Firm-Specific Investor Sentiment
David Aboody
UCLA Anderson Graduate School of Management
[email protected]
Omri Even-Tov
UCLA Anderson Graduate School of Management
[email protected]
Reuven Lehavy
Ross School of Business
University of Michigan
e-mail: [email protected]
Brett Trueman
UCLA Anderson Graduate School of Management
[email protected]
Current draft: July 2013
We thank Brad Barber, Avanidhar Subrahmanyam, and participants at the 2013 London Business
School Transatlantic Doctoral Conference for their helpful comments. All remaining errors are
our own.
Abstract
A significant body of research is devoted to examining the effect of investor sentiment on
the price response to firm-level disclosures. Absent a firm-level measure of sentiment, these
studies have used proxies of market-wide sentiment. In this paper we introduce a measure of
firm-specific investor sentiment and use it to gain insights into sentiment’s effect on the price
response to earnings surprises. We develop a simple model which predicts that the greater the
level of firm-specific sentiment, the weaker the relation between announcement return and
earnings surprise and the more negative the market response to earnings that just meet
expectations. Using our proxy for firm-specific investor sentiment, we find strong empirical
support for both of the model’s key predictions. In addition, we show that our firm-specific
measure has greater explanatory power for announcement returns than does the market-wide
sentiment measure used in prior literature.
Firm-Specific Investor Sentiment
Introduction
The effect of market sentiment on the cross-sectional and time-series properties of stock
returns is a topic of substantial research interest. 1 Among the proxies that have been used to
measure market sentiment are NYSE share turnover, the closed-end mutual fund discount, the
degree of underpricing in initial public offerings, and the aggregate level of corporate
investment. There is also a significant body of work studying how investor sentiment affects
decision-making at the firm level and the price response to firm-level disclosures. While this
type of cross-sectional research naturally calls for the use of a measure of sentiment that is
calculated at the firm level, there are no measures of this sort in the literature. As a consequence,
these studies utilize proxies of market-wide sentiment which, although varying over time, are
invariant in the cross-section. In this paper we introduce a measure of firm-specific investor
sentiment and use it to gain new insights into sentiment’s impact at the individual firm level.
Baker and Wurgler (2006) discuss the potential weakness of using a market-wide
measure of sentiment in firm-level studies. The main focus of their paper is on the construction
of an index of market sentiment (hereafter the BW index), which they use to study the timeseries properties of stock returns. However, in a final analysis, they test whether their marketwide sentiment index is useful for predicting the returns around individual firms’ earnings
announcements. While they find some evidence that it does, they caution against interpreting too
much into those results, acknowledging that measures of market-wide sentiment have limited
power to detect sentiment at the firm level. They write that “…our [time-series return] results
1
Papers include Arif and Lee (2013), Baker and Wurgler (2006), Brown and Cliff (2004, 2005), Lee et al. (1991),
Lemmon and Portniaguina (2006), Ljungqvist et al. (2006), Neal and Wheatley (1998), Qui and Welch (2004),
Ritter (1991), Stambaugh et al. (2012), and Yu and Yuan (2011).
1
are driven by the correlated correction of mispricing, but a firm’s announcement event return
picks up the expectational corrections that occur only to it alone, within its own announcement
window.” Similar limitations are expressed by Brown et al. (2012), who use the BW index to
examine the effect of sentiment on a firm’s decision whether to voluntarily release pro forma
earnings statements. 2
Our measure of firm-specific sentiment is based upon the notion that investor sentiment
reflects either optimism or pessimism about a firm’s value that is not justified by the available
information. 3 It has as its underlying premise that investors who are influenced by sentiment
would be most likely to establish their speculative positions, and thereby influence stock prices,
shortly before an earnings announcement, since that is when they expect their optimism or
pessimism to be confirmed. Establishing positions much earlier would needlessly expose them
to risks unrelated to the announcement. Those investors who are optimistic would tend to
purchase shares prior to the earnings announcement, causing prices to go up, while those who are
pessimistic would sell shares, depressing prices. Reflecting all this, our measure of firm-specific
investor sentiment is the market-adjusted return during the five trading days before the firm’s
earnings announcement.
We begin our analysis by developing a simple model of firm-level investor sentiment,
which generates theoretical predictions regarding the impact of sentiment on the stock reaction to
earnings surprises. Our model shows that the more positive (or less negative) the sentiment, the
less sensitive will returns be to unexpected earnings and the more negatively will the market
2
Other papers that examine firm-level issues using market-wide sentiment measures include Livnat and Petrovits
(2009) and Mian and Sankaraguruswamy (2012), who employ the BW index to study the effect of sentiment on the
sensitivity of stock prices to firm-specific earnings news, and Bergman and Roychowdhury (2008), who use the
Michigan Consumer Confidence Index to investigate the effect of investor sentiment on managerial forecasting
behavior. Mikhail et al. (2009) employ the Michigan Index of Consumer Expectations and Hribar and McInnis
(2012) use the BW Index to study the impact of sentiment on analysts’ forecast errors.
3
This is similar to the definition of Baker and Wurgler (2007), that sentiment is “a belief about future cash flows
and investment risks that is not justified by the facts at hand.”
2
react to an announcement that earnings exactly met expectations. Intuitively, when investors
have positive sentiment about a firm, the firm’s pre-earnings announcement stock price will
reflect an unjustifiably high earnings expectation. With expectations high, just meeting the prior
earnings forecast will be looked upon negatively. Moreover, with the stock price elevated, the
marginal return to a one-cent change in the earnings surprise will be muted. Conversely, when
investors have negative sentiment, the pre-announcement stock price will reflect an earnings
expectation that is unjustifiably low, and so meeting the prior forecast will be viewed positively.
Also, with the stock price depressed, the marginal return to a penny change in the earnings
surprise will be magnified.
Our analyses are conducted on a sample of more than 400,000 quarterly earnings
announcements made over the years 1973 through 2010. We first rank each quarter’s
announcements according to the five-day pre-announcement return (cumulated over days -6
through -2, where day 0 is the date of the earnings announcement). We then partition the
observations into deciles, with the highest (lowest) decile containing the stocks with the most
positive (negative) pre-announcement returns. Regressing the earnings announcement return
(cumulated over days -1 through +1) on unexpected earnings, we find that both the estimated
slope and intercept decline with the sentiment decile, as our model predicts. These results
support our conjecture that the five-day pre-announcement return serves as a measure of firmspecific investor sentiment. We obtain similar results when we estimate the return-earnings
surprise regression each month over our sample period using a Fama-MacBeth approach.
Results also remain substantially unchanged when we add size, book-to-market, and momentum
as explanatory variables to control for differences in firm-specific characteristics across
sentiment deciles, along with a dummy variable for firm-quarters with losses.
3
Baker and Wurgler (2006), Hribar and McInnis (2012), Mian and Sankaraguruswamy
(2012), and Seybart and Yang (2012) all conjecture that sentiment will have a greater impact on
the returns of firms that are harder to value. The evidence they present is supportive of this
conjecture. If our measure captures firm-specific investor sentiment, then we would expect to
find a similar result. Specifically, the impact of the pre-announcement return on the slope and
intercept of the return-earnings surprise relation should be greater for harder-to-value firms.
To test this prediction, we employ four different proxies, widely used in the literature, to
measure the degree of difficulty in assessing firm value: firm size (smaller firms are likely harder
to value), age (younger firms are expected to be more difficult to value), earnings volatility, and
return volatility (higher volatility is expected to be associated with harder-to-value firms). For
all four proxies, the intercept on the return-earnings surprise regression is more sensitive to
changes in investor sentiment for harder-to-value firms. For three of the four proxies, the
regression slope is also more sensitive for the harder-to-value firms. These results provide
additional support for our conjecture that the five-day pre-announcement return captures firmspecific investor sentiment.
Recognizing that a theory cannot determine the precise length of the pre-announcement
period that should be used for cumulating returns, we test the robustness of our results by
extending the window over which sentiment is measured to the two months prior to the earnings
announcement. In re-estimating our main regression using this longer window, we end the return
accumulation period six days before earnings are released. We do so in order to ensure that none
of our results are driven by the short-term return reversal pattern documented by Lehmann
(1990). In his paper, Lehmann (1990) finds that a portfolio long (short) in stocks with a positive
(negative) prior five-day return earns positive hedge returns over the next five days. While that
4
pattern cannot explain the inverse relation we document between investor sentiment and the
slope of the return-earnings surprise regression, it could partially contribute to the documented
negative relation between sentiment and the regression intercept. Using the longer window, we
find that the estimated regression slope and intercept remain decreasing functions of investor
sentiment. This is evidence that our results are robust to the length of the return accumulation
window and that they are not attributable to short-term return reversals.
Berkman et al. (2009) show that a portfolio long (short) in stocks with high (low)
dispersion of opinion generally earns a significantly positive hedge return prior to earnings
announcements and a significantly negative return afterwards. As with short-term return
reversals, this result cannot explain the negative relation we find between investor sentiment and
the slope of the return-earnings surprise regression. However, it could potentially be a
contributing factor to the negative relation between investor sentiment and the regression
intercept. To test whether differences of opinion around earnings announcements drives that
result, we re-estimate our return-earnings surprise regression, adding a proxy for the magnitude
of these differences. Including this control variable, we again find that the estimated slope and
intercept are decreasing functions of the level of investor sentiment, consistent with our model’s
prediction.
Using our theoretical model and data, we also re-examine whether the BW measure has
value in explaining the returns around earnings announcements. The evidence here is mixed.
Taking this analysis one step further, we find that our measure of firm-specific investor
sentiment has significant incremental explanatory power for earnings announcement returns over
the BW index. These results reinforce the importance of using a firm-specific measure to study
the effect of sentiment at the individual firm level.
5
The plan of this paper is as follows. In Section I we develop a model of firm-specific
investor sentiment. Our empirical methodology and hypothesis development appear in Section
II. This is followed in Section III by a description of our sample. Our full-sample empirical
results are presented in Section IV. An analysis of whether sentiment has the greatest effect in
hard-to-value firms appears in Section V. We present the results of robustness tests in Section
VI. Section VII provides a summary and conclusions.
I. Model development
In this section we develop a model which will serve as the basis of our empirical
analyses. We consider a one-period, three-date economy and two types of risky firms – one that
is traded solely by investors who are influenced by sentiment (as described below) and the other
that is traded exclusively by investors unaffected by sentiment. In all other respects the two
types of firms are indistinguishable. All investors in the economy are risk neutral and the oneperiod discount rate is assumed equal to zero. The fraction of firms in the economy that are
traded by investors influenced by sentiment is denoted by p. Each firm’s earnings for the period,
X, are normally distributed with a mean of E ( X ) > 0 and a standard deviation of σ X2 , and are
assumed equal to the period’s cash flows.
At the beginning of the period, date 0, the price of each firm, P0 , is equal to the risk
neutral investors’ prior expectation of earnings, E ( X ) . At the end of the period, date 2, each
firm’s earnings are formally announced and all firms are liquidated. Firm price at that time, P2 ,
is therefore equal to X. With probability 1 − q the value of the realized earnings leaks out at date
1, just before the formal announcement. In that case, the date 1 price of the firm, P1 , would also
equal X. The return at date 1, R1 , would be given by:
6
R1 =
X − E( X )
P0
(1)
and the date 2 return, R2 , would equal zero. The returns at dates 1 and 2 would be unrelated
when there is information leakage. Note that the right hand side of (1) is just the firm’s
unexpected earnings (normalized by price). This quantity will sometimes be denoted by UE in
our analysis below.
With probability q there is no leakage of information at date 1. In that case, investors
who are influenced by sentiment receive in its place a completely noisy signal of earnings,
denoted by B, which they incorrectly believe provides information about the realized value of X.
(The investors who are not influenced by sentiment recognize that this signal is worthless.)
These investors conjecture that B= X + ε B , where ε B is normally distributed with a mean of zero
and a finite standard deviation of σ B2 . Given their belief, the date 1 price of a firm traded by
these investors equals wE ( X ) + (1 − w) B , where w =
σ B2
. The return at date 1 for such a
σ X2 + σ B2
firm is equal to:
=
R1
wE ( X ) + (1 − w) B
−1
E( X )
The realized value of the signal, B, determines whether the investors are optimistic or
pessimistic. If B is greater than the initial earnings expectation, E ( X ) , then investors are
optimistic and the return at date 1 will be positive. If B is less than E ( X ) , then they are
pessimistic and the return will be negative. Of course, B is not observable to the researcher.
However, the date 1 return, R1 , is observable and fully captures the information in B. It is our
measure of firm-specific sentiment. Values of R1 greater than zero imply investor optimism.
7
(2)
The more positive is R1 , the greater the level of optimism and the greater will be the positive
deviation of price from underlying firm value. Values of R1 less than zero imply investor
pessimism. More negative values reflect greater levels of pessimism and a greater negative
deviation of price from underlying firm value.
The date 2 return for a firm traded by investors influenced by sentiment is:
R2 =
X − [ wE ( X ) + (1 − w) B ]
[ wE ( X ) + (1 − w) B ]
(3)
From equations (2) and (3), we can express R2 as a function of R1 and UE as follows:
R2 =
1
UE
+
−1
1 + R1 1 + R1
(4)
The final possible scenario in this economy is one in which there is no leakage of
information and the investors trading in the firm are unaffected by sentiment. In this case, the
price of the firm at date 1 is simply equal to E ( X ) and the date 1 return is zero. The date 2
return is equal to unexpected earnings, UE. As in the case of information leakage, the returns at
dates 1 and 2 would be unrelated.
Calculating the expected return at date 2, E ( R2 ) , over all possible scenarios in this
economy yields:
  1


UE
+
− 1 + (1 − p )UE  + (1 − q) *0,
E ( R2 )= q  p ∗ 
  1 + R1 1 + R1 

(5)
which can be rewritten as:
E ( R2 ) =
−qp + q (1 − p )UE + qp
8
1
UE
,
+ qp
1 + Sent
1 + Sent
(6)
where we use Sent in place of R1 in (6) to emphasize that this is our measure of firm-specific
sentiment.
II. Empirical methodology and hypothesis development
The empirical analog to equation (6) is:
R jt =
α + β1UE jt + β 2
1
1
+ β3UE jt
+ ε jt ,
1 + Dc ( Sent jt )
1 + Dc ( Sent jt )
(7)
where:
R jt = the cumulative market-adjusted return for firm j (computed using the CRSP valueweighted market index) in the three days surrounding the announcement of quarter t earnings
(trading days -1, 0, and 1, where day 0 denotes the date of the earnings announcement); 4
UE jt =
X jt − X jt − 4
Pjt − 4
, where X jt is earnings before extraordinary items for firm j in quarter t and
Pjt − 4 is the share price of firm j at the end of quarter t-4;
Sent jt = the cumulative market-adjusted return for firm j over the period from six trading days
before to two trading days before the announcement of quarter t earnings (sometimes referred to
as the pre-announcement return);
Dc ( Sent jt ) = the decile rank of Sent jt , where the rank is determined relative to the values of
Sentit for all firms, i, that announce in the same calendar quarter c (higher deciles reflect greater
optimism/less pessimism); and
ε jt = the regression residual for firm j and quarter t.
4
Our results are robust to the use of raw returns, rather than market-adjusted returns.
9
We use a three-day earnings announcement window in order to mitigate the effect of
possible database errors with respect to the exact date of the earnings announcement as well as to
account for uncertainty over whether earnings were announced after hours. The earnings
announcement date used for our analysis is that reported in Compustat, unless the announcement
date also appears in the I/B/E/S database and the I/B/E/S date is the earlier of the two. In that
case, the I/B/E/S date is used instead. If I/B/E/S also specifies the exact time of the
announcement and it is after trading hours, then the earnings announcement date is advanced by
one trading day. We use a firm’s four-quarters ago earnings, rather than the consensus analyst
forecast, as our expectation of current-quarter earnings in order to be able to retain in our sample
those firms without analyst coverage. Recognizing that the cross-sectional distribution of our
sentiment measure changes over time, we use the sentiment decile in our regression, rather than
the value of the measure, itself. This allows us to maintain comparability across our sample
period and is consistent with Baker and Wurgler (2006), Hribar and McInnis (2012), and Seybart
and Yang (2012). 5 We use a relatively short period of five trading days prior to the earnings
announcement window to measure sentiment because individuals who believe they have private
information about upcoming earnings are more likely to trade on their information close in time
to the earnings announcement. Establishing positions earlier would subject the traders to a
greater level of risk unrelated to the forthcoming announcement. 6
Our formal hypothesis follows directly from expression (7):
5
Using the actual value of sentiment, rather than the sentiment decile, does not qualitatively affect our main results.
Using similar reasoning, Berkman et al. (2009) hypothesize that firms subject to high dispersion of opinion should
experience a share price run-up in the few days before their earnings announcements. Their empirical findings
support this conjecture. As we report later, our main results are qualitatively unchanged when we control for
dispersion of opinion.
6
10
Hypothesis: In a regression of earnings announcement return on earnings surprise, the intercept
and slope will be decreasing functions of firm-specific investor sentiment. That is, the
coefficients β 2 and β3 in expression (7) will be positive. 7
III. Data selection and descriptive statistics
Our initial sample is the set of quarterly earnings announcements contained in the merged
CRSP-Compustat quarterly file for the period from January 1973 through December 2010. We
end our sample period in December 2010 because some of our tests involve the BW index, for
which data is provided only through the end of that year. 8 From this sample we remove those
announcements for which either the announcement date is not available or is more than 90 days
after quarter-end. We also delete any announcement of quarter t earnings by firm j if one or
more of the following variables are missing from the CRSP-Compustat dataset: (1) the earnings
of firm j for quarter t-4, (2) the stock price of firm j at the end of quarter t-4, (3) firm j’s market
capitalization at the end of quarter t, (4) monthly returns for firm j over the period from 12
months before the end of quarter t through two months before the end of that quarter (this data is
used to calculate return volatility, which is defined as the standard deviation of monthly stock
returns over those months, and is also used to calculate stock price momentum), (5) firm j’s book
value of equity at the end of quarter t, and (6) any day’s return during the period from six trading
days before to one trading day after firm j’s announcement of quarter t’s earnings.
We also remove announcements for which (a) the announcement date of quarter t
earnings recorded in Compustat and the date recorded in I/B/E/S differ by more than five trading
days, (b) firm j’s price per share at the end of quarter t is less than $1, or (c) firm j’s market value
7
Since our measure of sentiment appears in the denominator of the independent variables in (7), an inverse relation
between sentiment and the slope and intercept of the return-earnings surprise relation requires that the two
regression coefficients be positive.
8
Our main results are robust to extending our sample period to the end of 2012.
11
at the end of quarter t is less than $1 million. Finally, for each calendar quarter we truncate those
observations that are at the top and bottom one percent of the unexpected earnings distribution,
in order to lessen the impact of outliers. After removing all announcements that meet any of
these criteria, we are left with a final sample of 425,170 observations.
A few of our regressions include earnings volatility as an independent variable. We
define earnings volatility as the standard deviation of the ratio of quarterly operating income
before depreciation to average total assets, calculated over the prior 20 quarters. For these tests
we drop any earnings announcement for which this operating income-to-asset ratio is not
available for at least eight of the prior 20 quarters. This reduces the sample size in these tests to
332,033 observations.
Descriptive statistics for our variables are presented in Table I, panel A. The average
cumulative market-adjusted return over the three-day earnings announcement window (days -1,
0, and 1) is 0.22 percent, while the average value of our firm sentiment measure, the cumulative
market-adjusted return over the pre-announcement period (days -6 through -2), is 0.28 percent.
These positive average returns likely reflect the earnings announcement premium, which has
been documented by Ball and Kothari (1991), Cohen et al. (2007), Frazzini and Lamont (2007),
and Barber et al. (2012), among others. The mean (median) market value of the firms in our
earnings announcement sample is $2.11 billion ($214 million), while the mean (median) bookto-market ratio is 0.99 (0.63). 9 These numbers compare to a mean (median) end-of-quarter
market value for all New York Stock Exchange firms over our sample period of $3.13 billion
($490 million) and a mean (median) book-to-market ratio of 2.06 (0.68). 10 The average age of
the firms in our sample (where age is defined as the number of years a firm’s shares have been
9
For firms with negative book value, we set the book-to-market ratio equal to zero.
As we demonstrate by adding controls for firm characteristics to our regression, our results are not driven by the
specific size and book-to-market attributes of our sample.
10
12
publicly trading) is just under 17 years. Unexpected earnings has a mean of 0.34 percent of
stock price, reflecting growth in earnings, on average, over our sample period. Return volatility
has a mean of 12.4 percent, while earnings volatility averages 3.8 percent.
The pairwise Pearson correlation coefficients for the variables of interest are presented in
Table I, panel B. As expected, age and firm size are strongly positively correlated (correlation =
0.225). Earnings volatility and return volatility are also significantly positively correlated, as
expected; however, the magnitude of the correlation, 0.005, is low. The correlation between the
market-adjusted return during the earnings announcement window and the corresponding return
during the pre-announcement period is a significantly negative 0.087. The negative correlation
is consistent with the notion that the announcement of a firm’s earnings serves to partially
correct the misvaluations of investors who are influenced by sentiment.
IV. Full-sample results
We begin our empirical analysis by estimating equation (7). Results are presented in
column 1 of Table II. Consistent with our hypothesis, and the conjecture that pre-earnings
announcement returns are a measure of firm-specific investor sentiment, the coefficients on both
1
1
and UE jt
in (7) are positive and significant. Alternatively stated,
1 + Dc ( Sent jt )
1 + Dc ( Sent jt )
the sensitivity of announcement returns to earnings surprises is decreasing in investor sentiment
as is the expected stock price reaction to an announcement that earnings just met expectations. 11
To ensure that our results are not driven by differences in firm characteristics across
sentiment deciles, we add ln(size), ln(book-to-market ratio), momentum, and a dummy variable
11
Cohen et al (2007) show that unexpected delays in earnings announcements are accompanied by negative returns
in the pre-announcement period. To determine whether this phenomenon has an impact on our results, we repeat
our analysis, excluding any firm-quarter observation for which the earnings announcement date falls more than
seven days after the announcement date in the corresponding quarter of the previous year. Untabulated results are
similar to those presented in Table II.
13
equal to one for firm-quarters with losses as explanatory variables to (7). 12 As reported in
column 2 of Table II, the coefficients on our independent variables,
UE jt
1
and
1 + Dc ( Sent jt )
1
, remain significantly greater than zero and are of similar magnitudes to the
1 + Dc ( Sent jt )
values reported in column 1 of the table. From this we conclude that our findings are not due to
differences in firm characteristics across the sentiment deciles.
In order to reduce the potential impact of influential observations of UE jt on our results,
we re-estimate (7) using the rank of UE jt , in place of UE jt :
R jt =
α + β1 Rank (UE jt ) + β 2
We do not replace the variable
1
1
+ β3 Rank (UE jt )
+ ε jt .
1 + Dc ( Sent jt )
1 + Dc ( Sent jt )
(8)
1
by the rank of that variable since we are already
1 + Dc ( Sent jt )
using the decile rank of Sent jt , rather than Sent jt , itself, in the regression. The results of
estimating this rank regression appear in column 3 of Table II. The coefficients on
1
1
and Rank (UE jt )
are significantly positive, confirming that
1 + Dc ( Sent jt )
1 + Dc ( Sent jt )
influential observations are not driving our results.
We also employ a Fama-MacBeth approach and estimate regression (8) for each month
of our sample period. 13 The averages of the monthly coefficients on
Rank (UE jt )
1
and
1 + Dc ( Sent jt )
1
are reported in column 4 of Table II. Both are reliably positive.
1 + Dc ( Sent jt )
12
For firms with negative book value, we set ln(book-to-market ratio) equal to zero.
We exclude from our regressions the 12 months at the beginning of our sample period that have fewer than 100
observations.
13
14
Figure I, panel A (panel B) plots the estimated value of β 2 ( β3 ) for each month of our sample
period. As is clear from the figure, the months with positive coefficients are not concentrated
within any narrow time frame. Rather, they are spread out over our entire sample period. The
coefficient, β 2 , is positive in 91.1 percent of the months, while β3 is positive for 53.7 percent of
the months. 14
We alternatively calculate unexpected earnings using as our expectation the consensus
analyst forecast, computed as the average of all valid individual forecasts in I/B/E/S that are
outstanding seven days before the earnings announcement. 15 Unexpected earnings in this case is
equal to the I/B/E/S reported earnings minus the consensus analyst forecast. (We normalize each
earnings surprise by the stock price at the end of the prior quarter.) As before, we truncate the
highest and lowest one percent of the unexpected earnings observations. The requirement that
I/B/E/S consensus forecasts be available, along with the truncation of the top and bottom one
percent of the unexpected earnings observations, reduces our sample to slightly more than
220,000 announcements. The results of re-estimating regression (8) using this alternative
calculation of unexpected earnings are presented in panel A of Table III. Both the coefficient on
1
1
and the coefficient on Rank (UE jt )
remain significantly greater than
1 + Dc ( Sent jt )
1 + Dc ( Sent jt )
zero.
14
When estimating panel regressions it is common practice to adjust the standard errors for correlation over time
and across firms (usually referred as double clustering). This procedure is used because market-wide shocks may
induce correlation between firms at a given point in time, and persistent firm-specific shocks may induce correlation
across time for a given firm. In addition, researchers use the White correction to account for potential crosssectional heteroskedasticity (see Thompson, 2011). Our research design, though, eliminates the need to adjust our
standard errors for auto-correlation and heteroskedasticity. Our firm-specific sentiment measure is the pre-earnings
announcement return; as such, the errors should not be correlated over time. Moreover, we use the rank of the
sentiment measure; hence, market-wide shocks, while affecting all firms, should not affect their decile ranking. This
allays concerns over the correlation between firms at a given point in time and heteroskedasticity. Nevertheless, we
replicated our analysis using both double clustering (over time and across firms) and employing the White
correction and obtained qualitatively similar results.
15
A valid forecast is one that is issued no more than 90 days prior to the earnings announcement.
15
The I/B/E/S sample also gives us a convenient alternative means of testing the hypothesis
that the greater the level of firm-specific sentiment, the less positive (or more negative) the stock
price reaction to an announcement of earnings that just meets expectations (UE=0). Using the
subsample of observations for which realized earnings equal the consensus forecast, we regress
the market-adjusted announcement return on the announcement’s sentiment decile. As we report
in Table III, panel B, the coefficient on Dc ( Sent jt ) in this regression is negative and significant,
consistent with our conjecture.
While not the main focus of their paper, Baker and Wurgler (2006) examine whether
their market-wide sentiment index is useful for predicting the returns around individual firms’
earnings announcements. Although they find some evidence that it does, they acknowledge that
their methodology has limited power in this setting since their index is not explicitly designed to
detect sentiment at the firm level. We re-examine the effectiveness of their measure at the firm
level using our theoretical model and our data, by substituting the BW index for our measure of
sentiment in expression (7):
R jt =
α ′ + β1′UE jt + β 2′
1
1
+ β3′UE jt
+ ε jt ,
1 + BWSentt
1 + BWSentt
(9)
where BWSentt is the value of the BW index during the last month of quarter t. As reported in
Table IV, column 1, the regression results are mixed. Consistent with the hypothesis, the
coefficient, β 2′ , on
1
is significantly positive. However, contrary to the hypothesis,
1 + BWSentt
the coefficient, β3′ , on UE jt
1
is insignificantly different from zero.
1 + BWSentt
We take this analysis one step further and test whether our measure of firm-specific
investor sentiment has incremental explanatory power for earnings announcement returns over
16
the BW index. We do so by adding the two independent variables,
UE jt
1
and
1 + BWSentt
1
, to regression (7):
1 + BWSentt
R jt =
α + β1UE jt + β 2
1
1
+ β3UE jt
1 + Dc ( Sent jt )
1 + Dc ( Sent jt )
1
1
+ β 2′
+ β3′UE jt
+ ε jt .
1 + BWSentt
1 + BWSentt
(10)
Coefficient estimates from this regression are reported in Table IV, column 2. The coefficients
on
1
1
and UE jt
remain positive and significant, providing evidence
1 + Dc ( Sent jt )
1 + Dc ( Sent jt )
that our firm-specific sentiment measure has incremental explanatory power for announcement
window returns over the BW index. As before, the coefficient β 2′ is significantly positive,
while the coefficient β3′ is insignificantly different from zero. These results reinforce the
importance of using a firm-specific measure to study the effect of sentiment at the individual
firm level.
V. The impact of sentiment on hard-to-value firms
Baker and Wurgler (2006), Hribar and McInnis (2012), Mian and Sankaraguruswamy
(2012), and Seybart and Yang (2012) all conjecture that sentiment will have a greater effect on
returns for subsets of firms that are harder to objectively value. The empirical evidence they
present is consistent with this conjecture. If our measure captures firm-specific investor
sentiment, then we should find a similar result. Specifically, the impact of the pre-announcement
return on the slope and intercept of the return-earnings surprise relation should be greater for
harder-to-value firms.
17
To test this we estimate the following regression:
R jt =
β 0 + β1UE jt + β 2
1
1
+ β3UE jt
1 + Dc ( Sent jt )
1 + Dc ( Sent jt )
1
1
+γ 1 Dc ( HTV jt ) + γ 2 Dc ( HTV jt )
+ γ 3 Dc ( HTV jt ) ∗ UE jt
+ ε jt ,
1 + Dc ( Sent jt )
1 + Dc ( Sent jt )
(11)
where:
HTV jt = the value of the proxy (see below) used to measure the difficulty of valuing firm j in
quarter t and
Dc ( HTV jt ) = the decile rank of HTV jt , where the rank is determined relative to the values of
HTVit for all firms, i, that announce in the same calendar quarter c. 16 If the market-adjusted
return over days -6 through -2 is, indeed, a measure of firm-level investor sentiment, then the
coefficients γ 2 and γ 3 in expression (11) should be positive. We proxy for the extent to which a
firm is difficult to value by four different variables: firm size (smaller firms are likely harder to
value), age (it is expected that younger firms will be more difficult to value), earnings volatility,
and return volatility (higher volatility is likely associated with harder-to-value firms). 17 We
expect that firm-specific investor sentiment will have the greatest impact on the return-earnings
surprise relation for the smallest and youngest firms, and for those with the greatest earnings
volatility and return volatility.
Table V reports the results of estimating (11) over our entire sample period for each hardto-value proxy. The coefficient γ 2 is significantly positive for all the proxies – age (column 1),
size (column 2), earnings volatility (column 3), and return volatility (column 4). The coefficient
γ 3 is positive and significant for three of the four proxies (all but return volatility, where it is
16
The deciles are constructed such that firms in higher deciles are more difficult to value.
These proxies are also used by Baker and Wurgler (2006), Hribar and McInnis (2012), Mian and
Sankaraguruswamy (2012), and Seybert and Yang (2012).
17
18
negative). These findings provide support for the hypothesis that the intercept and slope of the
return-earnings surprise relation are more sensitive to firm-specific investor sentiment for firms
that are harder to value. As such, the results are also supportive of our conjecture that the fiveday pre-announcement return captures firm-specific investor sentiment.
VI. Robustness tests
Recognizing that theory cannot determine the precise length of the pre-announcement
period that should be used to measure sentiment, we test the robustness of our results by
extending the return measurement window to the two months prior to the earnings
announcement. In re-estimating our main regression we exclude from this return accumulation
period the returns over days -6 through -2. We do so in order to ensure that none of our results
are driven by the short-term return reversal pattern documented by Lehmann (1990). In his
paper, Lehmann (1990) finds that stocks whose prices increase (decrease) over the previous five
days tend to decline (rise) over the next five. While this pattern cannot account for the inverse
relation we find between investor sentiment and the sensitivity of announcement returns to
earnings surprises, it could potentially be a contributing factor to the negative relation between
investor sentiment and the intercept of the return-earnings surprise regression.
We estimate the following regression using the longer return accumulation window:
R jt =
α + β1UE jt + β 2
1
1
+ β3UE jt
+ β 4 Sent jt + ε jt ,
1 + Dc ( Sent ′jt )
1 + Dc ( Sent ′jt )
(12)
where Sent ′jt is the cumulative market-adjusted return for firm j over the period from 60 trading
days before to seven trading days before the announcement of quarter t earnings and Dc ( Sent ′jt )
is the decile rank of Sent ′jt , where the rank is determined relative to the values of Sentit′ for all
firms, i, that announce in the same calendar quarter c. As a control variable, we also include in
19
the regression the cumulative market-adjusted return over days -6 through -2. Results of
estimating (12) are reported in Table VI, panel A. Although smaller than their counterparts in
Table II, column 1, both β 2 and β3 are reliably greater than zero. This is evidence that our
results are robust to the length of the pre-announcement return accumulation window and that
they are not attributable to short-term return reversals.
That the cumulative market-adjusted return over days -60 through -7 reflects firmspecific investor sentiment invites the question of whether our five-day sentiment measure
provides incremental explanatory power over this longer-window measure. We expect that it
will have additional explanatory power, given that investors who are influenced by sentiment are
arguably more likely to establish their speculative positions shortly before an earnings
announcement. To test this conjecture, we add our measure as a main effect to regression (12)
and also interact it with the longer-term sentiment measure:
R jt =
α + β1UE jt + β 2
1
1
1
+ β 3UE jt
+ β4
1 + Dc ( Sent ′jt )
1 + Dc ( Sent ′jt )
1 + Dc ( Sent jt )
1
1
1
1
+ β5
x
+ β 6UE jt
x
+ ε jt .
1 + Dc ( Sent ′jt ) 1 + Dc ( Sent jt )
1 + Dc ( Sent ′jt ) 1 + Dc ( Sent jt )
(13)
Results of estimating (13) are presented in Table VI, panel B. As expected, the coefficient on the
five-day measure of firm-specific investor sentiment, β 4 , is significantly greater than zero, as
are the coefficients on the interaction terms between the short-window and longer-window
measures, β5 and β 6 . Moreover, the coefficient on
1
, β 2 , is no longer positive,
1 + Dc ( Sent ′jt )
consistent with the market-adjusted return over days -6 through -2 being the more powerful of
the two sentiment measures.
20
We next test whether differences of opinion could be driving any of our results. In the
presence of differences of opinion and short-sales constraints, Miller (1977) conjectures that
stock prices will be positively biased; investors with favorable information will bid prices up,
while those with unfavorable information will be constrained from fully realigning prices to
fundamental value. Berkman et al. (2009) posit that this bias will be stronger just before
earnings announcements (when investors trade on their private information), but will dissipate
once earnings are announced and differences of opinion are reduced. Consistent with their
conjecture, they find that a hedge portfolio long in stocks with high dispersion of opinion and
short in stocks with low dispersion generally earns a significantly positive return during the two
weeks before the earnings announcement and a significantly negative return during the
subsequent two weeks. As with short-term return reversals, this phenomenon cannot account for
the negative relation we find between investor sentiment and the sensitivity of returns to earnings
surprises. However, it, too, could partially contribute to the documented negative relation
between sentiment and the intercept of the return-earnings surprise regression. To test whether
differences of opinion is driving the intercept result, we re-estimate (7), adding an explanatory
variable to capture the degree to which there are differences of opinion prior to an earnings
announcement:
R jt =
α + β1UE jt + β 2
1
1
+ β3UE jt
+ β 4 Dc ( Diff jt ) + ε jt ,
1 + Dc ( Sent jt )
1 + Dc ( Sent jt )
(14)
where Diff jt is our proxy (see below) for differences of opinion prior to firm j’s announcement
of quarter t earnings and Dc ( Diff jt ) is the decile rank of Diff jt , where the rank is determined
relative to the values of Diffit , for all firms, i, that announce earnings in calendar quarter c
(higher deciles reflect greater differences of opinion). We employ two separate proxies,
21
earnings volatility and return volatility. Regression results are reported in Table VI, panel C.
Consistent with Berkman et al. (2009), the coefficient on the earnings volatility variable (column
1) and on the return volatility variable (column 2) are significantly negative. More importantly
for our purposes, the coefficients, β 2 and β3 , remain significantly greater than zero and are of a
magnitude similar to those estimated for regression (7), when no difference of opinion proxy is
included. We infer from this that our results are not driven by differences of opinion.
VII. Summary and conclusions
This paper tests the conjecture that firm-specific investor sentiment can be measured by a
firm’s pre-earnings announcement return. To guide our empirical analysis, we develop a simple
theoretical model to show how firm-level investor sentiment is expected to impact the relation
between earnings announcement returns and unexpected earnings. The model leads to the
predictions that the return-earnings surprise relation will be weaker the greater the level of
investor sentiment and that the announcement return in cases where earnings just meet
expectations will be more negative the greater the level of investor sentiment. These predictions
are a consequence of the role that earnings play in mitigating investors’ misvaluation of the firms
in which they invest. Our empirical analysis provides support for each of these predictions. We
also conjecture that the impact of investor sentiment on the return-earnings surprise relation will
be greater for harder-to-value firms than for those that are easier to value. Our empirical results
are supportive of this prediction as well.
Market-wide measures of sentiment have been used in the past to examine cross-sectional
and time-series properties of stock returns. Absent a firm-specific measure of sentiment,
researchers have also employed these market-wide measures to study the effect of sentiment on
22
decisions and prices at the individual firm level. By developing a firm-level measure of
sentiment, we provide a tool that can be used in future research on this topic.
23
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25
Figure I
Monthly coefficients from regressions of earnings announcement return on firm-specific sentiment
This figure reports the β 2 (panel A) and β 3 (panel B) coefficients for monthly regressions of
α + β1UE jt + β 2
R jt =
1
1 + Dc ( Sent jt )
+ β 3UE jt
1
1 + Dc ( Sent jt )
+ ε jt , where R jt is the cumulative market-
adjusted return (computed using the CRSP value-weighted market index) over the three-day window
(days -1, 0, and 1) surrounding the announcement of quarter t earnings for firm j; UE jt is the difference
between firm j's earnings before extraordinary items for quarter t (Compustat item EPSPXQ) and for quarter
t-4, normalized by the share price of firm j at the end of quarter t-4, and then multiplied by 100 (UE is
truncated at the top and bottom one percent of its distribution); Sent jt is the cumulative market-adjusted
return for firm j over the period from six trading days before to two trading days before the announcement
of quarter t earnings; Dc ( Sent jt ) is the decile rank of Sent jt , where the rank is determined relative to the
values of Sentit for all firms, i, that announce in calendar quarter c. Our sample period spans the years
1973 through 2010.
Panel A: Monthly coefficients on
0.3
1
1 + D ( Sent )
0.25
0.2
0.15
0.1
0.05
0
-0.1
Dec-73
Dec-74
Dec-75
Dec-76
Dec-77
Dec-78
Dec-79
Dec-80
Dec-81
Dec-82
Dec-83
Dec-84
Dec-85
Dec-86
Dec-87
Dec-88
Dec-89
Dec-90
Dec-91
Dec-92
Dec-93
Dec-94
Dec-95
Dec-96
Dec-97
Dec-98
Dec-99
Dec-00
Dec-01
Dec-02
Dec-03
Dec-04
Dec-05
Dec-06
Dec-07
Dec-08
Dec-09
Dec-10
-0.05
Panel B: Monthly coefficients on UE
1
1 + D ( Sent )
6
4
2
0
-2
-6
Dec-73
Dec-74
Dec-75
Dec-76
Dec-77
Dec-78
Dec-79
Dec-80
Dec-81
Dec-82
Dec-83
Dec-84
Dec-85
Dec-86
Dec-87
Dec-88
Dec-89
Dec-90
Dec-91
Dec-92
Dec-93
Dec-94
Dec-95
Dec-96
Dec-97
Dec-98
Dec-99
Dec-00
Dec-01
Dec-02
Dec-03
Dec-04
Dec-05
Dec-06
Dec-07
Dec-08
Dec-09
Dec-10
-4
Table I
Descriptive statistics and correlation coefficients
This table provides sample descriptive statistics for our variables (panel A) and the average of the quarterly pairwise Pearson correlation
coefficients (panel B). For a given quarter, t, earnings announcement return is equal to the cumulative market-adjusted return (using
the CRSP value-weighted market index) over the three-day window (days -1, 0, and 1) surrounding the announcement of quarter t
earnings. Sentiment is defined as the cumulative market-adjusted return over the pre-announcement period (days -6 through -2), prior
to the announcement of quarter t earnings. Market value is the end-of-quarter t market value of equity. Book-to-market is the end-ofquarter t book value of equity divided by the end-of-quarter t market value of equity (we set this variable to zero for firms with negative
book value). Age is defined as the number of years a firm’s shares have been publicly trading, as of the end of quarter t. Unexpected
earnings is computed as the difference between earnings before extraordinary items for quarter t (Compustat item EPSPXQ) and for
quarter t-4, normalized by the share price at the end of quarter t-4, and then multiplied by 100. Unexpected earnings is truncated at
the top and bottom one percent of its distribution. Return volatility is computed as the standard deviation of monthly stock returns
over months t-12 through t-2. Earnings volatility is defined as the standard deviation of the ratio of quarterly operating income before
depreciation to average total assets, calculated over the 20 quarters prior to quarter t (with a minimum of 8 out of 20 quarters required
for computation). Our sample period spans the years 1973 through 2010. In Panel B, numbers in bold are significant at the 10%
level or better.
Panel A: Descriptive statistics
Variable
No. of
observations
Mean
Median
Std. Dev.
25th
percentile
75th
percentile
Earnings announcement return
425,170
0.22%
0.000
0.082
-0.032
0.033
Sentiment
425,170
0.28%
-0.001
0.070
-0.029
0.028
Market value
425,170
2,105
214
11,238
55
903
Book-to-market
425,170
0.987
0.632
7.057
0.371
1.012
Age
425,170
16.832
12.166
14.981
6.333
21.750
Unexpected earnings
425,170
0.335
0.186
3.917
-0.577
0.923
Return volatility
425,170
0.124
0.105
0.086
0.072
0.151
Earnings volatility
332,033
0.038
0.014
2.788
0.007
0.025
Earnings
announcement
return
Sentiment
Market
value
Book-tomarket
Age
Unexpected
earnings
Sentiment
-0.0868
1
Market value
-0.0023
-0.0042
1
Book-to-market
0.0028
0.0030
-0.0143
1
Age
-0.0004
-0.0153
0.2251
-0.0075
1
Unexpected earnings
0.1397
0.0494
-0.0049
-0.0036
-0.0116
1
Return volatility
-0.0124
0.0451
-0.0792
-0.0042
-0.2170
0.0720
1
Earnings volatility
0.0006
-0.0013
-0.0017
-0.0009
-0.0072
0.0017
0.0048
Panel B: Pairwise Pearson correlation coefficients
Return
volatility
Table II
Earnings announcement returns and firm-specific sentiment
α + β1UE jt + β 2
This table reports the coefficients for regressions of R jt =
1
1 + Dc ( Sent jt )
+ β 3UE jt
1
1 + Dc ( Sent jt )
+ ε jt ,
where R jt is the cumulative market-adjusted return (using the CRSP value-weighted market index) over the three-day
window (days -1, 0, and 1) surrounding the announcement of quarter t earnings for firm j; UE jt is the difference
between firm j's earnings before extraordinary items for quarter t (Compustat item EPSPXQ) and for quarter t-4,
normalized by the share price of firm j at the end of quarter t-4, and then multiplied by 100 (UE is truncated at the top
and bottom one percent of its distribution); Sent jt is the cumulative market-adjusted return for firm j over the period
from six trading days before to two trading days before the announcement of quarter t earnings; Dc ( Sent jt ) is the decile
rank of Sent jt , where the rank is determined relative to the values of Sentit for all firms, i, that announce in calendar
quarter c; ln( Market value jt ) is the log of the end-of-quarter t market value of equity of firm j; ln( Book -to-market jt ) is
the log of the ratio of end-of-quarter t book value of equity of firm j to end-of-quarter t market value of equity of firm j
(we set the value of this variable to zero for firms with negative book value); Momentum jt is the cumulative monthly
stock return for firm j over months t-12 through t-2. DLoss is equal to one if the earnings of firm j in quarter t are
1
negative and zero, otherwise. For column 4, UE jt and UE jt
are replaced by the rank of each of these
1 + Dc ( Sent jt )
variables. Below each coefficient value is the corresponding t-statistic. Our sample period spans the years 1973 through
2010. In column 2, significance is determined based on the time-series distribution of equally weighted monthly
coefficient estimates. ***=Significant at the 1% level; **=significant at the 5% level; *=significant at the 10% level (all
one-tailed).
Coefficient estimates
Panel regression
Panel regression
including control
variables
Rank regression
Fama-MacBeth
monthly rank
regressions
(1)
(2)
(3)
(4)
Intercept
-0.007***
-0.00003
-0.0319***
-0.035***
UE
0.277***
0.245***
48.0
42.0
0.011E-5***
0.013E-5***
60.3
35.6
-32.0
-0.7
Rank(UE)
1
1 + D ( Sent )
UE
1
1 + D( Sent )
Rank (UE )
-67.7
-41.2
0.044***
0.049***
0.040***
0.043***
43.5
48.5
21.2
12.7
0.096***
0.093***
4.3
4.2
0.044E-6***
0.036***
5.7
2.5
0.038
425,170
0.046
436
1
1 + D( Sent )
-0.0004***
ln(Market value )
-7.1
ln(Book-to-market )
.0025***
Momentum
-0.003***
Dloss
-0.018***
15.8
-15.3
-57.8
Adjusted R-squared
No. of observations
0.024
425,170
0.033
425,170
Table III
Earnings announcement returns and firm-specific sentiment for I/B/E/S subsample
Panel A reports the coefficients for regressions of
1
1
R jt =
α + β1 Rank (UE jt ) + β 2
+ β 3 Rank (UE jt )
+ ε jt , for all observations with valid
1 + Dc ( Sent jt )
1 + Dc ( Sent jt )
individual forecasts in I/B/E/S that are outstanding seven days before the earnings announcement, where R jt is
the cumulative market-adjusted return (using the CRSP value-weighted market index) over the three-day window
(days -1, 0, and 1) surrounding the announcement of quarter t earnings for firm j; UE jt is the difference between
firm j's earnings as reported on I/B/E/S and the I/B/E/S consensus forecast (computed as the average of all valid
individual forecasts in I/B/E/S that are outstanding seven days before the earnings announcement), normalized
by the share price of firm j at the end of quarter t-1, and then multiplied by 100 (in this case, UE is truncated at
the top and bottom one percent of its distribution); Rank (UE jt ) is the rank of UE jt ; Sent jt is the cumulative
market-adjusted return for firm j over the period from six trading days before to two trading days before the
announcement of quarter t earnings; Dc ( Sent jt ) is the decile rank of Sent jt , where the rank is determined
relative to the values of Sentit for all firms, i, that announce in calendar quarter c. Panel B reports the coefficient
for a regression of R jt on Dc ( Sent jt ) for the subsample of I/B/E/S observations with UE jt = 0 . Below each
coefficient value is the corresponding t-statistic. Our sample period spans the years 1983 through 2010.
***=Significant at the 1% level; **=significant at the 5% level; *=significant at the 10% level (all one-tailed).
Panel A: I/B/E/S subsample results
Coefficient estimates
Intercept
Panel regression
-0.041***
-64.3
Rank(UE)
0.030E-5***
60.0
1
1 + D ( Sent )
Rank (UE )
1
1 + D( Sent )
Adjusted R-squared
No. of observations
Panel B: Results for I/B/E/S subsample with UE = 0
Coefficient estimates
Intercept
0.034***
13.3
0.014E-5***
6.8
0.070
220,495
Panel regression
0.011***
6.9
D ( Sent )
-0.003***
-11.1
Adjusted R-squared
No. of observations
0.012
9,700
Table IV
Earnings announcement returns, Baker-Wurgler (BW) index, and firm-specific sentiment
Columns (1) and (2) report the coefficients for regressions of R jt =
α ′ + β1′UE jt + β 2′
α + β1UE jt + β 2
and R jt =
1
1 + Dc ( Sent jt )
+ β 3UE jt
1
1 + Dc ( Sent jt )
+ β 2′
1
1 + BWSentt
1
1 + BWSentt
+ β 3′UE jt
+ β 3′UE jt
1
1 + BWSentt
1
1 + BWSentt
+ ε jt
+ ε jt ,
respectively, where Rjt is the cumulative market-adjusted return (using the CRSP value-weighted market index) over the
three-day window (days -1, 0, and 1) surrounding the announcement of quarter t earnings for firm j; UEjt is the
difference between firm j's earnings before extraordinary items for quarter t (Compustat item EPSPXQ) and for quarter t4, normalized by the share price of firm j at the end of quarter t-4, and then multiplied by 100 (UE is truncated at the
top and bottom one percent of its distribution); BWSentt is the value of the Baker-Wurgler (BW) index of market
sentiment during the last month of quarter t. Sentjt is the cumulative market-adjusted return for firm j over the period
from 6 trading days before to two trading days before the announcement of quarter t earnings; Dc(Sentjt) is the decile
rank of Sentjt, where the rank is determined relative to the values of Sentit for all firms, i, that announce in calendar
quarter c. Our sample period spans the years 1973 through 2010. Below each coefficient value is the corresponding tstatistic. ***=Significant at the 1% level; **=significant at the 5% level; *=significant at the 10% level (all one-tailed).
Intercept
UE
With BW index only
(1)
With BW index and firm-specific
sentiment measure
(2)
0.001***
-0.007***
8.7
-32.2
0.29***
0.27***
89.4
47.6
0.0443***
1
1 + D ( Sent )
UE
0.096***
1
1 + D ( Sent )
1
1 + BWSent
UE
43.5
1
1 + BWSent
Adjusted R-squared
No. of observations
4.3
0.00012***
0.00013***
3.5
3.9
-0.001
-0.00126
-1.4
-1.3
0.0195
425,170
0.0240
425,170
Table V
Earnings announcement returns, firm-specific sentiment, the BW index, and hard-to-value firms
This table reports the coefficients for regressions of R jt =
β 0 + β1UE jt + β 2
+γ 1 Dc ( HTV jt ) + γ 2 Dc ( HTV jt )
1
1
+ β 3UE jt
1 + Dc ( Sent jt )
1 + Dc ( Sent jt )
1
1
+ γ 3 Dc ( HTV jt ) ∗ UE jt
+ ε jt , where R jt is the cumulative market-adjusted return
1 + Dc ( Sent jt )
1 + Dc ( Sent jt )
(using the CRSP value-weighted market index) over the three-day window (days -1, 0, and 1) surrounding the announcement of quarter t
earnings for firm j; UE jt is the difference between firm j's earnings before extraordinary items for quarter t (Compustat item EPSPXQ) and for
quarter t-4, normalized by the share price of firm j at the end of quarter t-4, and then multiplied by 100 (UE is truncated at the top and
bottom one percent of its distribution); Sent jt is the cumulative market-adjusted return for firm j over the period from six trading days
before to two trading days before the announcement of quarter t earnings; Dc ( Sent jt ) is the decile rank of Sent jt , where the rank is
determined relative to the values of Sentit for all firms, i, that announce in calendar quarter c; HTVjt is the value of the proxy (Age, Size,
Earnings volatility, and Return volatility) used to measure how hard it is to value firm j in quarter t; Dc ( HTV jt ) is the decile rank of HTV jt ,
where the rank is determined relative to the values of HTV jt for all firms, i, that announce in the same calendar quarter c (higher deciles
mean that the firms are more difficult to value). Age is defined as the number of years a firm’s shares have been publicly trading, as of the
end of quarter t; Size is the end-of-quarter t market value of equity; Earnings volatility is defined as the standard deviation of the ratio of
quarterly operating income before depreciation to average total assets, calculated over the 20 quarters prior to quarter t (with a minimum of 8
out of 20 quarters required for computation); Return volatility is computed as the standard deviation of monthly stock returns over months t12 through t-2. Our sample period spans the years 1973 through 2010. Below each coefficient value is the corresponding t-statistic.
***=Significant at the 1% level; **=significant at the 5% level; *=significant at the 10% level (for t-statistics, all significance levels are onetailed).
Age
Size
Earnings volatility
(1)
(2)
(3)
(4)
Intercept
-0.01***
-20.34
-0.013***
-25.09
-0.002***
-3.37
-0.002***
-3.6
UE
0.28***
48.13
0.29***
49.79
0.28***
40.93
0.28***
48.13
1
1 + D ( Sent )
0.047***
23.4
0.069***
34.1
0.042***
15.0
0.036***
14.1
0.13***
4.23
0.508***
18.39
-0.007
-0.15
0.294***
6.17
-0.0004***
-5.54
-0.0010***
-11.82
-0.0010***
-10.9
-0.0010***
-12.6
0.0008**
2.31
0.0051***
14.06
0.0006*
1.46
0.0015***
4.17
0.007*
1.68
0.121***
25.14
0.013***
2.45
-0.026***
-5.05
0.0241
425,170
0.0259
425,170
0.023
332,033
0.0248
425,170
Coefficient estimates
UE
1
1 + D ( Sent )
D( HTV )
D ( HTV )
1
1 + D ( Sent )
D( HTV ) ∗ UE
1
1 + D( Sent )
Adjusted R-squared
No. of observations
Return volatility
Table VI
Robustness tests
α + β1UE jt + β 2
Panel A reports the coefficients for regressions of R jt =
1
1
+ β 3UE jt
+ β 4 Sent jt + ε jt ,
1 + Dc ( Sent ′jt )
1 + Dc ( Sent ′jt )
where Rjt is the cumulative market-adjusted return over the three-day window (days -1, 0, and 1) surrounding the
announcement of quarter t earnings for firm j; Sent’jt is the cumulative market-adjusted return for firm j over the period
from 60 trading days before to seven trading days before the announcement of quarter t earnings; Dc(Sent’jt) is the decile
rank of Sent’jt, where the rank is determined relative to the values of Sent’it for all firms, i, that announce in calendar quarter
c; Sentjt is the cumulative market-adjusted return for firm j over the period from six trading days before to two trading days
before the announcement of quarter t earnings; and UEjt is the difference between firm j's earnings before extraordinary
items for quarter t (Compustat item EPSPXQ) and for quarter t-4, normalized by the share price of firm j at the end of
quarter t-4, and then multiplied by 100 (UE is truncated at the top and bottom one percent of its distribution). Panel B
reports the coefficients for regressions of
R jt =
α + β1UE jt + β 2
+ β 6UE jt
1
1
1
1
1
+ β 3UE jt
+ β4
+ β5
x
1 + Dc ( Sent ′jt )
1 + Dc ( Sent ′jt )
1 + Dc ( Sent jt )
1 + Dc ( Sent ′jt ) 1 + Dc ( Sent jt )
1
1
+ ε jt , where Dc(Sentjt) is the decile rank of Sentjt, where the rank is determined relative to
x
1 + Dc ( Sent ′jt ) 1 + Dc ( Sent jt )
the values of Sentit for all firms, i, that announce in calendar quarter c. Panel C reports the coefficients for regressions of
R jt =
α + β1UE jt + β 2
1
1
+ β 3UE jt
+ β 4 Dc ( Diff jt ) + ε jt , where Diffjt is a proxy for difference of opinion
1 + Dc ( Sent jt )
1 + Dc ( Sent jt )
prior to firm j’s announcement of quarter t earnings (the two proxies used are earnings volatility and return volatility); Dc(Diffjt)
is the decile rank of Diffjt, where the rank is determined relative to the values of Diffit, for all firms, i, that announce earnings
in calendar quarter c; Earnings volatilityjt is the standard deviation of the ratio of quarterly operating income before
depreciation to average total assets for firm j, calculated over the 20 quarters prior to quarter t; Return volatilityjt is the
standard deviation of monthly stock returns for firm j (calculated over months t-12 through t-2). Our sample period spans
the years 1973 through 2010. Below each coefficient value is the corresponding t-statistic. ***=Significant at the 1% level;
**=significant at the 5% level; *=significant at the 10% level (all one-tailed).
Panel A: Extending sentiment measurement window to (-60, -7) prior to earnings announcements
Intercept
-0.0005**
-2.1
0.287***
UE
49.8
1
1 + D( Sent′)
UE
1
1 + D( Sent′)
Sent
0.010***
9.9
0.081***
3.7
-0.110***
-62.4
Adjusted R-squared
No. of observations
0.0286
425,170
Table VI - Continued
Panel B: Incorporating both long and short sentiment measurement windows
Intercept
-0.006***
-14.0
0.279***
UE
48.2
1
1 + D( Sent′)
UE
1
1 + D( Sent′)
1
1 + D ( Sent )
1
1
x
1 + D( Sent ′) 1 + D( Sent )
UE
1
1
x
1 + D( Sent ′) 1 + D( Sent )
Adjusted R-squared
No. of observations
-0.005***
-2.9
0.059**
2.1
0.0323***
16.9
0.055***
7.5
0.175**
2.2
0.0242
425,170
Panel C: Incorporating proxy for dispersion of opinion
Intercept
UE
1
1 + D( Sent )
UE
1
1 + D( Sent )
D(diff )
Adjusted R-squared
No. of observations
Earnings volatility
Return volatility
(1)
(2)
-0.002***
-0.004***
-7.7
-12.0
0.27***
0.283***
40.9
49.0
0.046***
0.046***
39.2
45.4
0.092***
0.087***
3.5
3.9
-0.0009***
-0.0007***
-18.7
-18.0
0.023
332,033
0.0247
425,170