
and a decrease in analyst recommendation concentration, respectively. In these portfolios, the
returns (in absolute terms) are lower across all news categories. Only an extreme increase in
concentration is associated with significant mispricing.18
16
Our results are not sensitive to this requirement. 17
The pattern of lower returns in the “no news” category is consistent with the finding in Kecskés et al. (2010) that
recommendation changes accompanied by earnings forecast revisions have greater price reaction than those that are
not accompanied by earnings forecast revisions. 18
The finding that the returns for portfolios with decreases in BUY% (Panel C) show no mispricing cannot be
interpreted as evidence that the decrease was a coordinating event that ended prior mispricing. The only inference
from these results is that a decrease in BUY% is not associated with continued beliefs about future speculative
profits.
23
3.2 Evidence on month t+1 returns as a rational speculative bubble
In order to interpret the association between BUY% and period t+1 post-news returns as
evidence of an association between BUY% and traders’ beliefs in speculative profits, we must
also provide evidence that the month t+1 post-news returns are consistent with a rational
speculative bubble and not with other sources of mispricing such as short sale constraints or
under-reaction. Two findings suggest that the month t+1 returns are not explained by short sale
constraints, which is the most credible alternative explanation for the mispricing (Abreu and
Brunnermeier 2003; Chen et al. 2002). The first finding is reported in Table 5. For the
portfolios with increases in BUY% in Panel A, the differences in the returns for the “Good News
– Bad News” portfolio (in bold) are not significant. Regardless of the direction of the news, if
the fraction of analysts making a buy recommendation increases, the firm’s stock exhibits greater
positive one month ahead returns. This symmetry is consistent with rational speculative trading.
Fundamental news becomes irrelevant if traders believe other traders will ignore it, and so on,
and thus traders continue to anticipate speculative profits. The symmetry, however, is not
consistent with short sale constraints for which the mispricing will be lower following bad news.
A significant difference between good news and bad news portfolios appears only when there
was no change in BUY% (Panel B), such that the news dominates the signal associated with
traders’ beliefs.
The second finding that disputes short sale constraints as an explanation for the month
t+1 returns is from an analysis that conditions the portfolio results on breadth of ownership as a
measure of investor interest in shorting a firm’s stock (Chen et al., 2002). Following Chen et al.
(2002), we calculate breadth of ownership as the ratio of the number of mutual funds that hold a
long position in the stock to the total number of mutual funds in the sample for each quarter
24
using data from the Thomson Financial mutual fund database. A firm is classified as high (low)
breadth if the most recent quarterly measure as of the end of month t is greater than the median
measure across all firms as of month t. If short sale constraints explain bubbles, we expect to
observe greater mispricing in the low breadth portfolios, in which arbitrageurs with bad news are
sitting out during month t.
Table 6 reports the results. To conserve space, we report results only for the portfolios
with increases in BUY% corresponding to Panel A of Table 5. There are two key patterns to
note. First, within the high breadth portfolios, returns are 0.058 and 0.028 following bad news
and good news, respectively, which is significantly larger than the returns to less extreme
increases in BUY%, and consistent with the unconditional results reported in Panel A of Table 5.
Given that short sale constraints are not expected to be significant in the high breadth portfolios,
the fact that we find mispricing within these portfolios suggests that short sale constraints do not
fully explain the mispricing.
Second, for firms with bad news, the month t+1 returns to the low breadth portfolio are -
0.003, which are not significantly different from zero, and are significantly lower than the returns
to the high breadth portfolio (0.058) following an increase in BUY% from “Low to High.” These
patterns are opposite to the expectation if short sale constraints play a significant role in the
mispricing, although we caution that the result is based on only 18 observations in the bad
news/low breadth portfolio. For firms with good news in month t, across all categories of
BUY%, the returns to the low breadth portfolio are greater than the returns to the high breadth
portfolio, significant at conventional levels in the “Low to Medium” and “Medium to High”
categories but not in the “Low to High” category. These patterns suggest that short sale
constraints affect the extent of mispricing although the impact is muted when belief revisions are
25
significant. These findings in conjunction with the finding reported in Table 5 that the returns
following good news and bad news are symmetric suggests that short sale constraints have a
limited impact on the observed mispricing in our sample.
The second set of analyses designed to provide evidence on whether the month t+1
returns represent a rational speculative bubble and not some other form of mispricing examine
portfolio returns in subsequent months. Table 7 reports results for months t+2 and t+3 for the
portfolios with an increase in BUY% in Table 5. The post-news returns continue into month t+2
for both good and bad news firms. The differences between the returns following good and bad
news remain insignificant. By month t+3, the returns for the portfolio of firms with an extreme
increase in BUY%, which was approximately 0.04 in month t+1, has decreased to zero.
Our second analysis of subsequent returns examines crash incidence for the portfolios in
months t through t+3. In line with prior research that examines stock price crashes (e.g., Chen,
Hong and Stein 2001), for each firm-month observation, we compute the left skewness in returns
as the negative of (the sample analog to) the third moment of daily returns divided by (the
sample analog to) the standard deviation of daily returns raised to the third power. Thus, for any
stock i over any given month t, skew is:
)
∑ ) ) ) ∑
)
).
We rank the firms into quintiles based on skew within each calendar month and then compute the
fraction of firms in a given portfolio with skew in the top quintile of all firms for that month
(CRASH%).19
Table 8 presents the CRASH% for each portfolio. The first notable pattern relates to time
t, which is presented as a benchmark. Looking down the columns for all three news categories,
19
This measure of crash incidence is extremely conservative because it is constructed to capture only the top
quintile fraction in the left-skew.
26
CRASH% is highest in the portfolios with decreases in BUY% (Panel C). Thus, the market
appears to react in month t at the time of the recommendation decrease, regardless of the news.
The second notable pattern is the time trend in CRASH% for the portfolios of firms that
have an increase in BUY% (Panel A) and which exhibited evidence of mispricing in Table 5. In
month t+1, crash incidence for these portfolios is generally lower than in the month t baseline.
In the portfolios of firms with extreme increases in BUY% (“Low to High”), the drops in
CRASH% are directionally large. The number of observations in these portfolios is small, but
the drop in the good news portfolio from 25.34% to 16.44% is significant. The lower crash
incidence in month t+1 in the portfolios with extreme changes in BUY% is consistent with the
interpretation of Table 5 that these portfolios are experiencing positive mispricing. Crash
incidence increases significantly in month t+2 for the bad news portfolios and in month t+3 for
the no news and good news portfolios. In the no news and good news portfolios, the increases in
CRASH% in month t+3 are 5.81 and 6.16, respectively, both significant in one-tailed tests. In
the bad news portfolio, the increase in month t+2 of 11.34 is significant, as is the subsequent
decrease in month t+3. These findings suggest a reversal of the initial mispricing associated
with an increase in BUY% in month t.
In the portfolios with less extreme increases in BUY% (“Low to Medium” and “Medium
to High”), the time trends are directionally similar, although not as economically dramatic.
Statistical significance is higher due to larger numbers of observations. In the portfolios of firms
with no change in BUY% (Panel B) and a decrease in BUY% (Panel C), we do not observe a
similar pattern in CRASH%.
In summary, the month t+1 mispricing reported in Table 5 appears to be temporary and
there is evidence that the returns subsequently reverse (i.e., crashes). These patterns are expected
27
if the t+1 mispricing represents a rational speculative bubble. They are not consistent with other
explanations for the month t+1 returns including an omitted variable that is correlated with
changes in analyst recommendation concentration but relates to permanent changes in
characteristics such as growth or risk. These findings, taken together with the evidence that short
sale constraints are not driving the mispricing, is consistent with interpreting the month t+1
returns as mispricing based on rational speculative trading.
3.3 Additional sensitivity analyses of month t+1 portfolio returns
We conduct two conditional portfolio sorts to mitigate concerns that omitted correlated
variables explain the month t+1 returns. We first condition on analyst forecast dispersion. We
measure dispersion in month t as the standard deviation of analyst earnings forecasts for the
current fiscal year scaled by the prior year-end stock price to mitigate heteroskedasticity. A firm
is classified as high (low) dispersion in month t if its dispersion is above (below) the monthly
median.
Table 9 Panel A shows that the change in analyst recommendation concentration is a
significant determinant of month t+1 returns across both high and low dispersion portfolios for
both good news and bad news portfolios. Looking down the bad news columns (1 and 2) and the
good news columns (3 and 4), the returns are significantly greater for firms with extreme
increases in BUY% (“Low to High”) than for firms with “Low to Medium” or “Medium to High”
consistent with Table 5. The finding that the Table 5 results hold across various levels of
forecast dispersion mitigates concerns that (the inverse of) analyst buy recommendation
28
concentration is a proxy for information uncertainty, which has been associated with mispricing
(Diether et al., 2002; Garfinkel and Sokobin, 2006; Zhang, 2006).20
We next condition on liquidity. We measure liquidity using the Amihud (2002) price
impact measure, which is the ratio of absolute stock return to dollar volume for each day.
Greater price impact implies lower liquidity. A firm is classified as high (low) liquidity in month
t if the average daily price impact during month t is below (above) the monthly median.
Table 9 Panel B shows a decreasing pattern in portfolio returns as a function of the
increase in BUY% that is similar to that reported in Panel A of Table 5, regardless of the
portfolio’s liquidity. The only exception is in the portfolio with low liquidity (high price impact)
and a “Low to High” increase in recommendation concentration, which has only 29 observations.
The consistency of the results across the low and high liquidity portfolios suggests that variation
in liquidity is not driving the results.
We also conduct a series of sensitivity tests. Our main findings in Table 5 are robust to
all of these tests and we do not tabulate the results. First, we use price momentum as an
alternative proxy for news to identify post-news returns. Momentum is calculated as the stock’s
past 11-month return (Jegadeesh and Titman, 1993). The results are consistent with those in
Table 5. Increases in BUY% are associated with one month ahead returns, and there are no
significant differences across the good and bad news portfolios. The magnitudes of the returns in
the “Low to High” portfolios for both bad news and good news (0.037 and 0.038, respectively)
are lower than those when forecast revisions are used as a proxy for news (0.040 and 0.048 in
20
Table 9 Panel A also provides weak evidence that the impact of analyst recommendation concentration on returns
is more significant in the presence of greater dispersion. In both the bad news and good news portfolios, the return
for the “Low to High” portfolio is significantly greater than zero only in the high dispersion portfolios (columns (2)
and (4)). Traders should rationally expect other traders to underweight their private information about expected
intrinsic value in the presence of greater dispersion, which in turn implies that traders will increase the weight they
place on their higher order beliefs about stock price (e.g., Diether et al., 2002; Banerjee et al., 2009).
29
Table 5). One explanation is that momentum itself includes an element of mispricing in addition
to acting as a proxy for news about intrinsic value (Banerjee, 2011).
Second, the results are consistent across the early part of the sample period (1994 to
2001) and the later sub-period (2001 to 2009). This analysis is motivated by potential changes in
the information outputs of analysts following Regulation FD (Agrawal, Chadha, and Chen, 2006),
which could affect how recommendations are formed and viewed.
Third, we separately analyze triple-sorted portfolios conditioning on whether the firms
are in low or high intangible industries.21 The results in Table 5 hold in both the low and high
intangible portfolios, but the relation between analyst recommendation concentration and
mispricing is stronger in high intangible industries. This pattern is consistent with rational
speculation as the source of the mispricing, as we expect stronger results when publicly available
signals other than recommendations are less informative about intrinsic value.22
The finding that
the results hold in low intangibles industries, however, suggests that the association between
analyst recommendation concentration and mispricing is not limited to high intangibles firms,
which was a concern given that the in-sample tech bubble analysis motivated BUY% as a signal.
Finally, we compare the means of total assets, market value of equity, stock return
volatility, and sales volatility for firms in each recommendation-based portfolio. We find no
discernible pattern in the differences across portfolios, mitigating concerns that changes in
recommendation concentration are correlated with systematic differences in portfolio
characteristics.
21
Following Collins, Maydew, and Weiss (1997), we define high intangible industries as those with SIC codes 282
(plastics and synthetic materials), 283 (drugs), 357 (computer and office equipment), 367 (electronic components
and accessories), 48 (communications), 73 (business services), and 87 (engineering, accounting, R&D and
management related services). All other SIC codes are low intangible industries. 22
Earnings, for example, are less informative about intrinsic value for high intangible firms (e.g., Amir and Lev;
1996; Collins, Maydew, and Weiss, 1997; Lev and Zarowin, 1999; Barron, Byard, Kile, and Riedl, 2002).
30
3.4 Conditioning on analyst credibility to address the question of causality
We examine the impact of analyst credibility on the strength of changes in analyst buy
recommendation to explain returns with the purpose of providing evidence on whether analysts
influence traders’ beliefs or merely reflect them. Although tests of causality have low power in
the broad sample setting, we make an attempt by conditioning the relation between changes in
analyst recommendation concentration and month t+1 returns on the credibility of the portfolio
firms’ analysts. If changes in analyst recommendation concentration are the source of higher
order beliefs, and not just correlated with another signal, we expect a stronger relation between
BUY% and mispricing for portfolios containing firms with more credible analysts.
We use two proxies for analyst credibility: All-star analysts and analyst experience.
Firms are in the “All-star upgraded” portfolio if at least one All-star analyst issued an upgrade
during month t, where All-star analysts are defined by Fang and Yasuda (2011), described
previously. Firms are in the “high experience” (“low experience”) portfolio if the number of
years since the first year the analyst appears on I/B/E/S is above (below) the median experience
in month t. These proxies for credibility are consistent with prior research that suggests stronger
short window price reactions to recommendation upgrades by All-stars (Stickel, 1995) and more
experienced analysts (Mikhail, Walther, and Willis, 1997).
Table 10 presents portfolio returns for triple-sorted portfolios conditioning on whether an
All-star analyst (Panel A) or a highly experienced analyst (Panel B) issued an upgrade in month
t. We first present results for month t as a benchmark for comparison to prior evidence on
analyst credibility. The higher returns in month t for portfolios where an All-star analyst
upgraded (or the analyst experience was high) as compared to portfolios where All-star analysts
did not upgrade (or when analyst experience was low) confirm previously documented results
31
that the market response to upgrades is increasing in analyst credibility as measured by All-star
status or experience.
The main findings are the conditional portfolio returns for month t+1. Looking down the
columns, the results are consistent with Table 5, showing that extreme increases in analyst
recommendation concentration (“Low to High”) are associated with more extreme portfolio
returns compared to changes from “Low to Medium” and “Medium to High.” However, in Panel
A, we find no evidence that the All-Star analysts’ upgraded portfolios perform better than the
portfolios upgraded by the other analysts, which does not support a causal relation. To better
understand this “non” result, we further partition the portfolios with All-star upgrades based on
whether an All-star’s upgrade was accompanied by a forecast revision expecting that such
upgrades are viewed as more credible (Kecskés et al., 2010). The results, albeit based on small
samples, suggest that upgrades accompanied by forecasts are associated with significantly
greater mispricing (untabulated).23
In Panel B, the returns on portfolios based on more
experienced analysts are directionally greater than those on portfolios based on less experienced
analysts, but the difference is not significant. Overall, we interpret these findings as only weak
evidence in favor of causality.
5. Conclusion
Rational speculative trading based on traders’ higher order beliefs offers an explanation
for stock price bubbles. Despite the importance of traders’ beliefs to whether a stock follows a
bubble equilibrium or not, prior research has not attempted to identify specific observable 23
Of the 18 bad news firms in Table 6 Panel A that had at least one All-star making an upgrade, seven had an All-
star analyst concurrently making a positive forecast revision. For these seven firms, portfolio returns in month t+1
are 0.096, 0.030, and 0.037 for the three categories of upgrades. Portfolio returns are not significantly different from
zero for the other 11 bad news firms in any of the months. Of the 27 good news firms, 16 had a positive forecast
revision concurrent with an All-star upgrade. Portfolio returns in month t+1 for these 16 firms are 0.052, 0.008 and
0.012 for the three categories of upgrades.
32
mechanisms that are associated with traders’ beliefs about anticipated speculative profits. We
document two patterns in returns that suggest that analyst buy recommendations are an
observable signal associated with traders’ beliefs. First, analyst buy recommendation
concentration is associated with the continuation of the tech bubble. A related finding is that
during the period when tech stock prices were crashing, daily losses in market value for tech
firms are related to analyst downgrades. Second, changes in concentration of analyst buy
recommendations are related to one month ahead post-news portfolio returns. Each of the two
settings we analyze has distinct advantages for providing evidence on the observable
mechanisms associated with beliefs. The tech bubble is generally regarded as a rational
speculative “bubble.” Hence, we can infer from the results that the association between BUY%
and the bubble implies an association between BUY% and beliefs about anticipated speculative
profits. Although it is an in-sample investigation, the distinct bubble period, but only for some
tech firms, provides an opportunity to use a powerful difference-in-differences design. Because
of the relatively limited sample size, we can investigate mechanisms requiring hand-collected
data and we can conduct numerous robustness tests. The broad sample analysis provides out-of-
sample evidence and generalizes our findings to industries other than technology and to less
egregious levels of mispricing.
Although we cannot establish causality, we provide some evidence in this direction. In
the tech bubble setting, downgrades by All-star analysts and on days of media mentions are
associated with significantly greater losses in market value in the tech bubble setting. In the
broad sample analysis, the association between changes in BUY% and mispricing is weakly
positively correlated with analyst experience. These results are consistent with analyst
recommendations being the source of traders’ beliefs assuming that analyst credibility and
33
visibility make the signal more salient. Substantial room for future research remains to establish
causality.
In addition to providing evidence on observable sources of traders’ beliefs, this study
also provides indirect support for rational speculation as an explanation for bubbles. As part of
our broad sample analysis, we provide evidence that the month t+1 post-news returns associated
with a month t change in analyst recommendation concentration exhibit characteristics of a
rational speculative bubble. These returns are temporary, have greater subsequent crash
incidence, and are not explained by short sale constraints. This element of our study adds to the
recent empirical evidence that supports rational speculative trading as an explanation for bubbles
based on asset trading patterns and prices that are consistent with trading based on beliefs in
anticipated speculative profits (Brunnermeier and Nagel, 2004; Xiong and Yu, 2011; Griffin,
Harris, Shu and Topaloglu, 2011).
34
References
Abreu, Dilip, and Markus K. Brunnermeier, 2002, Synchronization risk and delayed arbitrage,
Journal of Financial Economics 66, 341-360.
Abreu, Dilip, and Markus K. Brunnermeier, 2003, Bubbles and crashes, Econometrica 71, 173-
204.
Agrawal, Anup, Sahiba Chadha, and Mark Chen, 2006, Who is afraid of Reg FD? The behavior and
performance of sell-side analysts following the SEC’s Fair Disclosure Rules, Journal of
Business 79, 2811-2834.
Allen, Franklin, Stephen Morris, and Hyun Song Shin, 2006, Beauty contests and iterated
expectations in asset markets, Review of Financial Studies 19, 719-752.
Amihud, Yakov, 2002, Illiquidity and stock returns: Cross-section and time-series effects,
Journal of Financial Markets 5, 31-56.
Amir, Eli, and Baruch Lev, 1996, Value-relevance of nonfinancial information: The wireless
communications industry, Journal of Accounting and Economics 22, 3-30.
Banerjee, Snehal, 2011, Learning from prices and the dispersion in beliefs, Review of Financial
Studies, 24(9): 3025-3068.
Banerjee, Snehal, Ron Kaniel and Ilan Kremer, 2009, Price drift as an outcome of differences in
higher order beliefs, Review of Financial Studies 22, 3707-3734.
Barber, Brad, Reuven Lehavy, Maureen McNichols, and Brett Trueman, 2001, Can investors
profit from the prophets? Security analyst recommendations and stock returns, Journal of
Finance 56, 531-563.
Barron, Orie E., Donal Byard, Charles Kile, Edward J. Riedl, 2002, High-technology intangibles
and analysts’ forecasts, Journal of Accounting Research 40, 289-312.
Battalio, Robert, and Paul Schultz, 2006, Options and the bubble, Journal of Finance 61, 2071–
2102.
Bergman, Nittai, and Sugata Roychowdhury, 2008, Investor sentiment and corporate disclosure,
Journal of Accounting Research 46, 1057-1083.
Bernhardt, Dan, Murillo Campello, and Edward Kutsoati, 2006, Who herds?, Journal of
Financial Economics 80, 657-675.
Bertrand, Marianne, Esther Duflo, and Sendhil Mullainathan, 2004, How much should we trust
differences-in-differences estimates? Quarterly Journal of Economics 119, 249-275.
Bhojraj, Sanjeev, Robert J. Bloomfield, and William B. Taylor, 2009, Margin trading,
overpricing, and synchronization risk, Review of Financial Studies 22, 2059-2085.
Biais, Bruno, and Peter Bossaerts, 1998, Asset prices and trading volume in a beauty contest,
Review of Economic Studies 65, 307-340.
Bradshaw, Mark T., 2004, How do analysts use their earnings forecasts in generating stock
recommendations? Accounting Review 79, 25-50.
35
Brunnermeier, Markus K., and Stefan Nagel, 2004, Hedge funds and the technology bubble,
Journal of Finance 59, 2013–2040.
Carhart, Mark M., 1997, On the persistence of mutual fund performance, Journal of Finance 52
(1), 57-82.
Chen, Joseph, Harrison Hong, and Jeremy C. Stein, 2002, Breadth of ownership and stock
returns, Journal of Financial Economics 66, 171-205.
Chen, Qi, and Wei Jiang, 2006, Analysts’ weighting of public and private information, Review of
Financial Studies 19, 319-355.
Chevalier, Judith, and Glenn Ellison, 1999, Career concerns of mutual fund managers, Quarterly
Journal of Economics 114, 389-432.
Clement, Michael, and Senyo Y. Tse, 2005, Financial analyst characteristics and herding
behavior in forecasting, Journal of Finance 60, 307-341.
Collins, Daniel W., Edward L. Maydew, and Ira S. Weiss, 1997, Changes in the value-relevance
of earnings and book values over the past forty years, Journal of Accounting and Economics
24, 39-67.
Daniel, Kent D., David Hirshleifer, and Avanidhar Subrahmanyam, 2001, Overconfidence,
arbitrage, and equilibrium asset pricing, Journal of Finance 56, 921-965.
Diether, Karl B., Christopher J. Malloy, and Anna Scherbina, 2002, Differences of opinion and
the cross section of stock returns, Journal of Finance 57, 2113-2141.
Fama, Eugene F., and Kenneth R. French, 1993, Common risk factors in returns on stocks and
bonds, Journal of Financial Economics 33, 3-56.
Fama, Eugene F., and Kenneth R. French, 1996, Multifactor explanation of asset pricing
anomalies, Journal of Finance 51, 55-84.
Fang, Lily H., and Ayako Yasuda, 2011, Are stars' opinions worth more? Evidence from stock
recommendations 1994-2009, Working paper.
Francis, Jennifer, and Leonard Soffer, 1997, The relative informativeness of analysts’ stock
recommendations and earnings forecast revisions, Journal of Accounting Research 35, 193-
211.
Froot, Kenneth A., David S. Scharfstein, and Jeremy C. Stein, 1992, Herd on the street:
informational inefficiencies in a market with short-term speculation, Journal of Finance 47,
1461-1484.
Gao, Pingyang, 2008, Keynesian beauty contest, accounting disclosure, and market efficiency,
Journal of Accounting Research 46, 785-807.
Garfinkel, Jon A., and Jonathan Sokobin, 2006, Volume, opinion divergence, and returns: A
study of post–earnings announcement drift, Journal of Accounting Research 44, 85–112.
Gilchrist, Simon, Charles P. Himmelberg, and Gur Huberman, 2005, Do stock price bubbles
influence corporate investment? Journal of Monetary Economics 52, 805–827.
Gleason, Christi A., and Charles M.C. Lee, 2003, Analyst forecast revisions and market price
discovery, The Accounting Review 78, 193-225.
36
Greenwood, Robin, and Stefan Nagel, 2009, Inexperienced investors and bubbles, Journal of
Financial Economics 93, 239-258.
Griffin, John M., Jeffrey H. Harris, Tao Shu, and Selim Topaloglu, 2011, Who drove and burst
the tech bubble?, Journal of Finance 66, 1251-1290.
Harrison, J. Michael, and David M. Kreps, 1978, Speculative investor behavior in a stock market
with heterogeneous expectations, The Quarterly Journal of Economics 92, 323-336.
Hirota, Shinichi, and Shyam Sunder, 2007, Price bubbles sans dividend anchors: Evidence from
laboratory stock markets, Journal of Economic Dynamics & Control 31, 1875-1909.
Hong, Harrison, Jeffrey D. Kubik, and Amit Solomon, 2000, Security analysts’ career concerns
and herding of earnings forecasts, RAND Journal of Economics 31, 121-144.
Hong, Harrison, Terence Lim, and Jeremy C. Stein, 2000, Bad news travels slowly: Size, analyst
coverage, and the profitability of momentum strategies, Journal of Finance 55, 265-295.
Hong, Harrison, Jose Scheinkman, and Wei Xiong, 2008, Advisors and asset prices: A model of
the origins of bubbles, Journal of Financial Economics 89, 268-287.
Jegadeesh, Narasimhan, Joonghyuk Kim, Susan D. Krische, and Charles M.C. Lee, 2004,
Analyzing the analysts: When do recommendations add value?, Journal of Finance 59, 1083-
1124.
Jegadeesh, Narasimhan, and Woojin Kim, 2010, Do analysts herd? An analysis of
recommendations and market reactions, Review of Financial Studies 23, 901-937.
Jegadeesh, Narasimhan, and Sheridan Titman, 1993, Returns to buying winner and selling losers:
Implications for stock market efficiency, Journal of Finance 48, 65-91.
Jegadeesh, Narasimhan, and Sheridan Titman, 2001, Profitability of momentum strategies: An
evaluation of alternative explanations, Journal of Finance 56, 699-720.
Kecskés, Ambrus, Roni Michaely, and Kent Womack, 2010, What drives the value of analysts’
recommendations: Earnings estimates or discount rate estimates?, Working paper.
Lev, Baruch, and Paul Zarowin, 1999, The boundaries of financial reporting and how to extend
them, Journal of Accounting Research 37, 353-385.
Lewellen, Jonathan, 2003. Discussion of ‘The Internet downturn: Finding valuation factors in
Spring 2000.’ Journal of Accounting and Economics 34, 237–247.
Malkiel, Burton G., 2010, Bubbles in asset prices, CEPS Working Paper No. 200, Princeton
University.
Mikhail, Michael, Beverly Walther, and Richard Willis, 1997, Do security analysts improve their
performance with experience? Journal of Accounting Research 35, 131-157.
Morris, Stephen, 1996, Speculative investor behavior and learning, NBER Working paper No.
96-5.
Morris, Stephen, and Hyun Song Shin, 2002, Social value of public information, American
Economic Review 92, 1521–1534.
Noussair, Charles, Stephane Robin, and Bernard Ruffieux, 2001, Price bubbles in laboratory
asset markets with constant fundamental values, Experimental Economics 4, 87–105.
37
Ofek, Eli, and Matthew Richardson, 2003, Dotcom Mania: The rise and fall of technology stock
prices, Journal of Finance 58, 1113 - 1138.
Pastor, Lubos, and Pietro Veronesi, 2006, Was there a Nasdaq bubble in late 1990s?, Journal of
Financial Economics 81, 61-100.
Polk, Christopher, and Paola Sapienza, 2009. The stock market and corporate investment: A test
of catering theory, Review of Financial Studies 22, 187-217.
Schultz, Paul, 2008. Downward-Sloping Demand Curves, the Supply of Shares, and the Collapse
of Internet Stock Prices, Journal of Finance 63, 351-378.
Skinner, Douglas, and Richard Sloan, 2002, Earnings surprises, growth expectations, and stock
returns or don’t let an earnings torpedo sink your portfolio, Review of Accounting Studies 7,
289–312.
Smith, Vernon L., Gerry L. Suchanek, and Arlington W. Williams, 1988, Bubbles, crashes, and
endogenous expectations in experimental spot asset markets, Econometrica 56, 1119-1151.
Stickel, Scott E., 1991, Common stock returns surrounding earnings forecast revisions: More
puzzling evidence, The Accounting Review 66 (2), 402-416.
Stickel, Scott E., 1995, The anatomy of the performance of buy and sell recommendations,
Financial Analysts Journal 51, 25-39.
Stiglitz, Joseph E., 1990, Symposium on Bubbles, Journal of Economic Perspectives, 4, 13–18.
Sunder, Shyam, 2002, Knowing what others know: Common knowledge, accounting and capital
markets, Accounting Horizons 16, 305-318.
Womack, Kent, 1996, Do brokerage analysts’ recommendations have investment value, Journal
of Finance 51, 137-167.
Xiong, Wei, and Jialin Yu, 2011, The Chinese warrants bubble, American Economic Review 101,
2723-2753.
Zhang, X. Frank, 2006, Information asymmetry and stock returns, Journal of Finance 61, 105-
136.
Zitzewitz, Eric, 2001, Measuring herding and exaggeration by equity analysts, MIT Mimeo.
38
Appendix A: Summary of potential coordinating events
The following table describes the twelve firm-specific and three industry-level potential
coordinating events during the crash of the tech bubble from September 2000 to October 2001.
Potential coordinating events related to analyst recommendations:
Variable name Description
(1) DOWN A dummy variable that equals one for a firm if it was downgraded by
at least one analyst on day t.
(2) DOWN+FCST A dummy variable that equals one when a downgrade is issued
contemporaneously with an earnings forecast.
(3) TOTDOWN Total number of downgrades experienced by all bubble firms on day t.
Potential coordinating events not related to analyst recommendations:
(1) EARNANN A dummy variable that equals one if the firm announces earnings on
day t.
(2) NEGSURP A dummy variable that equals one if the earnings announced on day t
are below the prevailing median analyst forecast.
(3) CIG A dummy variable that equals one if the firm issues earnings guidance
on day t.
(4) WALKDOWN A dummy variable that equals one if the guidance issued on day t falls
below the market expectations reported in the First Call database.
(5) FORECAST A dummy variable that equals one if an analyst issues an earnings
forecast on day t.
(6) LOWFORECAST A dummy variable that equals one if an analyst issues an earnings
forecast that is below the prevailing median forecast.
(7) MEDIA A dummy variable that equals one if an article in any major news and
business publication mentions the firm on day t.
(8) FIRM
INSIDERDUM
A dummy variable that equals one if firm i’s insiders were net sellers
of equity on day t. Information on insiders’ trading activity is
obtained from Thomson Reuters’s compilation of insider trades that
are filed with the Securities and Exchange Commission.
(9) NUM
TECHSALES
Industry-level measure of the number of tech firms that had insider
sales on day t.
(10) NUMLOCKUPS Industry-level measure of the number of tech firms that had lockup
expirations on day t. The lockup expiration date for a firm is assumed
to be the first day that an insider sale transaction appears in Thomson
Reuters’s compilation of insider trades.
Proxies for credibility and visibility of the recommendations:
DOWN+
ALLSTAR
A dummy variable that equals one when an All-star issues a
downgrade on day t.
DOWN+
MEDIA
A dummy variable that equals one when at least one analyst issues a
downgrade on day t and the firm is mentioned in any major news and
business publication on day t.
39
Table 1: Summary statistics for proposed signals for beliefs
Descriptive statistics across the pre-bubble, bubble, transition, and crash periods of the tech bubble for percentage of
buy recommendations (BUY%), concentration in long term growth forecasts measured as the proportion of forecasts
that lie in the top 40% of the range of the long term growth forecasts (HILTGFCST%), concentration in one year
ahead earnings measured as the proportion of forecasts that lie in the top 40% of the range of the one year ahead
earnings forecasts (HIEARNFCST%), an indicator for whether a firm has issued long term earnings guidance
(CIG_DUM), dispersion in analysts’ long term growth forecasts scaled by the mean forecast (DISPLTG), and
dispersion in analysts’ one year ahead earnings forecasts scaled by the mean forecast (DISPEARN). Statistical
significance (two-sided) at the 10%, 5% and 1% level is denoted by *, **, and ***, respectively.
Pre-Bubble Bubble Transition Crash
Panel A: Percentage buys (BUY%)
Mean Bubble firms 0.707 0.733 0.865 0.741
Non-bubble firms 0.650 0.529 0.392 0.464
Diff 0.057*** 0.204*** 0.473*** 0.277***
25th
percentile Bubble firms 0.500 0.545 0.800 0.600
Non-bubble firms 0.400 0.200 0.000 0.000
50th
Percentile Bubble firms 0.833 0.833 0.949 0.800
Non-bubble firms 0.750 0.500 0.333 0.500
Diff 0.08*** 0.33*** 0.62*** 0.30***
75th
Percentile Bubble firms 1.000 1.000 1.000 1.000
Non-bubble firms 1.000 1.000 0.667 1.000
Nobs Bubble firms 3,219 4,774 1,078 2,262
Non-bubble firms 1,841 2,519 480 755
Panel B: Concentration in Long term growth forecasts (HILTGFCST%)
Mean Bubble firms 0.568 0.556 0.503 0.511
Non-bubble firms 0.703 0.680 0.758 0.839
Diff -0.13*** -0.12*** -0.25*** -0.33***
25th
percentile Bubble firms 0.333 0.300 0.250 0.286
Non-bubble firms 0.500 0.500 0.500 0.500
50th
Percentile Bubble firms 0.500 0.500 0.500 0.500
Non-bubble firms 0.714 0.600 1.000 1.000
Diff -0.21*** -0.10*** -0.50*** -0.50***
75th
Percentile Bubble firms 1.000 1.000 0.667 0.667
Non-bubble firms 1.000 1.000 1.000 1.000
Nobs Bubble firms 2,574 3,969 887 1,931
Non-bubble firms 1,515 2,016 397 592
Panel C: Concentration in earnings forecasts (HIEARNFCST%)
Mean Bubble firms 0.588 0.570 0.564 0.518
Non-bubble firms 0.631 0.639 0.746 0.835
Diff -0.04*** -0.07*** -0.18*** -0.32
25th
percentile Bubble firms 0.333 0.333 0.286 0.222
Non-bubble firms 0.333 0.364 0.500 0.500
50th
Percentile Bubble firms 0.500 0.500 0.500 0.500
Non-bubble firms 0.500 0.500 1.000 1.000
Diff 0.00 0.00 -0.50*** -0.50***
75th
Percentile Bubble firms 1.000 0.900 0.875 0.846
Non-bubble firms 1.000 1.000 1.000 1.000
Nobs Bubble firms 2,874 4,232 973 2,026
Non-bubble firms 1,544 2,010 349 422
40
Table 1 (Cont’d)
Panel D: Long term management guidance (CIG_DUM)
Mean Bubble firms 0.007 0.014 0.021 0.045
Non-bubble firms 0.005 0.015 0.015 0.023
Diff 0.002 -0.001 0.007 0.022***
Nobs Bubble firms 3,219 4,774 1,078 2,262
Non-bubble firms 1,841 2,519 480 755
Panel E: Dispersion in Long term growth forecasts (DISPLTG)
Mean Bubble firms 0.189 0.197 0.219 0.235
Non-bubble firms 0.211 0.223 0.254 0.368
Diff -0.02*** -0.03*** -0.03 -0.13***
25th
percentile Bubble firms 0.126 0.131 0.137 0.150
Non-bubble firms 0.138 0.140 0.205 0.303
50th
Percentile Bubble firms 0.172 0.179 0.190 0.212
Non-bubble firms 0.191 0.207 0.266 0.346
Diff -0.02** -0.03*** -0.08*** -0.13***
75th
Percentile Bubble firms 0.242 0.243 0.256 0.292
Non-bubble firms 0.271 0.305 0.315 0.390
Nobs Bubble firms 767 1,573 400 889
Non-bubble firms 210 288 26 27
Panel F: Dispersion in earnings forecasts (DISPEARN)
Mean Bubble firms 0.055 0.060 0.035 0.090
Non-bubble firms 0.087 0.087 0.087 0.341
Diff -0.032 -0.027 -0.051* -0.252*
25th
percentile Bubble firms 0.015 0.000 0.015 0.013
Non-bubble firms 0.017 0.010 0.017 0.096
50th
Percentile Bubble firms 0.034 0.027 0.026 0.028
Non-bubble firms 0.035 0.036 0.037 0.167
Diff -0.001 -0.009*** -0.011** -0.139***
75th
Percentile Bubble firms 0.070 0.063 0.056 0.074
Non-bubble firms 0.100 0.134 0.154 0.305
Nobs Bubble firms 1,406 2,450 589 1,318
Non-bubble firms 463 518 26 40
41
Table 2: Probit regressions modeling bubble firms Probit regression estimates for equation (1) with proposed signals for beliefs. The dependent variable is equal to 1 for a firm that experienced a bubble in its
stock price during the technology bubble of 2000, and zero otherwise. Proposed signals for beliefs are: three measures of analyst recommendation concentration
including the percentage of buy recommendations (BUY%), percentage of recommendation upgrades (UP%), and percentage of recommendation downgrades
(DOWN%); concentration in long term growth (one year ahead earnings) forecasts measured as the proportion of forecasts that lie in the top 40% of the range of
the long term growth (and one year ahead earnings) forecasts (HILTGFCST% and HIEARNFCST%); an indicator for whether a firm has issued long term
earnings guidance (CIG_DUM); and dispersion in analysts’ long term growth forecasts scaled by the mean forecast (DISPLTG) and dispersion in analysts’ one
year ahead earnings forecasts scaled by the mean forecast (DISPEARN). The proposed signals are measured separately over the pre-bubble, bubble, transition,
and crash periods. The estimates reported represent marginal effects. t-statistics, reported in parentheses, are calculated based on standard errors obtained by
clustering at the firm level. Statistical significance (two-sided) at the 10%, 5% and 1% level is denoted by *, **, and ***, respectively.
Proposed signal:
Measures of analyst recommendation
concentration
LTG forecast
concentration
Earnings
forecast
concentration
Mgmt. guidance
indicator
LTG forecast
dispersion
Earnings
forecast
dispersion
BUY% UP% DOWN% HILTGFCST% HIEARNFCST% CIG_DUM DISPLTG DISPEARN
(1) (2) (3) (4) (5) (6) (7) (8)
Signal measured during:
Pre-bubble period 0.004 -0.047 -0.148** -0.052 0.067 0.115 -0.005 0.013
(0.079) (-0.686) (-2.041) (-0.613) (1.305) (0.658) (-1.001) (0.922)
Bubble period 0.243*** 0.230** -0.181*** -0.025 0.068* -0.056 -0.005 -0.017
(4.081) (2.574) (-3.068) (-0.351) (1.813) (-1.071) (-1.164) (-1.116)
Transition period 0.613*** 0.731** 0.278** -0.336*** -0.077 -0.117 -0.004 -0.006
(5.685) (2.407) (2.038) (-4.422) (-1.186) (-1.321) (-0.766) (-0.151)
Crash period 0.269*** 0.053 0.476*** -0.390*** -0.230*** -0.037 -0.010 -0.003
(2.774) (0.339) (2.625) (-3.670) (-2.812) (-0.474) (-1.198) (-0.180)
Control variables in levels YES YES YES YES YES YES YES YES
Control variables in changes YES YES YES YES YES YES YES YES
Time period dummies YES YES YES YES YES YES YES YES
Observations 16,928 16,580 16,580 13,881 14,430 16,928 4,180 6,810
Pseudo R-squared 47% 45% 45% 48% 44% 44% 69% 53%
Tests of differences across periods: Bubble – Pre-bubble period 0.239*** 0.276** -0.033 0.027 0.001 -0.172 0.001 -0.029
(3.474) (2.502) (-0.354) (0.324) (0.022) (-1.089) (0.215) (-1.309)
Transition – Bubble period 0.370*** 0.501 0.460*** -0.311*** -0.145* -0.060 0.001 0.010
(4.083) (1.565) (3.102) (-3.657) (-1.931) (-0.586) (0.227) (0.253)
Crash – Transition period -0.344*** -0.678** 0.197 -0.054 -0.153 0.080 -0.006 0.004
(-4.362) (-1.967) (0.869) (-0.633) (-1.621) (0.932) (-1.112) (0.099)
42
Table 2, continued
Control variables: (1) logarithm of net sales (LOGSALE), (2) number of analysts following a firm during period t (NUMANAL), (3) firm age (AGE) at the end
of period t estimated as the maximum of the time period over which the firm appears in CRSP or COMPUSTAT, (4) ratio of book value of total debt to book
value of total assets (LEVERAGE), (5) ratio of capital expenditure scaled by book value of total assets (CAPEX), (6) ratio of research and development
expenditure scaled by book value of total assets (R&D), (7) book value of intangible assets scaled by book value of total assets (INTANGIBLES), (8) book value
of total assets scaled by market value of total assets (BTOM) as a proxy for a firm’s growth opportunities, and (9) portion of a firm’s stock return volatility
explained by the Fama-French four factor model as proxy for a firm’s systematic risk (SYSVOL). SYSVOL is computed at any point in time by estimating the
Fama-French model using daily stock returns for the past six months. Finally, we include changes in LOGSALE, NUMANAL, LEVERAGE, CAPEX, R&D and
INTANGIBLES as control variables. All control variables are constructed using stock price data from CRSP, accounting data from the CRSP/COMPUSTAT
merged database, and analyst-related data from the I/B/E/S database. Control variables that require data solely from accounting reports are measured using
financial statements for the prior fiscal year closest to the beginning of period t, and the changes in these control variables are computed using financial
statements for the prior two fiscal years closest to the beginning of period t. Control variables that require stock price data (BTOM and SYSVOL), are measured
at the beginning of the bubble formation period (or beginning of January, 1998) for all firm-month observations following the pre-bubble period. During the pre-
bubble period, BTOM and SYSVOL are measured at the beginning of the month.
43
Table 3: Sensitivity of results to inclusion of proxies for news in recommendations about intrinsic value
Estimates from augmented versions of eqn. (1) that include two proxies for news in the analyst recommendations
about intrinsic value: the firm-level mean estimate of long term growth forecast (ESTLTG) and the mean analyst one
year ahead earnings forecast (ESTEARN1). ESTLTG and ESTEARN1 are measured separately over the pre-bubble,
bubble, transition, and crash periods. The estimates reported represent marginal effects. Coefficient estimates for
ESTEARN1 have been multiplied by 10,000. t-statistics, reported in parentheses, are calculated based on standard
errors obtained by clustering at the firm level. Statistical significance (two-sided) at the 10%, 5% and 1% level is
denoted by *, **, and ***, respectively.
Recommendation concentration variable BUY% UP% DOWN%
(1) (2) (3)
Analyst recommendation concentration during:
Pre-bubble period 0.009 -0.006 -0.185***
(0.181) (-0.105) (-2.658)
Bubble period 0.219*** 0.253*** -0.197***
(3.398) (2.712) (-3.508)
Transition period 0.542*** 0.456** 0.167
(3.892) (1.976) (1.442)
Crash period 0.032 -0.158 0.286*
(0.293) (-0.945) (1.652)
Proxies for analyst expectations of intrinsic value:
ESTLTG: Pre-bubble period 0.484** 0.417* 0.415*
(1.974) (1.774) (1.775)
Bubble period 0.521* 0.572* 0.568*
(1.838) (1.820) (1.817)
Transition period 0.766** 0.751 0.754
(2.120) (1.519) (1.519)
Crash period 1.851*** 1.792*** 1.762***
(3.260) (3.343) (3.336)
ESTEARN1: Pre-bubble period -0.103 -0.096 -0.098
(-0.502) (-0.446) (-0.461)
Bubble period -0.223 -0.103 -0.102
(-1.247) (-0.679) (-0.673)
Transition period 0.004 -0.062 -0.056
(0.097) (-0.399) (-0.390)
Crash period -0.152*** -0.152*** -0.151***
(-4.416) (-4.176) (-4.155)
Control variables YES YES YES
Time period dummies YES YES YES
Observations 13,087 12,992 12,992
Pseudo R-squared 53% 51% 51%
44
Table 4: Daily returns analysis of the crash period coordinating event
Analysis of daily crash period returns for the sample of bubble firms as a function of the proposed coordinating
events. Columns (1) and (3) present OLS estimates of equation (2) with the day t abnormal return as the dependent
variable. Columns (2) and (4) present tobit estimates of equation (3) with market value of equity lost on day t
divided by total market value lost over the crash period as the dependent variable (FRACMVELOST).
FRACMVELOST is set to zero for days when a firm experiences an increase in equity market value. Reported
estimates represent marginal effects. Possible coordinating events are described in Appendix A. Coefficient
estimates for total downgrades (TOTDOWN) and number of firms with insider sales (NUMTECHSALES) have been
multiplied by 10. t-statistics, reported in parentheses, are calculated based on standard errors obtained by clustering
at the firm level. Statistical significance (two-sided) at the 10%, 5% and 1% level is denoted by *, **, and ***,
respectively.
Dependent variable
Abnormal return
(day t)
(1)
%MVE lost
(day t)
(2)
Abnormal return
(day t)
(3)
%MVE lost
(day t)
(4)
Possible coordinating events on day t:
Downgrades: DOWN -0.026*** 0.019*** -0.017*** 0.014***
(-5.194) (4.079) (-3.620) (3.262)
DOWN+FCST -0.027*** 0.002 -0.021*** -0.001
(-3.435) (0.528) (-2.637) (-0.266)
DOWN+ALLSTAR -0.042*** 0.027**
(-3.547) (2.433)
DOWN+MEDIA -0.059*** 0.028**
(-3.085) (2.426)
Downgrades of bubble firms (TOTDOWN) -0.006* 0.003 -0.006* 0.003
(-1.871) (0.139) (-1.775) (0.139)
Earnings anncmts: EARNANN 0.002 0.004 0.001 0.005
(0.553) (1.216) (0.369) (1.323)
NEGSURP -0.008 -0.002 -0.009 -0.002
(-1.212) (-0.414) (-1.241) (-0.400)
Mgmt. forecasts: CIG -0.012 0.006 -0.010 0.005
(-1.386) (1.272) (-1.216) (1.128)
WALKDOWN -0.020* -0.004 -0.022* -0.003
(-1.832) (-0.798) (-1.960) (-0.624)
Earnings fcsts: FORECAST 0.008*** 0.001 0.008*** 0.001
(3.549) (0.284) (3.498) (0.324)
LOWFORECAST -0.010*** 0.004* -0.011*** 0.004*
(-4.225) (1.781) (-4.231) (1.785)
Media coverage (MEDIA) 0.005* 0.002 0.008** 0.001
(1.689) (1.344) (2.598) (0.501)
Insider sales: FIRMINSIDERDUM 0.003** 0.000 0.004** 0.000
(2.235) (0.139) (2.305) (0.114)
NUMTECHSALES 0.003*** -0.014*** 0.003*** -0.014***
(4.703) (-4.798) (4.685) (-4.792)
Industry-level NUMLOCKUPS -0.004*** -0.001 -0.004*** -0.001
lockup expirations (-3.133) (-0.042) (-3.135) (-0.057)
Control variables in levels YES YES YES YES
Times period dummies YES YES YES YES
Observations 45,459 45,385 45,459 45,385
R-squared 5.8% 6.0%
45
Table 5: Portfolio mispricing conditional on change in percentage of buy recommendations (BUY%)
One month ahead portfolio returns for firms with good news, no news, or bad news, sorted by the change in analyst
recommendation concentration. Stocks that have less than 33.3% (between 33.3% and 66.6%, between 66.6% and
100%) buy recommendations are classified as Low (Medium, High). Portfolios are formed based on the change in
BUY% from month t-1 to t, and the reported portfolio returns represent the equally weighted return for the portfolio
for month t+1. News is defined by the analyst earnings forecast revision (REV) for the current fiscal year. In each
month t, stocks are sorted into good news (no news) {bad news} categories if the earnings forecast revision from
month t-1 to t is positive (zero) {negative}. Stocks with a price less than $5 at the portfolio formation date are
excluded from the sample. t-statistics are reported below the mean returns in parentheses and are adjusted for
autocorrelation. The number of observations is presented below the t-statistics in italics.
Bad News
(REV < 0)
No News
(REV = 0)
Good News
(REV > 0)
Good - Bad
Panel A: Increase in BUY%
Low to High 0.040 0.023 0.048 0.008
(2.420) (1.836) (4.315) (0.382)
117 185 165
Medium to High 0.013 0.010 0.015 0.002
(4.907) (5.488) (7.762) (0.410)
3,154 4,854 4,884
Low to Medium 0.013 0.014 0.014 0.001
(5.888) (7.882) (8.128) (1.359)
3,229 4,443 4,272
Panel B: No change in BUY%
High to High 0.005 0.002 0.011 0.006
(4.123) (2.804) (12.028) (3.325)
2,2516 42,215 31,884
Medium to Medium 0.010 0.008 0.011 0.001
(11.642) (12.836) (17.464) (1.035)
27,636 40,957 31,160
Low to Low 0.008 0.009 0.011 0.003
(8.185) (12.047) (13.176) (2.025)
20,710 25,886 16,853
Panel C: Decrease in BUY%
High to Low 0.001 0.007 -0.013 -0.015
(0.123) (0.727) (-1.238) (-0.648)
371 195 110
High to Medium 0.005 0.002 0.009 0.005
(2.380) (0.905) (4.911) (1.240)
6,162 6,068 4,680
Medium to Low 0.009 0.007 0.011 0.011
(4.325) (3.812) (5.450) (0.582)
5,264 4,571 3,194
46
Table 6: Portfolio mispricing conditional on change in percentage of buy recommendations (BUY%) and breadth of ownership
One month ahead portfolio returns for firms with good news, no news, or bad news, sorted by the change in analyst recommendation concentration and breadth of
ownership. Breadth of ownership is the fraction of mutual funds holding the stock in month t. Low breadth indicates that short sale constraints are more binding
(Chen et al., 2002). Stocks that have less than 33.3% (between 33.3% and 66.6%, between 66.6% and 100%) buy recommendations are classified as Low
(Medium, High). Portfolios are formed based on the change in BUY% from month t-1 to t, and the reported portfolio returns represent the equally weighted
return for the portfolio for month t+1. News is defined by the analyst earnings forecast revision (REV) for the current fiscal year. In each month t, stocks are
sorted into good news (no news) {bad news} categories if the earnings forecast revision from month t-1 to t is positive, (zero) {negative}. Stocks with a price
less than $5 at the portfolio formation date are excluded from the sample. t-statistics are reported below the mean returns in parentheses and are adjusted for
autocorrelation. The number of observations is presented below the t-statistics in italics.
Bad news Good news Good News – Bad News
Low breadth
(1)
High breadth
(2)
High - Low
(2)-(1)
Low breadth
(3)
High breadth
(4)
High - Low
(4)-(3)
Low breadth
(3) – (1)
High breadth
(4) – (2)
Increase in BUY%
Low to High -0.003 0.058 0.061 0.075 0.028 -0.046 0.077 -0.030
(-0.063) (3.051) (2.126) (2.896) (2.032) (-1.475) (1.401) (-1.543)
18 65 34 83
Medium to High 0.011 0.017 0.006 0.024 0.010 -0.015 0.014 -0.007
(1.958) (3.980) (0.667) (6.012) (3.307) (-2.482) (1.674) (-1.119)
994 1,095 1,559 1,656
Low to Medium 0.010 0.017 0.008 0.019 0.009 -0.009 0.009 -0.008
(2.228) (5.073) (1.141) (5.394) (3.604) (-1.886) (1.384) (-1.618)
1,019 1,124 1,418 1,505
47
Table 7: Portfolio mispricing in months t+2 and t+3 conditional on change in percentage of buy
recommendations (BUY%) Two month ahead and three month ahead portfolio returns for firms with good news, no news, or bad news, sorted by
the change in analyst recommendation concentration. Stocks that have less than 33.3% (between 33.3% and 66.6%,
between 66.6% and 100%) buy recommendations are classified as Low (Medium, High). Portfolios are formed based
on the change in BUY% from month t-1 to t, and the reported portfolio returns represent the equally weighted return
for the portfolio for month t+1. News is defined by the analyst earnings forecast revision (REV) for the current fiscal
year. In each month t, stocks are sorted into good news (no news) {bad news} categories if the earnings forecast
revision from month t-1 to t is positive (zero) {negative}. Stocks with a price less than $5 at the portfolio formation
date are excluded from the sample. t-statistics are reported below the mean returns in parentheses and are adjusted for
autocorrelation. The number of observations is presented below the t-statistics in italics.
Month t+2 Returns
Bad News
(REV < 0)
No News
(REV = 0)
Good News
(REV > 0) Good - Bad
Increase in BUY%
Low to High 0.041 0.004 0.022 -0.019
(2.829) (0.427) (1.811) (-0.942)
117 185 165
Medium to High 0.003 0.011 0.011 0.008
(1.021) (5.725) (5.265) (1.933)
3,154 4,854 4,884
Low to Medium 0.010 0.009 0.012 0.002
(4.293) (5.404) (5.977) (0.486)
3,229 4,443 4,272
Month t+3 Returns
0.007 -0.005 -0.001 -0.007
Low to High (0.383) (-0.404) (-0.045) (-0.370)
117 185 165
0.002 0.008 0.007 0.005
Medium to High (0.874) (4.243) (3.553) (1.136)
3,154 4,854 4,884
0.009 0.007 0.007 -0.002
Low to Medium (3.765) (3.846) (3.853) (-1.327)
3,229 4,443 4,272
48
Table 8: Crash incidence conditional on change in percentage of buy recommendations (BUY%)
Crash incidence for firms with good news, no news, or bad news, sorted by the change in analyst recommendation concentration. Stocks that have less than
33.3% (between 33.3% and 66.6%, between 66.6% and 100%) buy recommendations are classified as Low (Medium, High). Portfolios are formed based on the
change in BUY% from month t-1 to t. Crash incidence is measured in the month ended t, concurrent with the month of the change in BUY%, and in months t+1,
t2, and t+3. Crash incidence (CRASH%) is measured as the fraction of firms in the portfolio that have a left-skewed distribution that is in the top quintile across
firms in the same news category. In each month t, stocks are sorted into good news (no news) {bad news} categories if the earnings forecast revision from month
t-1 to t is positive (zero) {negative}. Stocks with a price less than $5 at the portfolio formation date are excluded from the sample. Significance levels are
reported for the difference between the portfolio proportion in the period relative to the proportion of the previous period based on z-statistics. Statistical
significance (two-sided) at the 10%, 5% and 1% level is denoted by *, **, and ***, respectively. Statistical significance (one-sided) at the 10%, 5% and 1% level
is denoted by c, b, and a, respectively. The number of observations is presented below in italics.
Bad News (REV < 0) No News (REV = 0) Good News (REV > 0)
t t+1 t+2 t+3 t t+1 t+2 t+3 t t+1 t+2 t+3
Increase in BUY%
Low to High 22.68 17.53 28.87*,b
16.49**,b
20.93 16.28 15.12 20.93c 25.34 16.44
*,b 15.07 21.23
c
97 97 97 97 172 172 172 172 146 146 146 146
Medium to High 17.07 16.85 20.26***,a
21.18 18.30 16.69**,b
18.44**,b
20.64***,a
16.45 17.12 20.71***,a
20.33
2,747 2,747 2,744 2,739 4,404 4,404 4,403 4,395 4,287 4,287 4,287 4,280
Low to Medium 15.43 14.55 18.74***,a
18.70 18.09 16.72c 18.34
*,b 19.19 16.88 17.04 19.46
***,a 19.81
2,858 2,860 2,860 2,850 4,068 4,068 4,067 4,059 3,792 3,792 3,792 3,781
No change in BUY%
High to High 19.67 20.94***,a
20.66 20.95 19.88 19.69 20.62***,a
21.34**,a
20.18 19.90 20.88***,a
20.82
19,626 19,623 19,614 19,560 38,483 38,481 38,468 38,351 28,428 28,428 28,420 28,368
Medium to Medium 18.08 17.70 19.27***,a
19.45 19.33 19.47 19.42 19.33 19.34 19.30 19.03 19.38
24,295 24,296 24,286 24,230 37,363 37,363 37,337 37,172 27,493 27,492 27,469 27,385
Low to Low 18.45 17.38***, a
19.79***,a
19.37 19.48 19.21 18.76 18.74 19.59 19.99 18.97**,b
18.88
18,379 18,379 18,351 18,234 23,429 23,429 23,400 23,231 14,826 14,825 14,802 14,700
Decrease in BUY%
High to Low 49.56 51.31 18.37***,a
18.98 40.22 36.87 34.09 20.99***,a
37.11 32.99 32.29 21.59c
343 343 343 332 179 179 176 162 97 97 96 88
High to Medium 29.22 30.43c 20.29
***,a 20.61 25.12 27.12
**,a 23.57
***,a 19.89
***,a 25.81 25.51 21.42
***,a 20.69
5,428 5,429 5,422 5,401 5,549 5,549 5,542 5,479 4,053 4,053 4,048 4,017
Medium to Low 27.44 27.49 20.46***,a
19.43 24.27 27.06***,a
22.89***,a
18.94***,a
20.18 19.90 20.88***,a
20.82
4,686 4,686 4,682 4,642 4,158 4,158 4,146 4,082 2,775 2,775 2,769 2,734
49
Table 9: Portfolio mispricing conditional on change in percentage of buy recommendations (BUY%), dispersion, and liquidity
One month ahead portfolio returns for firms with good news, no news, or bad news, sorted by the change in analyst recommendation concentration. Panel A
presents results conditional on analyst forecast dispersion, measured as the standard deviation of forecasts in month t scaled by the prior year-end stock price. A
firm is classified as high (low) dispersion in month t if its dispersion is above (below) the monthly median. Panel B presents results conditional on liquidity
measured using the Amihud (2002) price impact measure. We use daily CRSP data (CRSP variables ret, prc, and vol) to calculate the ratio of absolute stock
return to dollar volume [1,000,000*|ret|/(|prc|*vol)] for each day. A firm is classified as high (low) liquidity in month t if its monthly average price impact is
below (above) the monthly median. In both panels, stocks that have less than 33.3% (between 33.3% and 66.6%, between 66.6% and 100%) buy
recommendations are classified as Low (Medium, High). Portfolios are formed based on the change in BUY% from month t-1 to t, and the reported portfolio
returns represent the equally weighted return for the portfolio for month t+1. News is defined by the analyst earnings forecast revision (REV) for the current
fiscal year. In each month t, stocks are sorted into good news (no news) {bad news} categories if the earnings forecast revision from month t-1 to t is positive,
(zero) {negative}. Stocks with a price less than $5 at the portfolio formation date are excluded from the sample. t-statistics are reported below the mean returns in
parentheses and are adjusted for autocorrelation. The number of observations is presented below the t-statistics in italics.
Bad News Good News Good News – Bad News
Panel A: Conditional on analyst forecast dispersion
Low
dispersion
High
dispersion High - Low
Low
dispersion
High
dispersion High - Low
Low
dispersion
High
dispersion
Increase in BUY%
Low to High 0.026 0.038 0.012 0.017 0.038 0.020 -0.008 0.000
(1.317) (1.896) (0.414) (0.965) (2.501) (0.758) (-0.435) (0.033)
22 59 34 92
Medium to High 0.012 0.015 0.003 0.010 0.018 0.008 -0.002 0.004
(3.402) (3.516) (0.381) (4.180) (6.059) (1.669) (-0.365) (0.553)
1,510 1,598 2,364 2,455
Low to Medium 0.013 0.015 0.002 0.011 0.017 0.005 -0.002 0.002
(4.645) (4.206) (0.334) (5.744) (5.950) (1.341) (-0.389) (0.377)
1,547 1,646 2,047 2,152
Panel B: Conditional on liquidity
Low liquidity High liquidity High - Low Low liquidity High liquidity High - Low Low liquidity High liquidity
Increase in BUY%
Low to High 0.052 0.004 -0.048 0.054 0.034 -0.020 0.002 0.030
2.485 0.205 -1.241 3.854 1.949 -0.831 0.079 1.144
88 29 113 52
Medium to High 0.011 0.016 0.005 0.020 0.010 -0.010 0.009 -0.006
2.829 4.182 0.700 6.879 3.925 -2.055 1.511 -1.031
1621 1533 2489 2395
Low to Medium 0.017 0.010 -0.007 0.013 0.015 0.002 -0.004 0.005
5.092 3.131 -1.276 5.395 6.119 0.462 -0.760 1.165
1667 1562 2187 2085
50
Table 10: Portfolio mispricing conditional on change in percentage of buy recommendations (BUY%) and analyst credibility
One month ahead portfolio returns for firms with good news or bad news, sorted by the change in analyst buy recommendation concentration and the credibility
of upgrades during month t. Firms in the portfolio labeled “Upgraded by an All-star” had at least one upgrade by an All-star during month t (Panel A). Firms in
the portfolio labeled “Upgraded by a High experience analyst” had an upgrade by an analyst with greater than the median experience level in month t (Panel B).
Stocks that have less than 33.3% (between 33.3% and 66.6%, between 66.6% and 100%) buy recommendations are classified as Low (Medium, High).
Portfolios are formed based on the change in BUY% from month t-1 to t, and the reported portfolio returns represent the equally weighted return for the portfolio
for month t+1. News is defined by the analyst earnings forecast revision for the current fiscal year. In each month t, stocks are sorted into good news (no news)
{bad news} categories if the earnings forecast revision from month t-1 to t is positive, (zero) {negative}. Stocks with a price less than $5 at the portfolio
formation date are excluded from the sample. t-statistics are reported below the mean returns in parentheses and are adjusted for autocorrelation. The number of
observations is presented below the t-statistics in italics.
Panel A: Conditioning on All-star analyst upgrades
Bad news Good news Good News – Bad News
Not upgraded
by an All-star
(1)
Upgraded by
an All-star
(2)
All-star – No
All-star
(2)-(1)
Not upgraded
by an All-star
(3)
Upgraded by
an All-star
(4)
All-star – No
All-star
(4)-(3)
Not
upgraded by
an All-star
(3)-(1)
Upgraded by
an All-star
(4)-(2)
Returns in month t
Low to High 0.057 0.160 0.103 0.045 0.083 0.037 -0.012 -0.078
(1.262) (2.661) (0.922) (3.098) (2.990) (1.041) (-0.275) (-1.504)
99 18 138 27
Medium to High 0.023 0.046 0.023 0.039 0.058 0.020 0.016 0.012
(7.719) (5.019) (2.471) (18.316) (6.669) (2.919) (4.361) (0.964)
2,817 337 4,358 526
Low to Medium 0.017 0.050 0.033 0.039 0.071 0.032 0.022 0.021
(6.590) (5.069) (3.992) (19.257) (9.802) (4.919) (6.721) (1.681)
2,883 346 3,786 486
Returns in month t+1
Low to High 0.043 0.024 -0.019 0.050 0.039 -0.011 0.006 0.015
(2.340) (0.631) (-0.408) (3.979) (1.658) (-0.241) (0.225) (0.347)
Medium to High 0.012 0.027 0.015 0.017 0.001 -0.015 0.005 -0.025
(4.035) (3.594) (1.658) (8.106) (0.233) (-2.457) (1.428) (-2.624)
Low to Medium 0.013 0.015 0.002 0.015 0.006 -0.009 0.002 -0.009
(5.417) (2.395) (0.294) (8.205) (1.205) (-1.616) (0.638) (-1.176)
51
Table 10, continued
Panel B: Conditioning on experienced analyst upgrades
Bad news Good news Good News – Bad News
Not upgraded
by a High
experience
analyst
(1)
Upgraded by
a High
experience
analyst
(2)
High
experience –
Low
experience
(2)-(1)
Not upgraded
by a High
experience
analyst
(3)
Upgraded by
a High
experience
analyst
(4)
High
experience –
Low
experience
(4)-(3)
Not
upgraded by
a High
experience
analyst
(3)-(1)
Upgraded by
a High
experience
analyst
(4)-(2)
Returns in month t
Low to High 0.011 0.112 0.101 0.032 0.065 0.033 0.021 -0.047
(0.356) (1.833) (1.242) (1.453) (3.999) (1.602) (0.696) (-0.865)
45 72 66 99
Medium to High 0.013 0.046 0.033 0.031 0.057 0.026 0.018 0.010
(4.082) (8.479) (5.609) (13.364) (14.098) (5.667) (4.584) (1.530)
1,933 1,221 2,983 1,901
Low to Medium 0.008 0.039 0.031 0.028 0.065 0.037 0.020 0.026
(2.872) (8.523) (5.964) (12.214) (18.742) (8.960) (5.365) (4.523)
1,963 1,266 2,623 1,649
Returns in month t+1
Low to High 0.029 0.047 0.018 0.041 0.052 0.011 0.012 0.005
(1.108) (2.186) (0.579) (1.889) (4.558) (0.403) (0.365) (0.206)
Medium to High 0.011 0.017 0.006 0.014 0.017 0.003 0.003 0.000
(3.176) (3.912) (1.022) (5.800) (5.161) (0.680) (0.702) (0.017)
Low to Medium 0.013 0.015 0.002 0.013 0.015 0.002 0.001 0.001
(4.329) (4.014) (0.391) (5.926) (5.632) (0.564) (0.175) (0.181)
0
Figure 1: Cumulative returns for technology firms across Nasdaq price-to-sales quintiles
Figure 1 presents the cumulative return (value weighted, rebalanced every month) for tech firms within the Nasdaq
P/S quintiles for the period January 1996 through October 2001. The pre-bubble period is from January 1996
through December end of year 1997. The bubble period is from January 1998 through February 2000. The
transition period is from March 2000 through August 2000. The crash period is from September 2000 through
October 2001. A firm is identified as a bubble firm based on its P/S ratio ranking at the end of February 2000.