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.

1

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).

2

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.

3

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

12

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.