Beyond earnings surprise:
Incremental information about future earnings around earnings announcement
a
James S. Anga
Shaojun Zhang
Department of Finance
Division of Banking and Finance
College of Business
Nanyang Business School
Florida State University
Nanyang Technological University
Tallahassee, FL 32306, USA
Singapore 639808
Tel.: +1-850-644-8208
Tel.: +65-6790-4240
Fax: +1-850-644-4225
Fax: +65-6792-4217
E-mail: jang@cob.fsu.edu
E-mail: asjzhang@ntu.edu.sg
Corresponding author
Beyond earnings surprise:
Incremental information about future earnings around earnings announcement
Abstract
We create two quantitative measures of forward-looking information disclosed by firms
in addition to the announced earnings during the announcement period. These two
variables are time horizon dependent: one for near term future earnings and the other for
longer-term future earnings. They are extracted from revisions in analyst forecasts of
future earnings and are orthogonal to earnings surprise. We obtain several new empirical
results. 1) The R2 of the abnormal return regression doubles after including the two new
forward information variables in addition to earnings surprise. Thus, earnings surprise is
not the only source of new information, news releases and conference calls around this
period are shown to contain relevant forward information. 2) Stock price of firms that are
larger, receive greater analyst coverage, and have lower trading costs exhibits greater
anticipation of forward earnings information prior to earnings announcement. 3) Price of
these firms adjusts to new information much faster as well. Market’s acquisition of
forward information is a function of the demand for information and the ease in cashing
in the new information through trading. 4) Since our technique allows us to decompose
information in this period into three independent components, we apply it to study the
time variation in price response to different information variables before, around, and
after the stock market bubble in 1999-2000. We observe an increasingly greater
emphasis by the stock market on earnings surprise and short-term information leading to
and during the bubble period. The observation that the market overreacts to short-term
earnings provides part of the explanations why prices could deviate far from their
fundamental values in the bubble period. It is consistent with a framework in which short
horizon investors are taking over as the marginal investors. The underlying foundation
may be traced to behavioral finance such as short memory, representativeness and
availability biases that would lead many investors to put too much weight on short term
information.
JEL Classification: G14; G24; D83
Keywords: Forward information; Analyst forecast; Bubble; Earnings surprise; Price
response
1
Beyond earnings surprise:
Incremental information about future earnings around earnings announcement
1. Introduction
Current stock price reflects investors’ expectations of future earnings. Unexpected
stock returns are strongly related to changes in expectations of future earnings (e.g.,
Brown, Foster and Noreen 1985, Cornell and Landsman 1989, Abarbanell and Bushee
1997, Liu and Thomas 2000). Increasingly more listed firms disclose information
relevant to future earnings via press releases and conference calls concurrently with
quarterly earnings announcements (e.g., Tasker [1998], Frankel, Johnson and Skinner
[1999], Miller [2002]). In this paper, we study how stock market impounds such
forward-looking information in price before, around, and after earnings announcements.
More specifically, we examine four aspects of this process: 1) investors’ knowledge of
the information before earnings announcements, 2) price response to the information
around earnings announcements, 3) difference between negative and positive
information, 4) determinants of the cross-sectional difference in the price formation
process.
Many studies, following Ball and Brown (1968), have documented strong relation
between unexpected stock returns around earnings announcements and earnings
surprises.b Earnings surprise itself contains information about future earnings. A
permanent unexpected increase in earnings is associated with higher unexpected stock
return than a transitory unexpected increase (Cornell and Landsman, 1989). In this paper,
we look beyond the information contained in earnings surprises. We study the other
b
See Kothari (2001) for a review of this research area.
1
information about future earnings that becomes available to investors during the earnings
announcement period. We construct quantitative measures of such incremental
information and use them as instruments to study how the information is impounded into
stock price.
Our procedure extracts these quantitative measures from changes in analysts’
earnings forecasts before and after earnings announcement. In a closer examination of
the timing and frequency of analyst forecasts around earnings announcement, we find
many analysts revise their forecasts immediately after earnings announcement. On
average, 50% of forecast revisions occur within two weeks following earnings
announcement. More importantly, analysts frequently update their forecasts for both next
quarter’s and next year’s earnings.c This enables us to create two expectation revision
variables: one is derived from the revision in the consensus forecast of next quarter’s
earnings and the other the consensus forecast of next year’s earnings. The two revision
variables capture the earnings-relevant new information, released concurrently with last
quarter’s earnings, pertaining to short-term and medium-term future horizons. Because
analysts are aware of announced earnings before revising their forecasts, their revised
forecasts, incorporating both earnings surprise and other earnings-relevant new
information, are highly correlated with earnings surprises. In order to capture distinctive
contribution of the three information sources: earnings surprise, forecasts of next
quarter’s earnings and forecasts of next year’s earnings, we apply a two-step statistical
procedure to obtain three orthogonal variables that contain mutually exclusive
Ivkovic and Jegadeesh (2004) focus on only forecasts of next quarter’s earnings. They also find
exceptionally high frequency of analyst revisions within a few days of earnings announcement. Their
empirical evidence shows that forecast revisions immediately following earnings announcement are mainly
based on analysts’ interpretation of newly released information, whereas forecast revisions after some time
reflects information collected by analysts.
c
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information. We then study how stock price responds to information in the three
variables.
Our empirical evidence shows that not only stock prices respond significantly to the
incremental forward-looking information, such information also accounts for substantial
portion of the variation in the announcement-period abnormal returns. In addition, we
find that the magnitude and speed of price response are related to the firm investors’
demand for information and ease of stock trading. We observe smaller price response in
the announcement period for firms that are of larger size, with higher analyst coverage
and lower transaction costs. This is consistent with the prior studies that such firms have
more informative stock prices prior to announcement. We also find that the speed of
price response is faster for firms that are of larger size, with higher analyst coverage,
lower transaction costs and higher share turnover.
We make four contributions to the literature. First, we introduce two quantitative
measures of the incremental information beyond earnings surprise that are disclosed
during the earnings announcement period. Second, we demonstrate that such information
plays a significant role in explaining price changes around earnings announcement.
Third, we show that the magnitude and speed of price response to new information are
related to a firm investors’ demand for information and ease of stock trading. Fourth, we
find evidence that, during the bubble period, marginal investors choose to focus on shortterm earnings information. This is consistent with the prediction of theoretical work on
speculative price bubble that the marginal investors during a bubble are more likely to be
speculators who expect to hold stocks only for a short period.
3
The paper is organized as follows. Section 2 includes motivation, methodology and
hypotheses. Section 3 gives details on data and variable construction. Section 4 presents
our empirical results. Section 5 concludes the paper with summary and discussions.
2. Motivation, methodology and hypotheses
2.1. Motivation
Current stock price reflects investors’ expectations of future earnings. Unexpected
stock returns are strongly related to changes in expectations of future earnings (e.g.,
Brown, Foster and Noreen 1985, Cornell and Landsman 1989, Abarbanell and Bushee
1997, Liu and Thomas 2000).
------ Brief summary of the details in these papers goes here. ------
Increasingly more listed firms disclose information relevant to future earnings via
press releases and conference calls concurrently with quarterly earnings announcements
(e.g., Tasker [1998], Frankel, Johnson and Skinner [1999], Miller [2002]).
------- Shorten the following to emphasize new information about future earnings
available. --------
Under the Securities Exchange Act of 1934, exchange-listed firms are required to
release information on a quarterly basis. The Securities Exchange Commission (SEC)
issues detailed guidelines in Rule 10-01 of Regulation S-X for filing quarterly financial
statement with Form 10-Q. Following Ball and Brown (1968), many studies have
examined the empirical relation between stock price adjustments and information
4
released around earnings announcement. These studies demonstrate time and again that
earnings announcements bring new information to investors, cause them to revise their
expectations of future earnings, and ultimately lead to changes in stock prices. These
studies have used earnings surprise as the sole measure of the change in investors’
information set in response to earnings announcements.d By implicitly assuming that the
announced earnings number is a sufficient summary of the information released during
the announcement period, it precludes the possibility that other information may also be
disclosed around the same period that can have an impact on stock price. In practice,
however, firms are known to release much more information to investors than the single
quarterly earnings number. Firms not only give details in quarterly report about how the
earnings number is generated according to the Generally Accepted Accounting Principles
(GAAP), they also use other communication channels to convey additional information to
investors.
Two commonly used channels are the quarterly earnings press releases and the
conference calls. Major stock exchanges require that listed firms issue a news
announcement that states the GAAP earnings reported in Form 10-Q. For example, the
New York Stock Exchange requires “publication of interim statements as news items in
the public press” and that, “whether or not the statements are sent to shareholders, to be
sure of adequate coverage the statements should be released to newspapers and to the
national news wire services”. (See the New York Stock Exchange Listed Company
Manual, para. 203.02.) Exchanges and SEC issue few formal guidelines regarding the
d
Different measures of earnings surprise have been used in this literature. Some authors measure surprise
by the difference between current earnings and earnings of the same quarter in previous fiscal year, while
others use the difference between current earnings and the forecast generated by a time series model of
quarterly earnings. More recently, many authors use analysts’ consensus forecast as the benchmark.
5
content of these quarterly earnings press releases, other than that they must not contain
counterfactual or misleading information. In press releases, managers frequently
highlight a single aggregate "pro forma" earnings outcome, one that excludes certain
GAAP revenue and expense items. Managers often believe that presenting the GAAP
earnings number without comment is misleading because significant valuation irrelevant,
transitory, and less persistent components of GAAP earnings would not be apparent to
readers.
In addition to press releases, firms often hold conference calls when announcing
earnings. Miller (2002) find firms often bundle disclosure together with earnings
announcements. Frankel, Johnson and Skinner [1999] document that approximately 85
percent of conference calls are held concurrent with an earnings announcement. At
conference calls, managers provide supplementary information beyond that contained in
financial statements and related press releases. For example, Tasker [1998] identifies
new products and management’s guidance on interpreting financial statement line items
as topics of analysts’ most frequently asked questions in her detailed analysis of the
content of twelve conference calls in the high technology industry. Bowen, Davis and
Matsumoto (2002) find that conference calls increase the total information available
about a firm.
Given the considerable evidence that managers regularly engage in voluntary
disclosures around earnings announcement, it is reasonable to say that earnings surprises
capture, at best, only part of the information released to investors around the
announcement period. A plausible reason for the common practice of using only
quarterly earnings in empirical studies is the difficulty in quantifying the information
6
other than the earnings number. In this paper, we construct measures of such information
that enable us to study the effect of such information on stock price.
2.2. Methodology
Analysts play a key role as information intermediaries in well-functioning capital
markets. They are paid to collect and analyze information. Theory suggests that earnings
announcement stimulates financial analysts to conduct further research and generate new
information (Kim and Verrecchia 1994, 1997). Ivkovic and Jegadeesh (2004) and Stickel
(1989) document that analysts revise their forecasts immediately after earnings
announcements. Barron, Byard, and Kim (2002) show that the idiosyncratic information
contained in individual analysts’ forecasts increases immediately after earnings
announcements. Ivkovic and Jegadeesh (2004) study the information content of forecast
revisions of next quarter’s earnings and show that forecast revisions immediately
following earnings announcement are mainly based on analysts’ interpretation of newly
released information. These theoretical and empirical findings suggest that analysts
update their forecasts shortly after earnings announcement and their new forecasts should
reflect earnings relevant new information released during announcement period.
Therefore, we can calculate the difference in analysts’ forecasts between the before and
after earnings announcement period and use it as a quantitative measure of the new
information implied in the analysts’ revisions of future earnings.
We examine the timing and frequency of analysts’ forecasts after each quarterly
earnings announcement in the I/B/E/S Detailed Analyst Forecasts Database (March 2004
7
version) from January 1988 through December 2003. Our sample of quarterly earnings
announcement satisfies the following requirements. Let day T stand for the
announcement day of a firm’s earnings in a fiscal quarter, labeled as Quarter -1. We
require that 1) the firm has December-end fiscal year, 2) Quarter -1 is not the last quarter
of a fiscal year, and 3) there are at least 3 forecasts within the first two weeks after day T.
For each announcement in our sample, we study analysts’ forecasts in five time periods:
A) between the announcement day for Quarter -1’s earnings and the subsequent
announcement day for Quarter 0’s earnings, exclusive of the two announcement days, B)
in the first two-week period after day T, not including day T, C) in the second two-week
period after day T, D) in the third two-week period after day T, and E) in the fourth twoweek period after day T. Analysts often make forecasts for earnings of different future
horizons. We examine analysts’ forecasts for seven fiscal periods after Quarter -1, which
include the next two fiscal quarters (i.e. Quarter 0 and Quarter 1), current and the next
four fiscal years (i.e. Fiscal Year 0, 1, 2, 3 and 4). In Table 1, we report the mean
(median) number of analyst forecasts classified by the time period in which the forecast
was made and the fiscal period whose earnings was forecasted. The mean and median
was calculated over all announcements in our sample. In addition, Table 1 reports the
mean (median) ratio of the number of forecasts in time intervals B, C, D, and E,
respectively, to the number in interval A.
Insert Table 1 here
A few patterns stand out in Table 1. First, firms in our sample receive high analyst
coverage. Between the two announcement days, there are on average more than 10
8
forecasts for earnings of the four fiscal periods: Quarter 0, Quarter 1, Fiscal Year 0, and
Fiscal Year 1. Second, analysts issue forecasts almost immediately after earnings
announcement. On average, about half of the forecasts are made within two weeks after
announcement. The mean number of forecasts issued within the first two-week period
after day T is greater than 5. In contrast, the mean number is less than 2 in any other twoweek period. Third, analysts seldom issue long-term forecasts. The number of
observations reduced sharply from 36,203 for one year to 1,049 for two years, and to only
45 for four years ahead. Analysts issue earnings forecasts for Quarter 0, Quarter 1, Year
0 and Year 1 with greater regularity.
The findings that analysts quickly and frequently issue earnings forecasts for earnings
of the next two quarters and the next two years are particularly useful for our study. This
gives us assurance that analysts revise these forecasts in response to the new information
released in the announcement period. Now let us consider two consecutive quarterly
earnings announcement days of the same firm, day S and day T, when earnings of
Quarter -1 and Quarter 0 were announced. The pattern in Table 1 shows that analysts
issue forecasts of Quarter 1’s earnings after day S and update their forecasts of Quarter
1’s earnings again after day T. Therefore, we can calculate the difference in the
consensus forecasts of Quarter 1’s earnings between before and after day T as a measure
of the new information relevant to Quarter 1’s earnings. Similarly, because analysts issue
forecasts of Fiscal Year 1’s earnings after day S and update their forecasts after day T, we
can also calculate the change in analysts’ forecasts of Year 1’s earnings after day T as a
measure of the new information relevant to Year 1’s earnings. Further details on the
calculation are given in Section 3.
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As a result, we have three variables that reflect the information released around the
announcement day T: the surprise component of Quarter 0’s earnings, the change in
analysts’ forecasts of next quarter’s earnings and the change in analysts’ forecasts of next
year’s earnings. Following Joy, Litzenberger, and McEnally (1977), Latané and Jones
(1977, 1979) and the literature thereafter, we standardize the three variables by the
standard deviation of analyst forecasts before day T and obtain the standardized
unexpected earnings (SUE), the standardized quarterly earnings revision (SQR), and the
standardized annual earnings revision (SAR). Panel A of Table 2 reports descriptive
statistics of the three standardized variables for our sample. (The sample is described in
details in Section 3.1.) An interesting observation is that the mean and median of SUE
are positive while the mean and median of both SAR and SQR are negative. This
suggests that analysts tend to issue optimistic forecasts initially and, later, adjust their
forecasts downward despite the positive earnings surprise that appears subsequently.e
Insert Table 2 here
We expect that SQR and SAR contain additional information beyond what is in SUE.
But SQR and SAR must also reflect information in SUE because analysts know SUE
before revising their forecasts and if forecasts revision has a learning component. In
other words, it is reasonable to expect that earning surprise, SUE, be an input, together
with other new information in this period, to analysts making earnings revision SQR and
SAR. Empirically, we expect to observe high correlation coefficients between SQR
(SAR) with SUE. Panel B of Table 2 shows that the Spearman rank correlation
The evidence is also consistent with analyst behavior documented in O’Brien (1998), Bartov, Givoly and
Hayn (2002), Matsumoto (2002), and others.
e
10
coefficient between SUE and SQR is 0.542 and that between SUE and SAR is 0.579. To
extract the incremental information in SQR and SAR beyond SUE, we take the following
two-step statistical approach. We estimate a regression model, as in equation (1), of SQR
against SUE and use the residual term to represent the unique information in SQR beyond
SUE. Because analysts are expected to utilize both SUE and SQR in forming revised
expectation as measured by SAR, we also need to remove the information content of both
SUE and SQR from SAR. Thus, we estimate a second regression model, as in equation
(2), of SAR against both SUE and IQR, and use the residual term to represent the unique
information in SAR beyond SQR and SUE. We identify the residual term in equation (1)
as the incremental quarter-earnings revision (IQR) variable and the residual term in
equation (2) as the incremental annual-earnings revision (IAR) variable.
SQRi  a  b * SUEi   i
(1)
SARi  c  d * SUEi  e * IQRi   i
(2)
The result reported in Panel C of Table 2 shows that this procedure works well. The
three information variables are no longer correlated. The Spearman rank correlation
coefficients are reduced to an insignificant -0.025 between SUE and IQR, and a mere
0.002 between SUE and IAR. Now we have three truly independent variables that reflect
different aspects of the information released around earnings announcement. The
variable SUE represents the difference between investors’ expectation and the actual
firm’s earnings for the past quarter, while IQR and IAR contain incremental forwardlooking information about the firm’s earnings in next quarter and next fiscal year.
11
2.3. Literature review
------- Change the sub-section of Hypotheses to a section about Literature review -----
In this paper, we study how stock market impounds such forward-looking information in
price before, around, and after earnings announcements. More specifically, we examine
four aspects of this process: 1) investors’ knowledge of the information before earnings
announcements, 2) price response to the information around earnings announcements, 3)
difference between negative and positive information, 4) determinants of the crosssectional difference in the price formation process.
In the following, we provide a review of extant literature related to these issues. Most
previous studies focus on price response to earnings surprises. In the review, we focus on
the underlying theories or empirical facts that have implications to the relationship
between price formation and forward-looking information.
1) Investors’ knowledge of the information before earnings announcements
Relevant papers:
Atiase, Rowland Kwame, 1985. Predisclosure information, firm capitalization, and
security price behavior around earnings announcements. Journal of Accounting Research
23, 21-36.
12
Dempsey, Stephen J. "Predisclosure Information Search Incentives, Analyst Following,
And Earnings Announcement Price Response," Accounting Review, 1989, v64(4), 748757.
And possibly others.
2) Price response to the information around earnings announcements
Most previous studies focus on price response to earnings surprises. Kothari (2001)
reviews this area. Few papers look beyond earnings surprises.
The Hoskin, Hughes, and Ricks (1986) paper is an exception. They study additional
disclosures released in the Dow Jones News Retrieval Service concurrently with the
announcement of annual earnings and report statistically significant excess returns
associated with disclosed earnings components, dividend increases, prospective officer
comments, and prospective operating data.
Cornell and Landsman (1989) also study price response to revision in one-month-ahead
and one-year-ahead analyst forecasts. We need emphasize our new contributions.
Relevant papers:
Hoskin, Robert E., John S. Hughes and William E. Ricks, 1986. Evidence on the
incremental information content of additional firm disclosures made concurrently with
earnings. Journal of Accounting Research 24, 1-32.
Cornell, Bradford and Wayne R. Landsman. "Security Price Response To Quarterly
Earnings Announcements And Analysts' Forecast," Accounting Review, 1989, v64(4),
680-692.
13
3) Difference between negative and positive information
3.A. Difference in management disclosure of good versus bad news:
Adverse selection models of discretionary disclosure, such as Verrecchia (1983, 1990),
predict that only firms with good news will disclose. Studies such as Lang and
Lundholm (1993) and Botosan (1997) find a positive relation between past firm
performance and current disclosure, and interpret this as consistent with adverse selection
model predictions.
McNichols (1988) argues that opportunistic managers have incentives to leak upcoming
good news before earnings is disclosed, and earnings announcements will thus reveal
relatively more ‘bad earnings news’ to the market. McNichols predicts and finds that
returns are negatively skewed at earnings announcements relative to the rest of the year.
Skinner (1994) argues that managers release bad news early before earnings are
announced to reduce their investor litigation exposure. Skinner (1994) finds that large
negative earnings surprises are preempted by voluntary corporate disclosures more often
than other earnings releases.
Relevant papers:
Verrecchia, R. (1983). “Discretionary Disclosure.” Journal of Accounting and Economics
5, 179–194.
14
Verrecchia, R. (1990). “Information Quality and Discretionary Disclosure.” Journal of
Accounting and Economics 12, 365–380.
Lang, M., and R. Lundholm. (1993). “Cross-Sectional Determinants of Analyst Ratings
of Corporate Disclosures.” Journal of Accounting Research 31, 246–271.
Botosan, C. (1997). “Disclosure Level and the Cost of Equity.” Accounting Review 72,
23–349.
McNichols, Maureen. "A Comparison Of The Skewness Of Stock Return Distributions
At Earnings And Non-Earnings Announcement Dates," Journal of Accounting and
Economics, 1988, v10(3), 239-273.
Skinner, Douglas J. "Why Firms Voluntarily Disclose Bad News," Journal of Accounting
Research, 1994, v32(1), 38-60.
3.B. Difference in investor response to good versus bad news:
I read the following paper by Conrad, Cornell and Landsman (2002, JF)
Conrad, Jennifer, Bradford Cornell and Wayne R. Landsman. "When Is Bad News Really
Bad News?," Journal of Finance, 2002, v57(6,Dec), 2507-2532.
They argue that the model in Barberis, Shleifer and Vishny (1998) implies that following
a string of positive shocks observed, say, glamour stocks, the investor in this model
expects another positive shock, that is, he expects the earnings to trend. If good news is
announced, the market response is relatively small since the positive shock was
15
anticipated. A negative shock, on the other hand, generates a large negative return, since
it is more of a surprise.
We may borrow the same argument to explain the pattern in the coefficients of negative
and positive SQR and SAR in (-22, -3) and (-2, 10).
Skinner and Sloan (2002) also report asymmetric response to good versus bad news for
value and glamour stocks.
Skinner, Douglas J. and Richard G. Sloan. "Earnings Surprises, Growth Expectations,
And Stock Returns Or Don't Let An Earnings Torpedo Sink Your Portfolio," Review of
Accounting Studies, 2002, v7(2-3,Jun-Sep), 289-312.
4) Determinants of the cross-sectional difference in the price formation process
We expect that the cross-sectional difference in the price formation process is related to a
firm’s information environment and stock trading activity. Numerous papers have
theoretically or empirically shown that information environment and trading activity
influence price formation. We examine these theories or rationales for incremental
information about future earnings.
2.4. Hypotheses
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Having three independent information variables allows us to decompose stock price
changes around the earnings announcements period into component response to earnings
surprise, to incremental information about next quarter’s earnings, and to incremental
information about next year’s earnings. More specifically, we consider the following
regression model to explain abnormal return in an event window around announcement
day
ARi    1 * SUEi   2 * IQRi   3 * IARi   i .
(3)
The three coefficients,  1 ,  2 , and  3 , represent how market responds to SUE, IQR and
IAR, respectively. Although many studies have examined  1 closely, we are not aware
of any study that has estimated  2 or  3 . In the following, we will study several
hypotheses that relate to how market respond to forward-looking information through  2
and  3 .
The first question we ask is whether stock market recognizes and responds to
forward-looking information released during the announcement period. Theory says that
stock market is a price system that aggregates information (Grossman and Stiglitz, 1976).
Many event studies have shown that stock price reacts to earnings surprises. Although
the system may not be informationally efficient all the time (Grossman and Stiglitz,
1980), we expect it functions reasonably well when the spot light is turned on and
focused on the announcing firms. Therefore, we posit the following hypothesis.
Hypothesis 1. (Price response to incremental information)
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Stock price responds to forward-looking information disclosed concurrently with
earnings announcement. That is, we expect the coefficients  2 and  3 to be
positive and significant.
This hypothesis is essentially a joint hypothesis of market efficiency and how reliably
the variables, SQR and SAR, quantify the disclosed forward-looking information. If
SQR and SQR fail to capture such information, even if the market is efficient, we will not
be able to observe significant coefficients.
Our next two hypotheses concern the magnitude and speed of price response. Theory
suggests that market reactions to earnings announcements are inversely related to the
amount of information about earnings already impounded in share price before the
announcement (Holthausen and Verrecchia, 1988). How much of the new information is
already reflected in share price depends on investors’ incentives to collect information
and the ease of that information being reflected in share price through trading. Prior
empirical studies have used variables such as firm size and analyst following as proxies
for the amount of pre-announcement information available about a firm, because there is
greater demand for information production in large or more widely followed firms.
These studies have found a negative relation between the magnitude of price response
around earnings announcements and these proxies (e.g., Atiase, 1985, Lobo and
Mahmoud, 1989, Dempsey, 1989, and Shores, 1990), which is attributed to earlier
revelation of information prior to announcement for the more intensely researched firms.
Although previous empirical studies are concerned with price response to earnings
surprises, we expect the same arguments can be applied to incremental information on
future earnings. Therefore, we formulate our second hypothesis as
18
Hypothesis 2. (The magnitude of price response)
The magnitude of price response to incremental information depends on how
much information has already been incorporated in the pre-announcement price.
Firms with more informative stock prices have lower price response.
We construct our measures of forward-looking information using analysts’ forecast
revisions appearing within ten trading days after the announcement day, which provides
ample time for analysts to interpret and incorporate the new information and not long
enough to collect new information. To facilitate the analysis of the speed of price
response to new information not previously incorporated, we consider two event
windows: window (-2, 1) and window (2, 10) relative to the announcement day (day 0).
More informationally efficient firms, such as larger ones and those with wider analyst
following or lower costs to trade, are expected to respond to new information sooner.
Since these firms adjust to information faster, greater proportion of their total response to
new information is expected to occur in the first event window. Thus, by estimating the
price to information response coefficients in the regression model (3) separately for each
event window, the speed the firms adjust to the three earnings components can be
compared. For instance, if price responds completely in the first event window, the
coefficients in the second window regression should be insignificant. We expect the
speed to vary with a firm’s information environment and stock trading activity. Good
information environment facilitates dissemination of information among investors, and
liquid market with low transaction costs allows transactions to reflect new information
19
with little lag. Both are necessary to facilitate speedy price response. Therefore, our
third hypothesis is
Hypothesis 3. (The speed of price response)
The speed of price response to incremental information depends on how fast the
investors become aware of such information and how quickly trades take place
between them. Firms that are widely followed and whose stocks are more
actively traded are predicted to have faster price response.
Our last hypothesis is related to theoretical work on stock price bubble. Theory of
speculative price bubble hypothesizes that heterogeneous beliefs among investors result
in a speculative component in asset prices (Harrison and Kreps, 1978, and Scheinkman
and Xiong, 2003). Speculative investors expect to have short investment horizon and are
more sensitive to short-term information than average investors. The coefficients of SQR
and SAR in regression model (3) measure stock price response to information related to
short-term and medium-term future earnings, thus, offer a means to measure time
variations in the relative importance marginal traders assign to short versus medium term
information. Since our sample period covers the quiet period after 1987 market crash, the
bubble formation period in mid 1990s, the bubble period and a few post-bubble years, we
have a unique opportunity to study whether price response to short-term information did
increase in the bubble period. We formulate our last hypothesis as,
Hypothesis 4. (Price response during bubble period)
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During a stock price bubble, marginal investors are the speculative investors who
put greater weight on short-term earnings. Thus stock price responds more
strongly to short-term information in the bubble period than in any other period.
3. Data and variable construction
3.1. Data
Our sample consists of all eligible quarterly earnings announcement days from the
I/B/E/S Detailed Analyst Forecasts Database (March 2004 version) from January 1988
through December 2003. Each eligible announcement day corresponds to a unique firmquarter pair and satisfies the following requirements.
First, the firm’s fiscal year must end in December and the quarter is the first, second
or third quarter of a fiscal year. We study only interim quarters because SEC requires
quarterly reports only for interim quarters. Figure 1 presents graphically the important
calendar days involved. Day T represents an eligible announcement day when Quarter
0’s earnings are announced. We require Quarter 0 is the first, second or third quarter of
current fiscal year (i.e. Year 0).
Insert Figure 1 here
Second, the I/B/E/S database reports both day T and the previous earnings
announcement day before day T. In Figure 1, we use day S to represent the previous
announcement day on which the earnings of previous quarter (i.e. Quarter -1) or year was
21
announced.f We learn from Table 1 that analysts revise their forecasts immediately after
an earnings announcement. We require the knowledge of day S so that we don’t miss
any forecast between day S and day T.
Third, we require two forecast windows to measure revisions in analysts’ forecasts in
response to earnings announcement. The pre-announcement forecast window is between
day S+1 and day T-1 and the post-announcement one between day T+1 and T+14. Both
windows are inclusive of the beginning and end days. We require that, within the first
window, the I/B/E/S database have at least three forecasts for Quarter 0’s earnings,
Quarter 1’s earnings and Year 1’s earnings, and that, within the second window, the
I/B/E/S database have at least three forecasts for Quarter 1’s earnings and Year 1’s
earnings.g
Fourth, we match firms between the I/B/E/S database and the CRSP database by
CUSIP and include in our sample only firms that list ordinary shares (CRSP share code:
10 and 11) on the New York Stock Exchange and have valid daily return and daily
trading volume from 210 trading days before day T through 10 trading days after day T.
In total, our sample consists of 10,979 firm-quarter observations between January
1988 and December 2003 inclusively. For each firm-quarter pair, we calculate the
variables as described below.
3.2. Variable construction
If Quarter 0 is the first quarter in current fiscal year, day S is the announcement day of last year’s annual
earnings. This is because firms are not required to announce fourth quarter’s earnings separately from
annual earnings.
g
If an analyst issues more than one forecast of the same earnings in the same window, we keep only the
latest forecast.
f
22
First, we compute the three standardized revision variables SUE, SQR and SAR from
analyst forecasts in the I/B/E/S database. To obtain SUE, we calculate the mean and
standard deviation for the forecasts of Quarter 0’s earnings in the pre-announcement
forecast window, subtract the mean forecast from the actual earnings announced on day
T, and then divide the difference by the standard deviation. To obtain SQR, we calculate
the pre-announcement mean and standard deviation for the forecasts of Quarter 1’s
earnings in the pre-announcement forecast window and also the post-announcement mean
for the forecasts of Quarter 1’s earnings in the post-announcement forecast window,
subtract the pre-announcement mean from the post-announcement mean, and then divide
the difference by the pre-announcement standard deviation. Similarly, to obtain SAR, we
calculate the pre-announcement mean and standard deviation for the forecasts of Year 1’s
earnings in the pre-announcement forecast window and also the post-announcement mean
for the forecasts of Year 1’s earnings in the post-announcement forecast window, subtract
the pre-announcement mean from the post-announcement mean, and then divide the
difference by the pre-announcement standard deviation. We follow the methodology in
Section 2.2 to convert SUE, SQR and SQR to the three orthogonal information variables
SUE, IQR and IAR.
We measure abnormal returns in three event windows. Let the announcement day T
be day 0. We consider a long event window from day -2 to day 10 and two sub-windows
from day -2 to day 1 and from day 2 to day 10 respectively. The event windows include
both the beginning and the end day. We calculate abnormal return as the difference
between a firm’s buy-and-hold return over an event window and the contemporaneous
buy-and-hold return on the CRSP size-decile portfolio that matches the firm.
23
To test our hypotheses on the magnitude and speed of price response, we construct
four variables that proxy for a firm’s information environment and stock trading
activities: market capitalization, analyst coverage, level of transaction costs, and annual
share turnover. For each firm-quarter pair in our sample, we measure the quarter-end
market capitalization by the product of the closing price and outstanding shares on the
last trading day of the fiscal quarter. We measure the number of analysts following the
firm by the number of analysts that issued forecasts of Quarter 0 earnings in the preannouncement forecast window. Bhushan (1989) shows that the number of analysts
following a firm is positively related to the number of institutions holding its shares.
Since institutional investors have resources to pay for information and the capability to
act on them, firms with higher analyst coverage expect to have more informative stock
price. We follow Lesmond, Ogden, and Trzcinka (1999), who find that the number of
days with zero daily returns gives a good estimate of the level of transaction costs, and
record the number of days with zero daily returns between day -210 and day -10. We
measure the annual share turnover by 250 (trading days) times the average of daily share
turnover ratio between day -210 and day -10, where the daily share turnover ratio is the
ratio of daily trading volume over the total number of outstanding shares. Table 2 reports
summary statistics for these variables and their correlation coefficients.
4. Empirical results
4.1. Price response to information
Panel A of Table 3 reports the estimation results of the regression model (3), that is,
ARi    1 * SUEi   2 * IQRi   3 * IARi   i
24
where AR is abnormal return in an event window, SUE, IQR and IAR are the three
orthogonal information variables. We study abnormal returns in three event windows:
window (-2, 10) and its two sub-windows (-2, 1) and (2, 10), where day 0 is the earnings
announcement day. In Panel A, we find the coefficients of the three information
variables are positive and significant in all three event window regressions. It confirms
the expected significant relationship between abnormal returns and earning surprise,
which has been extensively documented in previous studies. Furthermore, Panel A
shows that abnormal returns are significantly and positively related to IQR and IAR.
Since IQR and IAR by construction contain incremental information beyond SUE, the
evidence supports our Hypothesis 1 that investors recognize and respond to new
information that firms disclose in addition to quarterly earnings.
Insert Table 3 here
We also observe in Panel A that the magnitude of the coefficients of all three
information variables is much greater in the earlier event window (-2, 1) than in the later
event window (2, 10). It confirms that market responds to information very quickly.
Market price adjusts to new information disclosed in this period even before analysts
incorporate such information in their forecasts released in the window (2, 10). Taking the
coefficients of SUE, IQR and IAR in the regression for the whole event window (-2, 10)
as the total response to one unit of information, we can measure the speed of price
response by forming a ratio of the coefficients of SUE, IQR and IAR for window (-2, 1)
to their respective coefficients over the window (-2, 10). These are plotted in Figure 2b.
More than 80% of price response occurs in window (-2, 1). The proportion is about the
25
same for all three information variables. Although these variables are independent of
each other, they show similar adjustment paths. In addition, the adjusted R-square in
Panel A of Table 3 provides further support of rapid price response.
Insert Figure 2 here
To gain insights on the effect the incremental new information has on the stock price
adjustment during announcement period, we estimate a second regression model of
abnormal returns without the information variables for future earnings, IQR and IAR, i.e.,
ARi    1 * SUEi   i .
(4)
Equation (4) corresponds to the approach used in previous studies where earnings
surprise is the sole source of information to drive abnormal returns.
Panel B of Table 3
reports the estimation results. The estimates of both  and  1 remain the same as in
Panel A because IQR and IAR by construction are orthogonal to SUE and have a zero
mean. However, the adjusted R-square is reduced to half the value reported for equation
(3). For example, in the regression of abnormal returns in window (-2, 1), the R-square is
only 6.2% in Panel B compared to that of 12.7% in Panel A. The reduction could be
attributed solely to the exclusion of incremental information in IQR and IAR. In other
words, new information on future earnings beyond that contained in earning surprise
could account for as much of the price response in this period as the earning surprise.
The effect of the two previously omitted information variables is significant enough not
to be ignored.
4.2. The magnitude and speed of price response
26
Given the strong evidence that stock price responds to incremental information
around earnings announcements, we take one step further to investigate the determinants
of the magnitude and speed of price response. We hypothesize in Hypotheses 2 and 3
that the magnitude and speed of price response are related to a firm’ information
environment and the ease of effecting price change via stock trading activities. We
consider four potentially influential factors; these are: market capitalization, analyst
coverage, level of transaction costs, and annual share turnover. If a factor has significant
influence on price response, we should observe significant difference in price responses
between firms that are grouped to differ in that factor. We report the empirical results for
each factor in the following.
Effect of market capitalization
We study the effect of market capitalization, as a measure of firm size, by comparing
the difference between large and small size stocks. Each quarter, we rank firms in our
sample by their market capitalization and classify the top one-third as large size stocks
and the bottom one-third as small size stocks.h We estimate the following model for
abnormal returns in the three event windows, for the regression:
ARi
 DLarge * ( L  1L * SUE   2L * IQR   3L * IAR)
,
 DSmall * ( S  1S * SUE   2S * IQR   3S * IAR)   i
where DLarge is a dummy variable equal to 1 for large size stocks and 0 otherwise,
DSmall is a dummy variable equal to 1 for small size stocks and 0 otherwise.
h
The stocks in our sample are large ones in the population of all listed firms because of the requirement
that there are at least three analyst forecasts in each forecast window (See Section 3 and Table 1).
27
Instead of estimating two separate regression models, one for large size stocks and the
other for small size stocks, we pool large and small stocks in one regression and
differentiate them by dummy variables. This approach has two advantages. First, it
yields two sets of coefficient estimates, one for large stocks and the other for small
stocks, as if we run the two separate regressions. Second, it allows us to directly test, via
the Wald statistic, whether the two groups have significantly different coefficients.
Table 4 reports the estimation results. We observe the same pattern as in Table 3: the
coefficients of the three information variables are all positive, significant, and much
larger in window (-2, 1) than in window (2, 10). The results support Hypothesis 2 that
market response around earnings announcement is a function of information environment
and trading activities. Specifically, large size stocks do behave differently from small
ones in several regards. First, consistent with greater price adjustment prior to earnings
announcement, large stocks have lower coefficients than low cost stocks for all three
information variables in window (-2, 10) (See Figure 3a). The Wald statistics in Table 4
show that the difference in the coefficient of SUE between large and small firms is
statistically significant and so is the difference for IQR.
Insert Table 4 here
Insert Figure 3 here
Second, large stocks respond much faster than small stocks.i Figure 4a plots the ratio
of the coefficients in window (-2, 1) to the coefficients in window (-2, 10). Almost 100%
i
This is consistent with previous studies of how firm size affects market response to earnings surprises,
such as Atiase (1985), Lev and Penman (1990), and others.
28
of total price response occurs in window (-2, 1) for large size stocks, in contrast to only
about 70% for small stocks. Because prices of large stocks have reflected almost all the
information in window (-2, 1), large stocks have small and insignificant coefficients of
the information variables in window (2, 10). In contrast, small stocks have significantly
positive coefficients in window (2, 10), and the difference between the two groups is
statistically significant according to the Wald test.
In summary, the results support both Hypotheses 2 and 3. Large size stocks have
smaller total price response to all three sources of information released around earnings
announcement because more information has been incorporated in the price prior to
announcement. Large size stocks respond much faster than small ones because large
stocks tend to be more actively followed and traded.
Effect of analyst coverage
We now study the effect of analyst coverage. Each quarter, we rank firms in our sample
by analyst coverage and classify the top one third as high coverage stocks and the bottom
one third as low coverage stocks. We estimate the following model for abnormal returns
in three event windows:
ARi
 DHigh * ( H  1H * SUE   2H * IQR   3H * IAR)
,
 DLow * ( L  1L * SUE   2L * IQR   3L * IAR)   i
where DHigh is a dummy variable equal to 1 for high coverage firms and 0 otherwise,
DLow is a dummy variable equal to 1 for low coverage firms and 0 otherwise.
Table 5 reports the estimation results. We observe several differences between high
and low analyst coverage stocks. First, high coverage stocks have significantly smaller
total response in window (-2, 10) than low coverage stocks (See Figure 3b). This is
29
consistent with the hypothesis that price of high coverage stocks contain more
information prior to earnings announcement than that of low coverage stocks.
Insert Table 5 here
Second, high coverage stocks register faster response to SUE and IQR in window (-2,
1) than low coverage stocks, but not to the longer term IAR. The proportional price
response plotted in Figure 4b shows that high coverage stocks respond to SUE and IQR
almost completely in window (-2, 1) while low coverage stocks experience only about
75% of total response to SUE and IQR. In contrast, both high and low coverage stocks
respond equally slowly to IAR in window (-2, 1). It seems to suggest that analyst
coverage does not affect trading decisions of those investors who act on long-term
information, probably because loner term traders are by definition trade less often and are
less likely to be the marginal investors.
Third, the coefficients of all three information variables are statistically significant in
window (2, 10) for low coverage stocks. It confirms that low coverage stocks respond
slowly and continue to adjust to new information even in window (2, 10).
Effect of transaction costs
We now study the effect of transaction costs. Each quarter, we rank firms in our
sample by the number of zero daily returns and classify the top one third as high
transaction cost stocks and the bottom one third as low transaction cost stocks. We
estimate the following model for abnormal returns in three event windows:
ARi
 DHigh * ( H  1H * SUE   2H * IQR   3H * IAR)
,
 DLow * ( L  1L * SUE   2L * IQR   3L * IAR)   i
30
where DHigh is a dummy variable equal to 1 for high transaction costs firms and 0
otherwise, DLow is a dummy variable equal to 1 for low transaction costs firms and 0
otherwise.
Table 6 reports the estimation results. We observe several differences between high
and low transaction cost stocks. First, low cost stocks have smaller price response in
window (-2, 10) than high cost stocks (See Figure 3c). It confirms that low transaction
cost facilitates price discovery and prices of low cost stocks are more informative prior to
announcement. Second, low cost stocks respond to information faster than their high cost
peers. The proportional price response in window (-2, 1) plotted in Figure 4c shows that
more than 90% of total price response occurs in window (-2, 1) for low cost stocks
whereas the proportion is about 70% for high cost stocks. Again, it suggests more
informed trading among low cost stocks. Third, because high cost stocks experience
incomplete price adjustment in window (-2, 1), their abnormal returns in window (2, 10)
are significantly positively related to the three information variables. In contrast, low
cost stocks have small and insignificant coefficients for the information variables in
window (2, 10).
Insert Table 6 here
Effect of share turnover
We now examine the effect of share turnover. Each quarter, we rank firms in our sample
by share turnover and classify the top one third as high turnover stocks and the bottom
one third as low turnover stocks. We estimate the following model for abnormal returns
in three event windows,
31
ARi
 DHigh * ( H  1H * SUE   2H * IQR   3H * IAR)
,
 DLow * ( L  1L * SUE   2L * IQR   3L * IAR)   i
where DHigh is a dummy variable equal to 1 for high share turnover firms and 0
otherwise, DLow is a dummy variable equal to 1 for low share turnover firms and 0
otherwise.
Table 7 reports the estimation results. It is a little surprising to observe no difference
in total price response in window (-2, 10) between high and low turnover stocks. It
seems to suggest that, although high turnover stocks in general experience high trading
activity, their prices need not be more informative prior to announcement, suggesting that
as long as the informed investors trade with respect to new information, it does not matter
who else or how many other investors trade. However, high share turnover does improve
the speed of market price discovery with respect to the new information revealed in this
period. The proportional price response plotted in Figure 4d show that high turnover
stocks respond more completely in window (-2, 1). Also, because of incomplete response
in window (-2, 1), low turnover stocks continue to drift into window (2, 10).
Insert Table 7 here
4.3. Price response under different market regimes
Motivated by the theory of speculative bubble, we posit in Hypothesis 4 that during
the bubble period, marginal investors are more likely to be speculative investors who
have short holding period and are therefore more focused on short-term earnings. To test
this hypothesis, we divide our sample period into four sub periods: the “normal” period
from 1988 to 1993, the “pre-bubble” period from 1994 to 1998, the “bubble” period from
32
1999 to June 2000, and the “post-bubble” period from July 2000 to 2003. Table 8 reports
the estimation results of the abnormal return regression for three event windows and the
four sub periods. Across all four periods, the coefficients of all three information
variables are significantly positive in both window (-2, 10) and (-2, 1). This provides
robust support to our previous results that stock price responds to the incremental
information in our variables IQR and IAR even under different market regimes.
Insert Table 8 here
In addition, Figure 5a shows the total price response in window (-2, 10) to long-term
information in IAR stays almost the same in the four periods. It suggests a potentially
important empirical finding that there exist a group of investors whose trading decision is
based solely on long-term information and are not influenced by changing market
conditions. In contrast, there is considerable variation in total response to SUE and IQR
across the four periods. Particularly noticeable is the spike in the coefficient of IQR
during the bubble period. This is consistent with our hypothesis that the stock market
becomes highly sensitive to short-term earnings during the bubble.
Insert Figure 5 here
Furthermore, we observe in Figure 5c that stock price responds to information at
greater time lags, i.e., slower, in the pre-bubble and bubble period than in normal and
post-bubble period. This is particularly true for the response to SUE and IQR. Of
interest are the generally long lags for all three types of information in the pre-bubble
33
period. Since the period is the precursor to the bubble period, the observation of slower
response to information in the pre-bubble buildup may add useful facts about the
formation of bubbles.
5. Summary and discussions
The earnings announcement period is information rich in that firms frequently use
press release and conference calls to disclose more information than just quarterly
earnings. Because of the difficulty in quantifying the incremental information beyond
earnings surprise, previous studies tend to ignore such information in their research
design. In this paper, we construct quantitative measures of incremental information and
study stock price response to such information.
We observe that analysts update their forecasts of both next quarter’s and next year’s
earnings immediately following earnings announcements. Since analysts are experts in
collecting and processing information, the revision in their consensus forecast should
reflect the information released during announcement period.
We construct two
quantitative measures of incremental information based on the changes in analyst
forecasts of next quarter and next year’s earnings before and after earnings
announcement. Our empirical evidence shows that a regression model with these two
variables in addition to earnings surprise has twice as much explanatory power for
announcement-period abnormal returns as the model with only earnings surprise. In
addition, we find that the magnitude and speed of price response to incremental
information are related to firms’ information environment and stock trading activities.
We observe smaller price response during announcement period for firms of larger size,
34
with higher analyst coverage and lower transaction costs. This is consistent with the
hypothesis that these firms have more informative stock prices prior to announcement.
We also observe that the speed of price response is faster for firms of larger size, with
higher analyst coverage, lower transaction costs and higher share turnover.
35
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39
Motivation:
1. Liu and Thomas (JAR, 2000) (probably, and others) have shown that stock return
depends on forecast revisions of current and future earnings.
2. Firms disclose forward-looking information around earnings announcement, via
press releases and conference calls.
3. Most previous studies of price adjustment around earnings announcement focus
on only SUE.
4. We study how market incorporates information of future earnings into price
during the period around announcement.
Research design:
We create measures of forward-looking information that is available around earnings
announcement. Such forward-looking information includes both disclosure by firms and
information discovery by analysts.
We study three event windows: pre, around and post announcement. More specifically,
they are (-33, -3), (-2, 10), and (11, 30) relative to day 0 (the announcement day). The
pre announcement window allows us to study market discovery of information and price
movement. The around announcement window is to study price response and speed of
adjustment. The post announcement window is to study the completeness of price
adjustment.
We also split the window (-2, 10) into (-2, 1) and (2, 10) to study the speed of adjustment
upon announcement.
We examine the asymmetric price adjustment to negative and positive information.
Hypotheses:
We conduct our empirical analysis to address four hypotheses (or four issues):
H1: How does price adjust to forward-looking information, pre, around and post
announcement?
H2: Does price adjust differently to negative and positive information, pre, around and
post announcement?
H3: The magnitude of price adjustment around announcement depends on the demand
for information and trading opportunities.
H4: The speed of price adjustment around announcement depends on the demand for
information and trading opportunities.
We drop the old hypothesis about the bubble period.
40
I suggest we emphasize H2 because it is one place we observe significant difference in
how market reflects information in SUE and forward-looking information in IQR and
IAR.
There are two explanations for the difference in the coefficients of negative and positive
SUE, IQR and IAR in pre-announcement abnormal return regression. One, investors
respond differently to negative and positive information. Two, investors have different
foreknowledge of negative and positive information. If the first is true, we should
observe significant difference between negative and positive SUE as well. Since we
didn’t observe that, the evidence suggests investors tend to know more positive
information. It also explains the smaller reaction to positive information in (-2, 10).
Organization of empirical results:
Table 1 and Figure 1: To motivate our methodology as in previous draft.
Table 2 and Figure 2: To report descriptive statistics.
Figure 3: To illustrate event windows
Table 3: To show how return is related to information in pre-, around-, and postannouncement window. Here we don’t separate negative and positive information. It
also shows the improvement in R-square over the model with only SUE.
Table 4 and Figure 4: To show the asymmetric relationship between return and
negative/positive information. Comparison between Table 4 and Table 3 shows the
importance of separating negative and positive information. Figure 4 graphically shows
the difference in price adjustment to negative and positive information in pre-, around-,
and post-announcement window.
Tables in Appendix: Here, or maybe even earlier, we point to the Appendix, where we
report some results for scaling earnings revisions by price instead of standard deviation.
One concern here is that the regression results in Appendix for pre-announcement return
show different pattern for IAR.
I am still not sure how we deal with this part. We may just leave them out and don’t
mention it at all.
Or if we show them, how do we reconcile them? I understand scaling by price makes
coefficients easier to interpret. But I like scaling by standard deviation for at least two
reasons: (1) scaling by standard deviation is a measure of how much new information
becomes available relative to previous information set. Since information cannot be
measured with some instruments, our quantitative information variables are only proxies.
(2) the higher regression R-squared for scaling by standard deviation suggests it is a
better proxy than scaling by price. Over short period such as (-22, -3) or (-2, 10), the
accounting price-earnings relationship may behave worse than a price-earnings model
41
based on the idea that investors adjust price relative to how surprised they are. I hope
these reasons may justify our use of scaling by standard deviation for the rest analyses. If
we were to use scaling by price for all these analyses, some of these results may be
sensitive. But we still have to take our position. Which set of results should we think a
better description of what’s going on?
Table 5 and Figure 5: To report the return regression for (-2, 10) and the two subwindow (-2, 1) and (2, 10). They focus on the speed of adjustment to negative and
positive information.
Table 6, 7, 8, 9, and Figure 6, 7: They are the same as in the previous draft. We do not
separate negative and positive information. I plan to do additional analysis with separate
negative and positive information variables. I hope we can claim in the paper that the
effect of these variables is the same on negative and positive information. Therefore, for
brevity, we report only the results without separation and further results are available
upon request.
Table 10: To study the joint effect of firm-specific variables. I scale them to decile score
variables taking values between -1 and 1. Now the coefficient of SUE will be the
response to SUE for firms with value 0 for all four variables. This table shows the
correlation coefficient for these scaled variables.
Table 11, 12, 13 and 14: To show return regression with interactions between firmspecific variables and information variables. Table 11 for (-2, 10) is to show adjustment
around announcement. Table 12 for (-22, -3) is to show pre-announcement adjustment,
which allows to say market discovery of information for different firm types. Table 13
for (11, 30) is to show post-announcement return, which allows to say completeness for
different firm types. Table 14 for (2, 10) allows us to say speed of adjustment for
different firm types.
42
Table 1: Timing and frequency of analyst forecasts following earnings announcements
We report the mean (median) number of analyst forecasts within five time intervals following a quarterly earnings announcement. We obtain all
eligible quarterly earnings announcement days from the I/B/E/S Detailed Analyst Forecasts Database (March 2004 version) from
January 1988 through December 2003. Each announcement day, denoted by day T, corresponds to a firm-quarter pair and satisfies the
requirements: 1) the firm’s fiscal year ends in December, 2) the quarter is not the last quarter of a fiscal year, and 3) there are at least 3 forecasts
within the first two weeks after day T. The five time intervals include: A) between day T and the subsequent announcement day, B) within the
first two-week period after T, C) within the second two-week period after T, D) within the third two-week period after T, and E) within the fourth
two-week period after T. Suppose Quarter -1’s earnings was announced on day T. We examine analysts’ forecasts for seven fiscal periods
after Quarter -1, which include Quarters 0 and 1 (i.e. the next two fiscal quarters after Quarter -1), and Years 0, 1, 2, 3 and 4 (i.e. current fiscal
year and the next four). We also calculate, for each announcement day, the ratio of the number of forecasts in time intervals B, C, D, and E,
respectively, to the total number in the longer interval A, and report the mean (median) ratio in percentage.
Fiscal period
Obs. #
Quarter 0
30128
Quarter 1
22834
Year 0
45069
Year 1
36203
Year 2
1049
Year 3
106
Year 4
45
A
Total # of
forecasts
B
# of
forecasts
% of
Total #
12.72
(10)
10.69
(8)
15.48
(12)
14.19
(12)
7.61
(6)
7.25
(6)
6.47
(6)
5.69
(5)
5.57
(4)
6.14
(5)
5.61
(4)
3.99
(3)
3.69
(3)
3.47
(3)
54%
(50%)
61%
(60%)
49%
(44%)
48%
(44%)
63%
(60%)
60%
(57%)
60%
(60%)
Time interval
C
D
# of
% of
# of
% of
forecasts Total # forecasts Total #
1.14
(1)
0.76
(0)
1.87
(1)
1.59
(1)
0.52
(0)
0.53
(0)
0.47
(0)
43
8%
(4%)
6%
(0%)
11%
(9%)
10%
(8%)
5%
(0%)
6%
(0%)
6%
(0%)
1.12
(1)
0.81
(0)
1.59
(1)
1.45
(1)
0.59
(0)
0.73
(0)
0.51
(0)
8%
(4%)
6%
(0%)
9%
(7%)
9%
(7%)
6%
(0%)
8%
(0%)
8%
(0%)
E
# of
forecasts
% of
Total #
1.34
(1)
1.02
(0)
1.76
(1)
1.64
(1)
0.67
(0)
0.69
(0)
0.42
(0)
9%
(6%)
8%
(0%)
10%
(8%)
10%
(8%)
7%
(0%)
8%
(0%)
6%
(0%)
Table 2: Descriptive statistics
Our sample consists of 10,979 firm-quarter observations from January 1988 through December
2003. For each firm-quarter pair, we measure the quarter-end market capitalization by the
product of the closing price and outstanding shares on the last trading day of the fiscal quarter.
We measure analyst coverage by the number of analysts that issued forecasts of the announced
quarterly earnings between last and current announcement day. We record the number of days
with zero daily returns between day -210 to day -10 before the announcement day (i.e. day 0).
We measure the annual share turnover by 250 (trading days) times the average of daily share
turnover ratio between day -210 to day -10, where the daily share turnover ratio is the ratio of
daily trading volume over the total number of outstanding shares. The variables that quantify
information include the standardized unexpected earnings (SUE), the standardized quarterly
earnings revision (SQR), and the standardized annual earnings revision (SAR). The incremental
quarter-earnings revision (IQR) is the residual from regressing SQR on SUE. The incremental
annual-earnings revision (IAR) is the residual term from regressing SAR on SUE and SQR.
Panel A. Descriptive statistics
Mean Std. Dev.
0.540
2.130
SUE
-0.383
1.612
SQR
-0.128
1.157
SAR
Percentage
6.01%
5.41%
of daily zero
return
Q1
Median
Q3
Max.
-11.258
-10.027
-6.492
-0.585
-1.116
-0.735
0.335
-0.293
-0.087
1.490
0.483
0.517
13.221
6.333
4.925
0.00%
1.50%
4.50%
9.00%
40.00%
83.41%
6.90%
63.03%
92.73%
143.25%
1,151.08%
12.2
26.6
0.049
1.7
4.0
11.0
520.3
16.5
9.9
3
10
14
21
94
Annual share
116.06%
turnover
Market cap.
($Billion)
Analyst
coverage
Min.
44
Table 2 (Cont.):
Panel B. Spearman rank correlation coefficient matrix
SQR
SAR
Percentage Annual
Market
Analyst
of daily
share
cap.
coverage
zero return turnover ($Billion)
0.542
0.579
-0.098
0.046
0.061
-0.146
SUE
0.667
0.005
-0.026
0.084
-0.037
SQR
0.018
-0.027
0.062
-0.082
SAR
Percentage of daily
-0.422
-0.289
-0.132
zero return
Annual share
-0.221
0.123
turnover
0.400
Market cap. ($Bil)
Panel C. Spearman rank correlation coefficient matrix for IQR and IAR
IAR
SUE
Percentage Annual
Market
Analyst
of daily
share
cap.
coverage
zero return turnover ($Billion)
0.051
-0.025
0.004
0.006
0.007
-0.013
IQR
0.002
-0.001
-0.012
-0.002
-0.011
IAR
45
Table 3: Price response to incremental information around earnings announcements
Our sample consists of 10,979 firm-quarter observations from January 1988 through December
2003. For each firm-quarter pair, we compute the standardized unexpected earnings (SUE), the
standardized quarterly earnings revision (SQR), and the standardized annual earnings revision
(SAR). The incremental quarter-earnings revision (IQR) is the residual from regressing SQR on
SUE. The incremental annual-earnings revision (IAR) is the residual term from regressing SAR
on SUE and SQR. We consider three event windows: pre, around and post announcement. The
event windows include both the beginning and the end day. We calculate abnormal return as the
difference between a firm’s buy-and-hold return over an event window and the contemporaneous
buy-and-hold return on the CRSP size-decile portfolio that matches the firm. Panel A reports the
estimated coefficients for the regression: ARi    1 * SUE   2 * IQR   3 * IAR   i .
Panel B reports the estimated coefficients for the regression: ARi    1 * SUE   i . The
White (1990) t-statistics are reported.
Window
Pre-announcement
return in (-22, -3)
Coef.
t-stat
Around announcement
return in (-2, 10)
Coef.
t-stat
Post-announcement
return in (11, 30)
Coef.
t-stat
Panel A: Regression with incremental information

-0.0091
-10.21
0.0010
1.15
0.0017
1.89
1
0.0071
16.12
0.0093
20.75
0.0000
-0.03
2
0.0078
12.50
0.0115
16.36
-0.0010
-1.73
3
0.0150
13.34
0.0131
12.29
0.0002
0.21
R-square
0.0598
0.0963
0.0000
# of obs.
10979
10979
10979
Panel B: Regression without incremental information

-0.0091
-9.87
0.0010
1.11
0.0017
1.89
1
0.0071
15.12
0.0093
18.92
0.0000
-0.03
R-square
0.0265
0.0477
-0.0001
# of obs.
10979
10979
10979
46
Table 4: Asymmetric response to negative and positive information:
Pre, around, and post announcement
The table reports the estimated coefficients for the following regression:
ARi
   1N * NegSUE   2N * NegIQR   3N * NegIAR
,
 1P * PosSUE   2P * PosIQR   3P * PosIAR   i
where NegSUE equals to SUE when SUE is negative and 0 otherwise, PosSUE equals to SUE
when SUE is nonnegative and 0 otherwise, and the other variables are similarly defined. The
White (1990) t-statistics are reported.
Window
Pre-announcement
return in (-22, -3)
Coef.

Around announcement
return in (-2, 10)
t-stat
Post-announcement
return in (11, 30)
Coef.
t-stat
-0.0140
-9.37
0.0061
4.19
0.0011
0.79

N
1
0.0080
8.54
0.0083
7.30
0.0005
0.57

P
1
0.0065
10.62
0.0098
17.02
-0.0003
-0.60

N
2
0.0047
4.68
0.0150
13.68
-0.0026
-2.57

P
2
0.0129
10.56
0.0058
5.05
0.0011
1.01

N
3
0.0119
6.17
0.0157
8.37
0.0013
0.68

P
3
0.0182
0.0622
9.81
0.0105
0.0988
5.88
-0.0009
0.0002
-0.53
R-square
# of obs.
10979
10979
10979
1.4726
0.2250
1.0381
0.3083
0.4923
0.4829
19.8663
0.0000
24.1297
0.0000
4.5162
0.0336
4.0644
0.0438
2.9354
0.0867
0.5413
0.4619
Wald Statistic for testing the null hypothesis
1N  1P  0
 2N   2P  0
 3N   3P  0
47
Table 5: Asymmetric response to negative and positive information:
Around announcement
The table reports the estimated coefficients for the following regression:
ARi
   1N * NegSUE   2N * NegIQR   3N * NegIAR
,
 1P * PosSUE   2P * PosIQR   3P * PosIAR   i
where NegSUE equals to SUE when SUE is negative and 0 otherwise, PosSUE equals to SUE
when SUE is nonnegative and 0 otherwise, and the other variables are similarly defined. The
White (1990) t-statistics are reported.
Window
(-2, 10)
Coef.

(-2, 1)
t-stat
Coef.
(2, 10)
t-stat
Coef.
t-stat
0.0061
4.19
0.0042
4.01
0.0018
1.75

N
1
0.0083
7.30
0.0081
9.08
0.0002
0.30

P
1
0.0098
17.02
0.0072
17.45
0.0024
5.94

N
2
0.0150
13.68
0.0130
14.74
0.0018
2.50

P
2
0.0058
5.05
0.0039
4.71
0.0018
2.26

N
3
0.0157
8.37
0.0128
8.41
0.0027
1.91

P
3
5.88
0.0082
0.1332
6.29
0.0022
0.0065
1.74
R-square
0.0105
0.0988
# of obs.
10979
10979
10979
1.0381
0.3083
0.6308
0.4271
6.0329
0.0141
24.1297
0.0000
39.5152
0.0000
0.0004
0.9849
2.9354
0.0867
3.7750
0.0520
0.0519
0.8197
Wald Statistic for testing the null hypothesis
1N  1P  0
 2N   2P  0
 3N   3P  0
48
Table 6: Effect of market cap on price response
Our sample consists of 10,979 firm-quarter observations from January 1988 through December
2003. For each firm-quarter pair, we compute the standardized unexpected earnings (SUE), the
standardized quarterly earnings revision (SQR), and the standardized annual earnings revision
(SAR). The incremental quarter-earnings revision (IQR) is the residual from regressing SQR on
SUE. The incremental annual-earnings revision (IAR) is the residual term from regressing SAR
on SUE and SQR. We consider three event windows: a long window (-2, 10) and two short
windows (-2, 1) and (2, 10), around the announcement day (i.e. day 0). The event windows
include both the beginning and the end day. We calculate abnormal return as the difference
between a firm’s buy-and-hold return over an event window and the contemporaneous buy-andhold return on the CRSP size-decile portfolio that matches the firm. We measure the quarter-end
market capitalization by the product of the closing price and outstanding shares on the last trading
day of the fiscal quarter. Each quarter, we rank firms in our sample by their market capitalization
and classify the top one-third as large market cap firms and the bottom one-third as small market
cap firms. The table reports the estimated coefficients for the following regression:
ARi
 DLarge * ( L  1L * SUE   2L * IQR   3L * IAR)
,
 DSmall * ( S  1S * SUE   2S * IQR   3S * IAR)   i
where DLarge is a dummy variable equal to 1 for large market cap firms and 0 otherwise, DSmall
is a dummy variable equal to 1 for small market cap firms and 0 otherwise. The White (1990) tstatistics are reported.
Window
(-2, 10)
Coef.
L
 1L
(-2, 1)
t-stat
Coef.
(2, 10)
t-stat
Coef.
t-stat
0.0013
0.98
-0.0018
-1.83
0.0032
3.42
0.0073
10.00
0.0071
12.93
0.0001
0.24

L
2
0.0090
7.57
0.0095
10.21
-0.0004
-0.51

L
3
0.0118
6.76
0.0116
8.58
0.0000
0.01

S
-0.0006
-0.34
-0.0035
-3.03
0.0026
2.13

S
1
0.0120
15.47
0.0091
16.01
0.0029
5.34

S
2
0.0137
12.84
0.0100
12.14
0.0035
4.45

S
3
0.0157
8.54
0.0109
8.03
0.0045
3.33
# of obs.
R-square
7321
0.1143
7321
0.1440
7321
0.0125
6.3182***
13.7397***
Wald Statistic for testing the null hypothesis
1L  1S  0
19.8934***
  0
8.6082***
0.2277
12.9052***
  0
2.2848
0.1314
5.8161**
L
2
L
3
S
2
S
3
49
Table 7: Effect of analyst coverage on price response
Our sample consists of 10,979 firm-quarter observations from January 1988 through December
2003. For each firm-quarter pair, we compute the standardized unexpected earnings (SUE), the
standardized quarterly earnings revision (SQR), and the standardized annual earnings revision
(SAR). The incremental quarter-earnings revision (IQR) is the residual from regressing SQR on
SUE. The incremental annual-earnings revision (IAR) is the residual term from regressing SAR
on SUE and SQR. We consider three event windows: a long window (-2, 10) and two short
windows (-2, 1) and (2, 10), around the announcement day (i.e. day 0). The event windows
include both the beginning and the end day. We calculate abnormal return as the difference
between a firm’s buy-and-hold return over an event window and the contemporaneous buy-andhold return on the CRSP size-decile portfolio that matches the firm. We measure analyst
coverage by the number of analysts that issued forecasts of the announced quarterly earnings
between last and current announcement day. Each quarter, we rank firms in our sample by
analyst coverage and classify the top one third as high coverage firms and the bottom one third as
low coverage firms. The table reports the coefficient estimation for the regression:
ARi
 DHigh * ( H  1H * SUE   2H * IQR   3H * IAR)
,
 DLow * ( L  1L * SUE   2L * IQR   3L * IAR)   i
where DHigh is a dummy variable equal to 1 for high coverage firms and 0 otherwise, DLow is a
dummy variable equal to 1 for low coverage firms and 0 otherwise. The White (1990) t-statistics
are reported.
Window
(-2, 10)
Coef.
(-2, 1)
t-stat
(2, 10)
Coef.
t-stat
Coef.
t-stat

H
0.0026
1.74
0.0001
0.09
0.0025
2.26

H
1
0.0057
5.84
0.0054
7.35
0.0002
0.34

H
2
0.0101
6.62
0.0097
7.97
0.0002
0.24

H
3
0.0107
4.59
0.0077
4.40
0.0031
1.74

L
-0.0013
-0.91
-0.0047
-4.36
0.0033
3.11

L
1
0.0115
19.48
0.0086
20.10
0.0027
6.46

L
2
0.0134
14.94
0.0098
13.54
0.0034
5.20

L
3
0.0144
9.70
0.0114
9.48
0.0026
2.47
# of obs.
7443
7443
7443
R-square
0.1107
0.1321
0.0120
14.2583***
9.0078***
Wald Statistic for testing the null hypothesis
1H  1L  0
25.9162***
  0
3.6685*
0.0053
7.2184***
 3H   3L  0
1.7617
3.0186*
0.0456
H
2
L
2
50
Table 8: Effect of transaction costs on price response
Our sample consists of 10,979 firm-quarter observations from January 1988 through December
2003. For each firm-quarter pair, we compute the standardized unexpected earnings (SUE), the
standardized quarterly earnings revision (SQR), and the standardized annual earnings revision
(SAR). The incremental quarter-earnings revision (IQR) is the residual from regressing SQR on
SUE. The incremental annual-earnings revision (IAR) is the residual term from regressing SAR
on SUE and SQR. We consider three event windows: a long window (-2, 10) and two short
windows (-2, 1) and (2, 10), around the announcement day (i.e. day 0). The event windows
include both the beginning and the end day. We calculate abnormal return as the difference
between a firm’s buy-and-hold return over an event window and the contemporaneous buy-andhold return on the CRSP size-decile portfolio that matches the firm. For a proxy of the level of
transaction costs, we compute the number of days with zero daily returns between day -210 to
day -10 before the announcement day (i.e. day 0). Each quarter, we rank firms in our sample by
the number of zero daily returns and classify the top one third as high transaction cost firms and
the bottom one third as low transaction cost firms. The table reports the coefficient estimation for
the regression:
ARi
 DHigh * ( H  1H * SUE   2H * IQR   3H * IAR)
,
 DLow * ( L  1L * SUE   2L * IQR   3L * IAR)   i
where DHigh is a dummy variable equal to 1 for high transaction costs firms and 0 otherwise,
DLow is a dummy variable equal to 1 for low transaction costs firms and 0 otherwise. The White
(1990) t-statistics are reported.
Window
(-2, 10)
Coef.
(-2, 1)
t-stat
Coef.
(2, 10)
t-stat
Coef.
t-stat

H
0.0020
1.23
-0.0013
-1.13
0.0031
2.63

H
1
0.0108
12.65
0.0076
12.14
0.0031
5.47

H
2
0.0122
11.07
0.0090
10.03
0.0030
3.64

H
3
0.0147
8.11
0.0091
6.63
0.0053
4.04

L
0.0003
0.18
-0.0018
-1.73
0.0020
2.08

L
1
0.0079
11.05
0.0071
12.89
0.0007
1.52

L
2
0.0099
8.52
0.0095
10.12
0.0005
0.72

L
3
0.0120
6.72
0.0113
7.92
0.0006
0.43
# of obs.
7504
7504
7504
R-square
0.0940
0.1164
0.0108
Wald Statistic for testing the null hypothesis
1H  1L  0
6.4342***
0.3254
10.2469***
H
2
L
2
  0
2.0460
0.1423
4.9073**

H
3
 0
1.0944
1.2799
6.5966***
L
3
51
Table 9: Effect of share turnover on price response
Our sample consists of 10,979 firm-quarter observations from January 1988 through December
2003. For each firm-quarter pair, we compute the standardized unexpected earnings (SUE), the
standardized quarterly earnings revision (SQR), and the standardized annual earnings revision
(SAR). The incremental quarter-earnings revision (IQR) is the residual from regressing SQR on
SUE. The incremental annual-earnings revision (IAR) is the residual term from regressing SAR
on SUE and SQR. We consider three event windows: a long window (-2, 10) and two short
windows (-2, 1) and (2, 10), around the announcement day (i.e. day 0). The event windows
include both the beginning and the end day. We calculate abnormal return as the difference
between a firm’s buy-and-hold return over an event window and the contemporaneous buy-andhold return on the CRSP size-decile portfolio that matches the firm. We measure the annual
share turnover by 250 (trading days) times the average of daily share turnover ratio between day 210 to day -10, where the daily share turnover ratio is the ratio of daily trading volume over the
total number of outstanding shares. Each quarter, we rank firms in our sample by share turnover
and classify the top one third as high turnover firms and the bottom one third as low turnover
firms. The table reports the coefficient estimation for the regression:
ARi
 DHigh * ( H  1H * SUE   2H * IQR   3H * IAR)
,
 DLow * ( L  1L * SUE   2L * IQR   3L * IAR)   i
where DHigh is a dummy variable equal to 1 for high share turnover firms and 0 otherwise,
DLow is a dummy variable equal to 1 for low share turnover firms and 0 otherwise. The White
(1990) t-statistics are reported.
Window
(-2, 10)
Coef.
(-2, 1)
t-stat
(2, 10)
Coef.
t-stat
Coef.
t-stat

H
-0.0008
-0.42
-0.0037
-2.78
0.0026
1.94

H
1
0.0099
10.71
0.0089
12.98
0.0009
1.53

H
2
0.0120
9.02
0.0116
10.90
0.0003
0.38

H
3
0.0139
5.85
0.0115
6.02
0.0024
1.38

L
0.0020
1.83
-0.0016
-1.98
0.0037
4.45

L
1
0.0091
16.73
0.0064
16.25
0.0025
6.25

L
2
0.0119
14.03
0.0081
11.95
0.0036
5.80

L
3
0.0114
8.64
0.0083
8.45
0.0029
3.04
# of obs.
7333
7333
7333
R-square
0.0956
0.1303
0.0082
Wald Statistic for testing the null hypothesis
1H  1L  0
0.5699
9.8070***
4.4525**
  0
0.0022
7.6900***
9.0881***

0.8782
2.1692
0.0762
H
2
L
2
H
3
 0
L
3
52
Table 10: Spearman rank correlation coefficient matrix between coded firm-specific
variables and information variables
I transform each of the four firm-specific variables, namely, share turnover, percentage of
zero daily returns, market capitalization and analyst coverage, into decile scores based
upon their rank within each calendar quarter. I scale the decile scores to range from -1.0
to 1.0. Similar method has been used in Bhushan (1994) and Bartov et al. (2000) to
control outliers and potential non-linearity in the abnormal return regression.
Bartov, Eli, Suresh Radhakrishnan and Itzhak Krinsky. "Investor Sophistication And
Patterns In Stock Returns After Earnings Announcements," Accounting Review, 2000,
v75(1,Jan), 43-63.
Bhushan, Ravi. "An Informational Efficiency Perspective On The Post-Earnings
Announcement Drift," Journal of Accounting and Economics, 1994, v18(1), 45-65.
Percentage
of daily
zero return
SUE
SQR
SAR
IQR
IAR
Percentage of daily
zero return
Annual share
turnover
Market cap. ($Bil)
-0.063
-0.068
-0.048
-0.0152
0.0014
Annual
share
turnover
Market
cap.
($Billion)
Analyst
coverage
0.000
-0.010
-0.011
0.0185
-0.0070
0.063
0.084
0.063
0.0064
-0.0016
-0.123
-0.025
-0.070
0.0236
-0.0155
-0.126
-0.483
-0.270
-0.250
0.156
0.418
53
Table 11: Multivariate analysis of the joint effect of firm-specific variables on
abnormal return in event window (-2, 10)
I transform each of the four variables, namely, share turnover, percentage of zero daily
returns, market capitalization and analyst coverage, into decile scores based upon their
rank within each calendar quarter. I scale the decile scores to range from -1.0 to 1.0. I
then estimate the abnormal return regression with the interaction terms between these
decile variables and each information variable. To investigate the potential bias due to
multicolinearity among coded firm-specific variables, I study five models:
ARi 
 1   2 * turnover   3 * zerocount   4 * mktcap   5 * coverage
 SUE * ( 1   2 * turnover   3 * zerocount   4 * mktcap   5 * coverage)
(1)
 IQR * ( 1   2 * turnover   3 * zerocount   4 * mktcap   5 * coverage)
 IAR * (1   2 * turnover   3 * zerocount   4 * mktcap   5 * coverage)   i
ARi
ARi

 1   3 * zerocount  SUE * ( 1   3 * zerocount )
(3)
 IQR * ( 1   3 * zerocount )  IAR * (1   3 * zerocount )   i

ARi
ARi
 1   2 * turnover  SUE * ( 1   2 * turnover)
(2)
 IQR * ( 1   2 * turnover)  IAR * (1   2 * turnover)   i


 1   4 * mktcap  SUE * ( 1   4 * mktcap)
(4)
 IQR * ( 1   4 * mktcap)  IAR * (1   4 * mktcap)   i
 1   5 * coverage  SUE * ( 1   5 * coverage)
(5)
 IQR * ( 1   5 * coverage)  IAR * (1   5 * coverage)   i
54
Model (1)
Coef.
t-stat
(2)
Coef.
(3)
Coef.
t-stat
1
2
0.0016
1.88
0.0010
1.15
-0.0017
-1.10
-0.0015
-1.03
3
0.0027
1.66
4
0.0016
0.88
5
0.0032
2.07
1
0.0085
17.17
0.0093
21.45
0.0007
0.96
0.0003
0.45
0.0007
0.91
4
-0.0016
-1.74
5
-0.0029
-3.46
0.0108
15.22
0.0114
18.23
2
0.0007
0.63
0.0003
0.29
3
0.0004
0.31
-0.0023
-1.78
-0.0016
-1.27
1
0.0126
11.11
0.0131
12.37
2
0.0016
0.88
0.0010
0.55
3
0.0013
0.67
4
-0.0011
-0.55
5
-0.0020
-1.01
2
3
1
4
5
Rsquare
# of
obs.
t-stat
0.0014
1.54
0.0014
0.99
0.0094
21.39
0.0020
2.91
0.0116
18.01
0.0019
1.84
0.0131
12.45
0.0021
1.27
(4)
Coef.
t-stat
0.0016
1.85
0.0018
1.32
0.0088
21.03
-0.0033
-4.72
0.0108
17.03
-0.0035
-3.41
0.0127
12.41
-0.0030
-1.80
(5)
Coef.
0.0010
1.16
0.0028
2.08
0.0084
17.59
-0.0037
-5.28
0.0111
15.23
-0.0025
-2.27
0.0127
11.22
-0.0028
-1.64
0.1010
0.0958
0.0975
0.0997
0.0998
10979
10979
10979
10979
10979
55
t-stat
Table 12: Multivariate analysis of the joint effect of firm-specific variables on
abnormal return in event window (-22, -3)
I transform each of the four variables, namely, share turnover, percentage of zero daily
returns, market capitalization and analyst coverage, into decile scores based upon their
rank within each calendar quarter. I scale the decile scores to range from -1.0 to 1.0. I
then estimate the abnormal return regression with the interaction terms between these
decile variables and each information variable. To investigate the potential bias due to
multicolinearity among coded firm-specific variables, I study five models:
ARi 
 1   2 * turnover   3 * zerocount   4 * mktcap   5 * coverage
 SUE * ( 1   2 * turnover   3 * zerocount   4 * mktcap   5 * coverage)
(1)
 IQR * ( 1   2 * turnover   3 * zerocount   4 * mktcap   5 * coverage)
 IAR * (1   2 * turnover   3 * zerocount   4 * mktcap   5 * coverage)   i
ARi
ARi

 1   3 * zerocount  SUE * ( 1   3 * zerocount )
(3)
 IQR * ( 1   3 * zerocount )  IAR * (1   3 * zerocount )   i

ARi
ARi
 1   2 * turnover  SUE * ( 1   2 * turnover)
(2)
 IQR * ( 1   2 * turnover)  IAR * (1   2 * turnover)   i


 1   4 * mktcap  SUE * ( 1   4 * mktcap)
(4)
 IQR * ( 1   4 * mktcap)  IAR * (1   4 * mktcap)   i
 1   5 * coverage  SUE * ( 1   5 * coverage)
(5)
 IQR * ( 1   5 * coverage)  IAR * (1   5 * coverage)   i
56
Model (1)
Coef.
1
2
t-stat
(2)
Coef.
t-stat
-0.0083
-9.17
-0.0090
-10.07
-0.0034
-2.09
-0.0065
-4.32
3
0.0013
0.80
4
0.0054
2.80
5
-0.0062
-3.88
1
0.0076
15.55
0.0070
16.48
0.0017
2.25
0.0032
4.46
0.0012
1.65
4
-0.0042
-4.94
5
0.0048
6.33
0.0076
11.21
0.0073
11.94
2
0.0015
1.41
0.0026
2.73
3
-0.0003
-0.30
-0.0011
-0.88
0.0044
3.77
1
0.0177
14.62
0.0150
13.76
2
0.0086
4.62
0.0097
5.41
3
0.0035
1.90
4
-0.0002
-0.11
5
0.0125
6.33
2
3
1
4
5
Rsquare
# of
obs.
(3)
Coef.
t-stat
-0.0090
-9.90
0.0010
0.67
0.0072
16.57
0.0013
1.88
0.0078
12.55
-0.0014
-1.44
0.0149
13.73
-0.0014
-0.83
(4)
Coef.
t-stat
(5)
Coef.
t-stat
-0.0090
-9.93
-0.0090
-10.12
0.0029
1.97
-0.0053
-3.85
0.0075
15.75
0.0028
4.23
0.0086
12.38
0.0046
4.66
0.0178
14.75
0.0125
7.27
0.0069
16.40
-0.0027
-3.80
0.0077
12.39
0.0013
1.33
0.0151
14.19
0.0026
1.47
0.0775
0.0669
0.0603
0.0615
0.0695
10979
10979
10979
10979
10979
57
Table 13: Multivariate analysis of the joint effect of firm-specific variables on
abnormal return in event window (11, 30)
I transform each of the four variables, namely, share turnover, percentage of zero daily
returns, market capitalization and analyst coverage, into decile scores based upon their
rank within each calendar quarter. I scale the decile scores to range from -1.0 to 1.0. I
then estimate the abnormal return regression with the interaction terms between these
decile variables and each information variable. To investigate the potential bias due to
multicolinearity among coded firm-specific variables, I study five models:
ARi 
 1   2 * turnover   3 * zerocount   4 * mktcap   5 * coverage
 SUE * ( 1   2 * turnover   3 * zerocount   4 * mktcap   5 * coverage)
(1)
 IQR * ( 1   2 * turnover   3 * zerocount   4 * mktcap   5 * coverage)
 IAR * (1   2 * turnover   3 * zerocount   4 * mktcap   5 * coverage)   i
ARi
ARi

 1   3 * zerocount  SUE * ( 1   3 * zerocount )
(3)
 IQR * ( 1   3 * zerocount )  IAR * (1   3 * zerocount )   i

ARi
ARi
 1   2 * turnover  SUE * ( 1   2 * turnover)
(2)
 IQR * ( 1   2 * turnover)  IAR * (1   2 * turnover)   i


 1   4 * mktcap  SUE * ( 1   4 * mktcap)
(4)
 IQR * ( 1   4 * mktcap)  IAR * (1   4 * mktcap)   i
 1   5 * coverage  SUE * ( 1   5 * coverage)
(5)
 IQR * ( 1   5 * coverage)  IAR * (1   5 * coverage)   i
58
Model (1)
Coef.
1
2
t-stat
(2)
Coef.
(3)
Coef.
t-stat
0.0023
2.58
0.0018
1.97
0.0005
0.29
-0.0017
-1.11
3
0.0049
2.97
4
0.0048
2.57
5
-0.0022
-1.38
1
-0.0001
-0.12
0.0000
-0.08
-0.0007
-1.06
-0.0004
-0.67
-0.0005
-0.68
4
-0.0005
-0.55
5
1
0.0008
0.90
-0.0010
-1.52
-0.0011
-1.93
2
0.0010
1.02
0.0010
1.01
3
0.0019
1.78
-0.0010
-0.82
0.0011
0.93
1
-0.0001
-0.06
0.0003
0.24
2
0.0043
2.47
0.0040
2.30
3
0.0043
2.43
4
-0.0024
-1.19
5
0.0003
0.13
2
3
4
5
Rsquare
# of
obs.
t-stat
0.0020
2.26
0.0031
2.25
0.0000
-0.02
-0.0003
-0.55
-0.0009
-1.59
0.0018
1.90
0.0003
0.25
0.0044
2.81
(4)
Coef.
t-stat
0.0018
2.09
0.0013
0.91
-0.0001
-0.23
0.0002
0.36
-0.0012
-2.16
-0.0015
-1.55
-0.0003
-0.31
-0.0050
-3.25
(5)
Coef.
0.0016
1.86
-0.0015
-1.07
0.0001
0.22
0.0006
0.92
-0.0009
-1.40
0.0003
0.33
-0.0003
-0.25
-0.0017
-0.97
0.0026
0.0006
0.0013
0.0010
-0.0001
10979
10979
10979
10979
10979
59
t-stat
Table 14: Multivariate analysis of the joint effect of firm-specific variables on
abnormal return in event window (2, 10)
I transform each of the four variables, namely, share turnover, percentage of zero daily
returns, market capitalization and analyst coverage, into decile scores based upon their
rank within each calendar quarter. I scale the decile scores to range from -1.0 to 1.0. I
then estimate the abnormal return regression with the interaction terms between these
decile variables and each information variable. To investigate the potential bias due to
multicolinearity among coded firm-specific variables, I study five models:
ARi 
 1   2 * turnover   3 * zerocount   4 * mktcap   5 * coverage
 SUE * ( 1   2 * turnover   3 * zerocount   4 * mktcap   5 * coverage)
(1)
 IQR * ( 1   2 * turnover   3 * zerocount   4 * mktcap   5 * coverage)
 IAR * (1   2 * turnover   3 * zerocount   4 * mktcap   5 * coverage)   i
ARi
ARi

 1   3 * zerocount  SUE * ( 1   3 * zerocount )
(3)
 IQR * ( 1   3 * zerocount )  IAR * (1   3 * zerocount )   i

ARi
ARi
 1   2 * turnover  SUE * ( 1   2 * turnover)
(2)
 IQR * ( 1   2 * turnover)  IAR * (1   2 * turnover)   i


 1   4 * mktcap  SUE * ( 1   4 * mktcap)
(4)
 IQR * ( 1   4 * mktcap)  IAR * (1   4 * mktcap)   i
 1   5 * coverage  SUE * ( 1   5 * coverage)
(5)
 IQR * ( 1   5 * coverage)  IAR * (1   5 * coverage)   i
60
Model (1)
Coef.
t-stat
(2)
Coef.
(3)
Coef.
t-stat
1
2
0.0036
5.73
0.0030
4.94
-0.0005
-0.47
-0.0008
-0.75
3
0.0010
0.89
4
0.0010
0.75
5
-0.0004
-0.40
1
0.0014
4.31
0.0017
5.96
-0.0010
-1.82
-0.0011
-2.15
0.0008
1.42
4
-0.0012
-2.02
5
-0.0006
-1.10
0.0016
3.40
0.0020
4.56
2
-0.0023
-3.08
-0.0022
-3.15
3
0.0006
0.78
-0.0024
-2.66
-0.0002
-0.25
1
0.0028
3.34
0.0026
3.37
2
-0.0004
-0.27
-0.0004
-0.31
3
0.0025
1.77
4
-0.0023
-1.45
5
0.0016
1.15
2
3
1
4
5
Rsquare
# of
obs.
t-stat
0.0034
5.45
0.0007
0.70
0.0018
5.94
0.0017
3.55
0.0019
4.38
0.0022
3.21
0.0025
3.27
0.0032
2.68
(4)
Coef.
t-stat
0.0035
5.66
0.0005
0.54
0.0014
4.98
-0.0018
-3.66
0.0014
3.41
-0.0025
-3.59
0.0022
2.91
-0.0028
-2.28
(5)
Coef.
0.0031
5.13
-0.0002
-0.21
0.0013
4.03
-0.0016
-3.31
0.0014
3.04
-0.0020
-2.74
0.0026
3.09
-0.0004
-0.36
0.0118
0.0076
0.0095
0.0092
0.0080
10979
10979
10979
10979
10979
61
t-stat
End of Quarter -1
Interval
End of Quarter 0
B
S
Announcement
day of Quarter -1’s
earnings
C
D
Calendar
day
E
Interval A
T
Announcement
day of Quarter 0’s
earnings
Figure 1. Timeline for frequencies of earnings forecasts in Table 1.
62
End of Quarter -1
End of Quarter 0
S
Announcement
day of Quarter -1’s
earnings
End of Quarter 1
T
Announcement
day of Quarter 0’s
earnings
The pre-announcement
forecast window of
Quarter 1’s earnings
T+14
Calendar
day
The post-announcement
forecast window of
Quarter 1’s earnings
Figure 2. Timeline for construction of earnings forecast revision variables, SQR and SAR. Details are given in Section 3.
63
Announcement
day of Quarter -1’s
earnings
End of Quarter -1
Announcement
day of Quarter 0’s
earnings, i.e.,
Day 0
End of Quarter 0
Day -22
End of Quarter 1
Day -2
Pre-announcement
event window
(-22, -3)
Day 10
Aroundannouncement event
window
(-2, 10)
Day 30
Post-announcement
event window
(11, 30)
Figure 3. Timeline for pre-, around-, and post-announcement event windows
64
Trading
day
Fig 4a. Pre-announcement event window
0.02
0.015
neg
0.01
pos
0.005
0
SUE
IQR
IAR
Fig 4b. Around-announcement event window
0.02
0.015
neg
0.01
pos
0.005
0
SUE
IQR
IAR
Fig 4c. Post-announcement event window
0.02
0.015
0.01
neg
pos
0.005
0
SUE
IQR
IAR
-0.005
Figure 4. Coefficients of information variables in three event windows.
65
Fig 5a. Ratio of (-2, +1) over (-2, +10)
1
0.8
0.6
neg
0.4
pos
0.2
0
SUE
IQR
IAR
Fig 5b. Ratio of (+2, +10) over (-2, +10)
1
0.8
0.6
neg
0.4
pos
0.2
0
SUE
IQR
IAR
Figure 5. Speed of price response to new information around earnings announcement,
measured by the ratio of coefficients for window (-2, 1) over those for window (-2, 10),
and the ratio of coefficients for window (2, 10) over those for window (-2, 10).
66
Fig 3b. High versus low analyst coverage
Fig 3a. Large versus small market cap
0.016
0.016
0.014
0.014
0.012
0.012
0.01
0.01
large
0.008
0.006
0.004
0.002
0.002
0
0
IQR
low
0.006
0.004
SUE
high
0.008
small
IAR
SUE
Fig 3c. High versus low transaction cost
IQR
IAR
Fig 3d. High versus low share turnover
0.016
0.016
0.014
0.014
0.012
0.012
0.01
0.01
0.008
high
0.008
0.006
low
0.006
0.004
0.004
0.002
0.002
0
high
low
0
SUE
IQR
IAR
SUE
IQR
IAR
Figure 6. Total price response to SUE, IQR and IAR by firms of different characteristics. We measure total price response by the
coefficients of SUE, IQR, and IAR in the regression model of abnormal returns in the window (-2, 10). Each panel compares two
groups of firms that differ in one of the four factors: market capitalization, analyst coverage, trading costs, and share turnover.
67
Fig 4a. Large versus small market cap
1.1
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
Fig 4b. High versus low analyst coverage
1.1
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
large
small
SUE
IQR
IAR
IAR
1.1
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
low
IQR
IQR
Fig 4d. High versus low share turnover
high
SUE
low
SUE
Fig 4c. High versus low transaction cost
1.1
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
high
IAR
high
low
SUE
IQR
IAR
Figure 7. Proportional price response to SUE, IQR and IAR in window (-2, 1) by firms of different characteristics. We measure the
proportional price response by the ratio of the coefficient in the regression model of abnormal returns in window (-2, 1) to the
corresponding coefficient in the regression model of abnormal returns in window (-2, 10). Each panel compares two groups of firms
that differ in one of the four factors: market capitalization, analyst coverage, trading costs, and share turnover.
68
Appendix: for scaling by price
Table : Descriptive statistics
Panel A. Descriptive statistics
Std.
Mean
Dev.
SUE
0.00018
0.00542
SQR
-0.00083
0.00469
SAR
-0.00124
0.01019
Percentage
6.01%
5.41%
of daily zero
return
Min.
Q1
Median
Q3
-0.14718
-0.13580
-0.18942
-0.00066
-0.00147
-0.00382
0.00023
-0.00025
-0.00030
0.00126
0.00040
0.00201
0.08492
0.07229
0.16632
0.00%
1.50%
4.50%
9.00%
40.00%
83.41%
6.90%
63.03%
92.73%
143.25%
1,151.08%
12.2
26.6
0.049
1.7
4.0
11.0
520.3
16.5
9.9
3
10
14
21
94
Annual share
116.06%
turnover
Market cap.
($Billion)
Analyst
coverage
69
Max.
Table 2 (Cont.):
Panel B. Spearman rank correlation coefficient matrix
SQR
SAR
Percentage Annual
Market
Analyst
of daily
share
cap.
coverage
zero return turnover ($Billion)
SUE
0.534
0.543
-0.061
0.040
0.056
-0.086
SQR
0.662
-0.001
-0.055
0.091
-0.088
SAR
0.026
-0.044
0.056
-0.096
Percentage of daily
zero return
-0.422
-0.289
-0.132
Annual share
turnover
-0.221
0.123
Market cap. ($Bil)
0.400
Panel C. Spearman rank correlation coefficient matrix for IQR and IAR
IAR
SUE
Percentage Annual
Market
Analyst
of daily
share
cap.
coverage
zero return turnover ($Billion)
0.170
IQR
-0.024
0.002
-0.004
0.008
0.021
IAR
-0.003
-0.001
-0.008
0.007
-0.012
70
Table : Asymmetric response to negative and positive information
The table reports the estimated coefficients for the following regression:
ARi
   1N * NegSUE   2N * NegIQR   3N * NegIAR
,
 1P * PosSUE   2P * PosIQR   3P * PosIAR   i
where NegSUE equals to SUE when SUE is negative and 0 otherwise, PosSUE equals to SUE
when SUE is nonnegative and 0 otherwise, and the other variables are similarly defined. The
White (1990) t-statistics are reported.
Window
Pre-announcement
return in (-22, -3)
Coef.

Around announcement
return in (-2, 10)
t-stat
Post-announcement
return in (11, 30)
Coef.
t-stat
-0.0064
-5.70
0.0051
4.71
0.0023
2.21

N
1
2.0480
6.14
1.1180
3.14
-0.4255
-1.56

P
1
2.3798
7.30
2.7699
8.37
0.5758
2.24

N
2
1.5591
3.07
2.1255
4.32
0.2998
0.99

P
2
3.2377
4.47
1.5751
3.00
-0.8563
-2.23

N
3
2.0708
8.41
1.3898
5.30
0.1977
0.82

P
3
1.4647
0.0464
6.61
0.9163
0.0302
5.01
-0.1664
0.0001
-1.14
R-square
# of obs.
10979
10979
10979
0.3910
0.5318
8.5344
0.0035
5.1507
0.0233
2.6631
0.1027
0.4173
0.5183
4.0994
0.0429
2.5359
0.1113
1.7534
0.1855
1.3913
0.2382
Wald Statistic for testing the null hypothesis
1N  1P  0
 2N   2P  0
 3N   3P  0
71