do firms’ nonfinancial disclosures enhance the value of...
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Do firms’ nonfinancial disclosures enhance the value of analyst services? *
D. Craig Nichols
Assistant Professor Johnson School of Management
Cornell University Ithaca, NY 14853 (607) 255‐0053
Matthew M. Wieland Assistant Professor
J.M. Tull School of Accounting University of Georgia Athens, GA 30602 (706) 542‐3628
August 2009
Abstract: Regulation FD recommends press releases as a primary avenue for timely disclosure of material information to market participants. Firms commonly issue product‐related and business expansion information through press releases, yet no study examines how analysts respond to these information events. We find that forecasting activity nearly doubles at the disclosure date, and that forecasts associated with these disclosures become more accurate and less dispersed across analysts. Finally, in short windows around the disclosure date, the market’s reaction is concentrated at the date of the subsequent forecast revision. Overall, our results suggest that nonfinancial disclosures improve the quality and quantity of information in capital markets and appear to enhance the value of analysts’ services, even though the information is made widely available to all market participants at the time the firm makes the disclosure.
* We thank B. Ayers, L. Bamber, L. Brown, J. Hales, M. Venkatachalam and participants at the 2009 Southeastern Schools Accounting Research Conference for helpful comments and suggestions, and we thank Kapil Jatindarya for valuable research assistance. All remaining errors are our own.
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Do firms’ nonfinancial disclosures enhance the value of analyst services?
1. Introduction
We examine how analysts and the market respond when firms issue product related
and business expansion information through press releases. Although these disclosures likely
have implications for future streams of sales and earnings, the information is not expressed
with accounting numbers. Therefore, we refer to these announcements as nonfinancial
disclosures.1 Nonfinancial disclosures are a common form of communication with capital
markets; Nichols (2009) reports that these disclosures occur more frequently than company
issued earnings guidance. The frequency of occurrence suggests that nonfinancial disclosures
are an important way managers communicate with capital markets. This is consistent with
Regulation FD which recommends press releases as a primary avenue for timely disclosure of
material information to market participants. Although research examines how analysts respond
to earnings announcements, management earnings guidance, and conference calls, we are
aware of no study that examines how analysts respond to nonfinancial disclosures through
press releases. Because nonfinancial disclosures are silent regarding the implications of the
news for future payoffs, we believe that these disclosures likely increase the demand for
analysts’ services in processing this information into earnings forecasts. Consequently, we
examine how a common form of firm communication impacts the value of analysts’ services.
Lang and Lundholm (1996) suggest that the value of analysts’ services stems from the
two roles analysts play in capital markets. First, analysts serve as intermediaries, receiving
information selectively disclosed by the firm and then relaying that information to market
1 We use press release and nonfinancial voluntary disclosure interchangeably throughout the paper.
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participants. Second, analysts are information providers, competing with the firm as a source of
value relevant information. The key difference in these roles is how the firm communicates its
information. In the intermediary role, the firm communicates to the analyst but not the market
as a whole; in the provider role, the firm communicates to all market participants at the same
time. Lang and Lundholm (1996) argue that improved firm disclosure enhances the value of
analysts’ services when firms selectively disclose, but reduces the value of analysts’ services
when firms disclose to everyone.
Regulation FD forbids selective disclosure of management’s private information to
analysts after 2000, thereby reducing the value of analysts’ services arising from their
intermediary role. However, Regulation FD also encourages firms to issue press releases as a
timely avenue for communicating value relevant information to all market participants.
Managers have wide latitude in the content of company issued press releases, and can issue
press releases with or without explicit earnings guidance. Compared to earnings guidance,
nonfinancial disclosures are less likely to preempt analysts’ forecasts. In fact, given the difficulty
in identifying the value implications of nonfinancial disclosures, many market participants are
unlikely to process and trade on the information in nonfinancial disclosures (Bloomfield 2002,
Hirshleifer and Teoh 2003). Consequently, nonfinancial disclosures can allow analysts to act as
information intermediaries even when the analysts’ reports are based on public information.
Moreover, theory predicts that superior information processors will increase private
information search activities in response to public information events (Kim and Verrecchia
(1994, 1997), suggesting that nonfinancial disclosures can lead to improvements in analysts’
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information provider role. Thus, improved firm disclosure can increase the demand for analysts’
services even when the firm discloses to all market participants.
If nonfinancial disclosures enhance the value of analysts’ services, we expect to observe
three key patterns in the data. First, analysts’ forecasting activity should increase at the date of
disclosure. If nonfinancial disclosures provide credible information about future streams of
revenues and earnings, the public release of this information should cause analysts to revise
their earnings expectations. Second, nonfinancial disclosures should improve accuracy across
analysts. If analysts have the ability to identify the implications of nonfinancial disclosures for
future sales and earnings, then revised forecasts should better reflect the future earnings
performance of the firm. Third, stock price reactions should be concentrated at the forecast
revision date instead of the nonfinancial disclosure date. If the value of the analyst report is
diminished because market participants fully trade on the information at the disclosure date,
the analysts’ report should not lead to an additional stock price response.
To test these predictions, we collect a sample of nonfinancial disclosures from Capital
IQ. Capital IQ, a division of Standard & Poor’s, maintains a database of key developments for all
publicly traded companies, including press releases. We focus our attention on product related
and business expansion press releases. Product announcements pertain to the introduction,
change, improvement, or discontinuation of a company’s products or services and include all
announcements from the research to final launch of the product and any enhancements to the
product after launching. Business expansion announcements refer to the growth of a company
by means of increasing their current operations through internal growth, like entering into new
markets with existing product, opening a new branch, establishing a new division, increasing
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production capacity, and investing additional capital in the current business. However, business
expansion does not include growth by acquisition. Although Capital IQ provides data for other
key developments (such as management turnover), we focus on product and business
expansion announcements to reduce costs of data collection and because these
announcements are most likely to provide information about future streams of revenues and
earnings.
We begin our analysis by examining changes in forecasting behavior around nonfinancial
disclosures. If nonfinancial disclosures provide credible, value relevant information that
increases the demand for analysts’ reports, forecasting activity should increase at the time of
the disclosure. This is what we find. In particular, starting with the day of the disclosure, analyst
forecasting activity almost doubles in intensity, reflecting a statistically significant increase in
forecasting behavior. This increase is persistent, extending up to five days after the disclosure
event. This confirms that nonfinancial disclosures provide credible information about future
earnings, and suggests that analysts respond to an increase in demand for their services by
providing more reports.
Next, we examine the effect that nonfinancial disclosures have on forecast accuracy.
Compared to nondisclosure quarters, analysts’ forecasts become more accurate when firms
issue nonfinancial disclosures. Although we do not make a directional prediction, we also show
that forecast dispersion declines in announcement quarters relative to non announcement
quarters. This suggests that analysts incorporate the information in nonfinancial disclosures
into superior estimates of future earnings, and that nonfinancial disclosures reduce the
uncertainty about future earnings across analysts.
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Finally, we examine the concentration of stock price reactions during short windows
starting at the announcement and ending at the forecast revision. The purpose of this analysis
is to examine whether analysts’ revisions that incorporate the implications of public,
nonfinancial disclosures trigger stock price reactions or whether analysts are merely responding
to information already incorporated into price (Abarbanell 1991). This analysis requires a
benchmark comparison, and the benchmark we choose is management forecasts. For
management forecasts, we find that stock price reactions are concentrated at the management
forecast date, with very little reaction occurring at the analyst revision date. In contrast, we find
a more concentrated reaction at the analyst revision date for nonfinancial disclosures. This is
consistent with nonfinancial disclosures enhancing the value of analysts’ services, even though
these disclosures are widely released to all market participants.
Our paper makes several contributions to the literature. First, we provide evidence on
the credibility of a common way managers communicate with capital markets. Regulation FD
recommends press releases as a means of conveying timely information to market participants,
yet little research examines the credibility and capital market consequences of press releases
other than earnings announcements and management guidance, and some researchers
question the credibility of such disclosures (Francis, Hanna, and Philbrick 1997). We confirm the
credibility of nonfinancial disclosures, which occur more frequently than guidance, by
demonstrating a significant increase in analyst forecasting activity at the date of the disclosure,
as well as an increase in the accuracy of those forecasts.
Second, we provide evidence on the types of information analysts use in developing
their forecasts. This is consistent with the calls in Schipper (1991) and Brown (1993) for more
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evidence on the inputs that analysts use in their production processes. We show that analysts’
forecasts that are revised to incorporate the information in nonfinancial disclosures are more
accurate, and that nonfinancial disclosures reduce the dispersion in analysts’ forecasts. Finally,
we provide evidence that firms’ public disclosure can enhance the value of analysts’ services.
Thus, even though Regulation FD abolishes selective disclosure of the firm’s information to
analysts, thereby weakening the intermediary role analysts play in capital markets, analysts can
still serve as intermediaries when the valuation implications of publicly available information
are unclear or difficult to identify.
The remainder of the paper proceeds as follows. Section 2 discusses related literature
and develops our predictions. We introduce and describe the sample in section 3. We present
our results in section 4, and we provide concluding remarks in section 5.
2. Background
We examine how analysts respond when firms issue product announcements and
business expansion announcements through press releases. Prior research examines how
analysts respond to other information provided by the firm. A number of papers examine how
analysts respond to earnings announcements (Chan et al. 1996; Zhang 2006). Several papers
examine how analysts respond to management issued guidance, which is typically released
through stand alone press releases or in conjunction with earnings announcement press
releases (Williams 1996; Libby et al. 2000). Prior research also examines how analysts respond
to conference calls. Bowen, Davis, and Matsumoto (2003) show that errors and dispersion in
analysts’ forecasts decline more during quarters with conference calls compared to those
without. Although prior research examines how analysts respond to earnings related
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information issued through press releases, we are not aware of any paper that examines how
analysts respond to nonfinancial disclosures. The purpose of this paper is to fill this void in the
literature.
Nonfinancial disclosures are a common way firms communicate with capital markets.
Nichols (2009) reports that nonfinancial disclosures occur more frequently than management
issued guidance, the most often studied form of voluntary disclosure examined in the
literature. Moreover, Nichols (2009) reports that over 60 percent of earnings guidance events
occur at the earnings announcement. In contrast, less than 10 percent of nonfinancial
disclosures occur at earnings announcements. This suggests that, as stand alone information
events, nonfinancial disclosures play a prominent role in firms’ disclosure strategies. In
addition, nonfinancial disclosures also appear to have important stock price effects. Nichols
(2009) reports significant positive stock price reactions of 30 to 60 basis points at the
nonfinancial disclosure date. This reaction contrasts with management guidance, which prior
research shows to have a significant negative reaction, on average (Hutton, Miller, Skinner
2003). The stock price reaction to nonfinancial disclosures suggests that they provide credible,
value relevant information to market participants. Overall, it appears that nonfinancial
disclosures affect the amount and quality of information provided by the firm to the market.
The amount and quality of information provided by the firm has important
consequences for the value of analysts’ services. Lang and Lundholm (1996) argue that analysts
serve two roles in capital markets. First, analysts are intermediaries. They receive information
selectively disclosed by the firm and relay that information to market participants (Ajinkya and
Gift 1984). Second, analysts are information providers. They provide new information to the
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market from independent research activities. In the Lang and Lundholm (1996) framework, the
effect of firm provided information on the value of analysts’ services depends on which channel
the firm uses to distribute its information. If the firm selectively discloses information to the
analysts, the value of analysts services increases because analysts’ reports add a richer set of
information to the information already possessed by the market. In contrast, if the firm
distributes its information widely to all market participants, the value of analysts services
declines. In this case, the information provided by the firm is more likely to preempt the analyst
report, thereby reducing its value.
In an attempt to level the playing field, Regulation FD suppresses selective disclosure of
firm information to certain market participants. However, Regulation FD also aims to avoid a
chilling effect on disclosure practices, and recommends press releases as a timely means for
firms to communicate material information to all markets participants. Thus, Regulation FD
eliminates the selective disclosure of firm information to analysts, and encourages more public
disclosure that potentially preempts analysts’ reports. Consequently, Regulation FD should
reduce the value of analysts’ services arising from both their intermediary and provider roles.
Analysts should therefore prefer less disclosure by the firm, or should choose to follow firms
with poorer information environments. However, this is inconsistent with empirical findings.
Although Mohanram and Sunder (2006) find some evidence that analysts shift coverage to
lesser followed firms after Reg FD, analysts still tend to follow large firms with rich information
environments (Lang and Lundholm 1993, among others). Interestingly, these are the same
types of firms that are likely to issue the nonfinancial disclosures that we examine. Consistent
with this, Nichols (2009) finds that the effect of number of analysts following the firm on the
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likelihood of a disclosure event is stronger for nonfinancial disclosures than for guidance. Thus,
analysts appear to have a preference for nonfinancial disclosures, suggesting that nonfinancial
disclosures actually stimulate demand for analysts’ services.
Lang and Lundholm (1996) assume a relatively high degree of market efficiency. Once
information is released by the firm, their framework assumes that information is impounded
into prices quickly and completely, leaving little room for analysts to promote the price
formation process. However, this ignores potentially substantial costs in acquiring and trading
on the information revealed by the firm. An alternative characterization of the use of
information in markets is given by the incomplete revelation hypothesis (Bloomfield 2002). The
incomplete revelation hypothesis states that investors will only acquire and trade on a signal to
the extent the benefits outweigh the costs.2 Signals that are difficult to acquire, process, or
trade on are unlikely to be fully impounded into price. Because nonfinancial disclosures do not
provide estimates of the earnings or sales effects, the valuation implications are likely to be
difficult to estimate. This should provide an opportunity for analysts to promote the price
formation process because identifying the sales and earnings effects of nonfinancial disclosures
likely requires a deep familiarity with the firm’s industry, and analysts are generally viewed as
industry experts (Piotroski and Roulstone 2004, Jacob et al. 1999).
If nonfinancial disclosures enhance the value of analysts’ services, we expect to observe
three key patterns in the data. First, analysts’ forecasting activity should increase at the date of
disclosure. If market participants have difficulty identifying the earnings implications of
nonfinancial disclosures, the demand for analysts’ reports that reflect revised earnings
2 Hirshleifer and Teoh (2003) make similar arguments on the basis of limitations in investors’ attention, processing skills, and cognitive abilities.
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expectations should increase. However, an increase in forecast intensity is not sufficient to
show that analysts benefit from additional disclosure of nonfinancial information. In particular,
managers could issue nonfinancial disclosures for strategic purposes, and such disclosures may
lack credible information about the firm’s future performance. Moreover, analysts have a
strong tendency to revise their forecasts after managers issue other types of information such
as earnings guidance, even though guidance largely preempts the information in the analysts’
forecasts. These concerns motivate our subsequent predictions.
Second, if nonfinancial disclosures provide credible information about future streams of
revenues and earnings, the public release of this information should improve accuracy across
analysts. If analysts have the ability to identify the implications of nonfinancial disclosures for
future sales and earnings, then revised forecasts should better reflect the future earnings
performance of the firm. We also examine whether nonfinancial disclosures reduce dispersion
in analysts’ forecasts. Dispersion should decline if nonfinancial disclosures resolve uncertainty
about the firm’s future prospects. However, although dispersion declines as uncertainty
declines, it rises with declines in consensus. Consensus declines with private information search
activities (Barron, Kim, Lim, and Stevens 1998), and Kim and Verrecchia (1997) suggest public
release of information spurs private information search by superior information processors.
Consistent with this, Barron, Byard, and Kim (2002) show that the amount of private
information in analysts’ forecasts increases immediately after earnings announcements.
Consequently, it is possible that nonfinancial disclosures lead to private information search
activities by analysts, which could reduce consensus and thereby increase dispersion. Because
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the effect of nonfinancial disclosures on dispersion is unclear, we make no prediction regarding
forecast dispersion.
Finding an improvement in accuracy helps establish that nonfinancial disclosures
increase the value of analysts’ services, but it does not rule out preemption. That is, the analyst
report may simply lag the stock price reaction to the information, and may do little to promote
the price discovery process. This leads to our final prediction, namely, that stock price reactions
should be concentrated at the forecast revision date instead of the nonfinancial disclosure date.
If the value of the analyst report is diminished because market participants fully trade on the
information at the disclosure date, the analysts’ report should not lead to an additional stock
price response. However, if the market’s response to the disclosure is incomplete, we should
observe not only a positive correlation between the disclosure and revision window stock price
reaction, but also a concentration of the stock price reaction at the revision date. The
remainder of the paper describes our empirical analyses designed to test these predictions.
3. Sample selection and research design
3.1 Sample selection
We draw our nonfinancial disclosure data from Capital IQ. Capital IQ maintains a
database of key developments for all publicly traded firms, collected from press releases and
news outlets. We restrict our focus to press releases to ensure the announcement was initiated
by the firm. Although Capital IQ provides data for other key developments (such as
management turnover, changes in dividend policy, etc.), we focus on product and business
expansion announcements to reduce costs of data collection and because these
announcements are most likely to provide information about future streams of revenues and
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earnings. The Capital IQ sample consists of 53,682 press releases for which the source is PR
Newswire, Business Wire, or SEC Form 8k for the period 2002 to 2008.
We provide examples of product and business expansion announcements from Capital
IQ in the appendix. Capital IQ defines product announcements as “announcements that pertain
to the introduction, change, improvement, or discontinuation of a company’s products or
services and include all announcements from the research to final launch of the product and
any enhancements to the product after launching.”3 Product announcements often provide
information about new products lines, new airline routes, new web sites, new software
applications, new drug developments or progress in clinical trials. In addition, product
announcements include discontinuations of products or services as well as product recalls.
Firms in health or technology related industries often include technical information in the
announcement pertaining to clinical trials or product capabilities and specifications, but firms in
more traditional manufacturing or retail industries rarely provide any quantitative information.
Capital IQ defines business expansions as “the growth of a company by means of
increasing their current operations through internal growth, like entering into new markets
with existing product, opening a new branch, establishing a new division, increasing production
capacity, and investing additional capital in the current business.” However, business expansion
does not include growth by acquisition. Business expansions typically provide information
about new store or restaurant openings, new distribution centers, new branches or
representative offices, and investment in new manufacturing facilities. Business expansions
commonly include quantitative measures of volume or capacity, such as the number of square
3 Capital IQ does not maintain a data definitions guide for end users. The definitions for product announcements and business expansion announcements were obtained through direct correspondence with Capital IQ.
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feet for stores or restaurants, the number of guests a new hotel can accommodate, and
expected volume of new production facilities. In addition, business expansion releases
commonly indicate the dollar amount invested in the expansion, but this information is not
easily extracted from the information provided by Capital IQ. Business expansions rarely refer
to closures or downsizings, and then only if an expansion is mentioned in the same
announcement.4
If an announcement meets the definition of more than one key development, Capital IQ
assigns it to multiple key developments. Thus, we are able to identify product and business
expansion announcements that do not have forward looking performance information such as
sales or earnings forecasts. Through examination of the data, we found that the vast majority of
business expansion and product announcements contained no forward looking information. For
a small sample of observations, we verified that announcements with forward looking sales or
earnings information were also appropriately classified as guidance by Capital IQ.
We conduct three sets of analyses based on this data. First, we examine changes in
analyst forecasting activity around the nonfinancial disclosure event. We label this the “forecast
activity” analysis. Second, we examine whether nonfinancial disclosures lead to more accurate
forecasts, which we call the “analyst reaction” analysis. Third, we examine how the market
reacts to the disclosure and subsequent revision, as captured by returns and trading volume.
We refer to this analysis as the “market reaction” analysis. Because these analyses have
4 Capital IQ has a separate key development for divestitures and downsizings. This key development has about 30,000 total events for all firms, compared to nearly 180,000 business expansion announcements for all firms. “All firms” includes public and private, foreign and domestic. This suggests that companies do not experience these events with the same frequency as expansions, or that companies do not publicize these events to the same extent. The latter possibility is consistent with Kothari et al. (2009), who suggest firms accelerate voluntary disclosure of good news but delay disclosure of bad news.
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different research designs and different data requirements, we use different criteria in selecting
the samples. Table 1, panel A summarizes our sample selection process for the analyst reaction
analysis. First, we create a sample that contains all firm quarters that have data available in the
I/B/E/S Detail and Actuals file to calculate beginning and ending event quarter analyst forecasts
and actual earnings for the period 2002 to 2008. We merge the data to CRSP to get market data
(61,381). We lose 8,875 observations due to missing control variables. We then eliminate the
top and bottom 1 percent for each dependent variable to arrive at a sample of 50,772 firm
quarters. Second, we merge the I/B/E/S sample to the Capital IQ sample and identify 11,701
firm quarters that contain a nonfinancial voluntary disclosure. We also create a sub sample that
only contains firm quarters for which the firm has at least one press release during the sample
period to conduct within firm tests. The within firms sample contains 40,334 firm quarter
observations.
Table 2 presents sample composition data. Table 2, panel A provides yearly totals for
the whole sample and broken out by firm quarters that include a press release. We observe
increases in the number of announcements each year through 2006. In 2007, the number of
announcements declines. For 2008 we only include observations for firms with fiscal years
ending in January because firms with later fiscal year ends are not included in the 2008 I/B/E/S
file. Firm quarters with press releases, in each year of our sample, range from 35% to 49% of
the observations in our sample period.
Table 1, panel B describes the sample selection procedure for the forecast activity and
market reaction analyses. It begins with the Capital IQ sample and is reduced by I/B/E/S and
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CRSP requirements to 31,374 observations. In addition, the following two criteria reduce the
effects of potentially confounding factors:
a) We eliminate observations that have an earnings announcement three days prior
to the press release or prior to the end of the returns window for the first analyst
forecast.
b) We eliminate press releases if the firm issues another press release prior to the
end of the returns window for the first analyst forecast.
After implementing these two criteria, the forecast activity sample includes 12,391
observations. For the market reaction analysis, we eliminate press releases for which the first
analyst forecast occurs on the day of or the day after the press release. This allows us to
examine differences in the market’s reaction to the disclosure and the forecast revision. To
complete the market analysis sample, we then eliminate observations for which the first analyst
forecast occurs more than 6 trading days after the press release. We do this to provide
reasonable assurance that the analyst forecast relates to the press release. The market reaction
test sample consists of 4,156 press releases.
Table 2, panel B provides data on the composition of the market reaction sample. The
sample increases over time to a peak of 1,475 in 2006. Product announcements represent the
majority of the press releases although the percentage of business expansion press releases
grew over the time period. Panel C reveals the sample consists largely of press releases made
by firms in the technology, health care, and consumer services sectors.
3.2 Research Design
3.2.1 Forecast Activity Analysis
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In our first analysis, we examine whether analysts respond to nonfinancial disclosures by
increasing their forecast activity. We predict that analysts will increase their forecasting activity
starting with the day of the disclosure event. To conduct this analysis, we first compute the
percentage of firms in the sample with at least one annual analyst earnings forecast made in
the eleven days surrounding the press release date. Then, we test for a significant increase in
the percentage of firms with a forecast using a z test for two proportions that compares the
days of and immediately after the nonfinancial disclosure to the days before the disclosure.
3.2.2 Analyst Reaction Analyses
In our next set of analyses, we examine whether press releases improve the quantity
and quality of information that analysts incorporate into their forecasts. We utilize two
observable properties of analysts’ forecasts to measure information: error in the mean
consensus forecast and dispersion in individual forecasts. We design our tests similar to Bowen,
Davis, Matsumoto (2003). The general form of each dependent variable is the scaled change in
the forecast characteristic – error (ERROR) or dispersion (DISP) – before and after a press
release:
(Post release measure – Pre release measure)(Stock price at beginning of fiscal quarter)
We measure forecast error for each firm quarter as the absolute value of the difference
between the mean of analysts’ annual earnings estimates and actual annual earnings per share.
We require analysts to make a forecast in the pre and post period to be included in the sample.
We examine annual earnings because we expect product announcements and business
expansion announcements to contain more forward looking information than is less related to
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the current quarter.5 We measure dispersion for each firm quarter as the standard deviation of
analysts’ individual forecast estimates. Post vs. pre differences isolate the effect of the press
release and control for differences in the levels of forecast error and dispersion across firms. If
forecast error decreases following press releases, then the post press release forecast error will
be less than the pre press release forecast error and the scaled changed in forecast error
(ERROR) will be negative. Similarly, if forecast dispersion decreases following press releases,
then the scaled change in forecast dispersion (DISP) will be negative. Figure 1 illustrates the
timeline of events and describes the measurement of the dependent variables in detail.
The pre press release component of the dependent variables is the forecast error or
forecast dispersion for the current annual period measured at the end of quarter t 1 (i.e.
beginning of quarter t). We consider quarter t a press release quarter if the firm issues a
business expansion or product announcement during the quarter.
We measure post press release forecast error or forecast dispersion at the end of the
fiscal quarter. We include analysts’ forecasts that have been issued or reviewed from the time
of the press release to the end of the fiscal quarter. If a firm issues more than one press release
during a fiscal quarter we use the first press release to identify the beginning of the forecast
window. For non press release quarters, we do not have an identifiable event to indicate the
beginning of the post period. To handle this problem, we include forecasts that have been
issued or reviewed after the mid point of the quarter as press releases are likely uniformly
distributed during a fiscal quarter.
5 Results are similar if we use forecasts for the next annual period instead of the current annual period.
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Our tests of the relation between press releases and (1) changes in analyst forecast
error and (2) changes in forecast dispersion, controls for firm size, earnings surprise, and
forecast age similar to Bowen et al. (2003). We define these variables as follows:
SIZE = log of the market value equity at the beginning of quarter t;
SURP = |EPSt – EPSt 1|/Pt 4, where EPSt is the I/B/E/S actual earnings per share forquarter t and P t 4 is the ending price per share at quarter t 4;
AGE = average age of forecasts of annual t earnings after press release less theaverage age of forecasts at the beginning of the quarter.
FEPRE = consensus forecast error for firm i for annual t earnings at the beginning ofthe quarter;
DISPPRE = standard deviation of analysts’ forecasts of firm i’s annual earnings at thebeginning of the quarter;
MIG = 1, if the quarter contains management issued guidance.
SIZE proxies for the richness of the firm’s information environment. We proxy for the
difficulty in forecasting earnings using the change in quarterly earnings relative to the prior year
(SURP) for the current and prior quarter. We also control for differences in the average age of
the consensus forecasts ( AGE) because forecast age is an important determinant of forecast
accuracy (Brown, 2001). FEPRE and DISPPRE capture the initial level of forecast error or
dispersion, which could limit the potential for reduction in these measures (Bowen et al. 2003).
We include MIG to control for management issued guidance because firms that issue
nonfinancial disclosures are more likely to issue guidance (Nichols 2009). We also include an
indicator variable for the fiscal quarter represented (QTR1, QTR2, QTR3) because horizon
impacts the accuracy of annual earnings forecasts.6
6 Prior quarter results become available during each quarter. Therefore, accuracy should improve and dispersion should decline mechanically for current year earnings. However, this should affect all observations the same, and should not be correlated with DPR. In addition, inclusion of quarterly indicators should help control for this effect.
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The following two regressions form the basis of our cross sectional tests:
Equation 1:
Equation 2:
DPR is a dummy variable equal to 1 if the quarter contains a press release and 0
otherwise. A significantly negative coefficient on 1 in equation 1 is consistent with press
releases reducing the error in analysts’ earnings forecasts. A significantly negative coefficient on
1 in equation 2 is consistent with press releases reducing the dispersion of analysts’ earnings
forecasts.
3.2.3 Market Reaction Analyses
In our final analysis we examine when the market incorporates the information in the
nonfinancial voluntary disclosure. If analysts have an information processing advantage due to
their knowledge of the industry, the firm, and the general economy, then we would expect
relatively more information to be incorporated into price at the time of the first analyst forecast
rather than at the press release date. For these tests we measure absolute returns and trading
volume over two event windows (see figure 1 for a description). The first event window,
labeled the total event window, begins on the day prior to the press release and extends
Finally, results are unchanged if we use forecasts for the next annual period (FY2), which is not subject to the same mechanical effect.
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through the second trading day following the first analyst forecast. The second event window,
the first analyst forecast window, begins on the day of the first analyst forecast and extends
two trading days. |RET%| (VOL%) equals the three day absolute returns (trading volume) from
the first analyst forecast window divided by the absolute returns (trading volume) over the
total event window to capture the percentage of information being impounded into price at the
time of the first analyst forecast. In order to determine whether the percentage of information
coming out around the first analyst forecast is meaningful, we gather a sample of 688 press
releases that Capital IQ identifies as management issued guidance and that fit our sample
selection criteria. Because management guidance tends to preempt analysts’ forecasts, using
management guidance as a benchmark allows us to assess whether nonfinancial disclosures
also preempt forecasts. To test this, we estimate the following models:
Equation 3:
Equation 4:
BE is a dummy variable equal to 1 if the press release relates to a business expansion
and 0 otherwise. PA is a dummy variable equal to 1 if the press release relates to a product
announcement and 0 otherwise. A significantly positive coefficient on 1 and 2 in equation 3 is
consistent with relatively more information being impounded at the first analyst forecast for
business expansion and product announcement press releases relative to management issued
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guidance. A significantly positive coefficient on 1 and 2 in equation 4 is consistent with
relatively more information being impounded at the first analyst forecast for business
expansion and product announcement press releases relative to management issued guidance.
We include the absolute value of the analysts’ forecast revision (|AFREV|) to control for
the amount of information created by a press release. We include all forecasts issued or
reviewed during the first analyst forecast window and the forecasts made by those same
analysts in the 60 trading days preceding the press release. We interact this variable with BE
and PA to examine whether greater information increases the information impounded at the
first revision date for nonfinancial voluntary disclosures. A significantly positive coefficient on 4
and 5 in equation 3 is consistent with relatively greater information impounded at the first
analyst forecast for business expansion and product announcement press releases relative to
management issued guidance. Similarly, a significantly positive coefficient on 4 and 5 in
equation 4 is consistent with relatively more information impounded at the first analyst
forecast for business expansion and product announcement press releases relative to
management issued guidance.
We control for the firm’s information environment by including the natural log of the
firm’s market capitalization (SIZE). We control for the uncertainty about the firm’s future
earnings by including the standard deviation of the consensus analysts’ annual earnings
forecast (SDAF_pre). We also control for the number of analysts issuing a forecast in the first
analyst forecast window (NUMAF_post). Finally, we control for the number of trading days after
the press release that the first analyst forecast falls on Tdays_af.
4. Empirical Results
22
We first examine whether analysts respond to business expansion or product
announcement press releases by issuing forecasts. If these press releases contain new
information then we should see an increase in forecasting activity following their issuance.
Table 3 reports the percentage of firms in the sample with at least one analyst forecast made in
the eleven days surrounding the press release. We find a significant increase in the percentage
of firms with an analyst forecast on the day of and the days following the press release. For
example, on the day of a business expansion (product announcement) press release, 19.2 (22.1)
percent of the sample has at least one analyst forecast, which represents an increase of 6.8
(10.2) percent over the day preceding the press release. The increased forecasting activity
continues through trading day three (five) for business expansions (product announcements).
This provides support for our prediction that nonfinancial disclosures increase the demand for
analysts’ reports that reflect revised earnings expectations.
In table 4, we report descriptive statistics for the variables used in our forecast accuracy
and bias tests. The negative mean on the ERROR and DISP variables indicate that analysts’
forecasts becomes more accurate and less dispersed over the fiscal quarter. Firm quarters that
contain a press release relate to firms with larger market capitalization. Press releases occur
less often in the first quarter than in quarters 2 through 4.
We present the results of the regressions in Table 5. As predicted the coefficient on the
press release variable (DPR) is significantly negative (p value <.01). Thus, the decrease in
forecast error directly after the press release is significantly greater in quarters in which firms
issue a business expansion or product announcement press release.
23
Table 5, panel B reports the results of the regressions to test the change in dispersion.
The coefficient on the press release variable (DPR) is significantly negative (p value <.01). This
suggests that the decrease in forecast dispersion when a firm issues a press release is
significantly greater than when firms do not issue a nonfinancial voluntary disclosure.
Table 6 presents the results of the within firm tests. Recall, that to be included in this
sample, the firm must have issued a nonfinancial voluntary disclosure at some point during the
sample period. Thus, the control group only contains quarters for which the firm does not issue
a press release. Table 6, panel A reports the results of the regressions to test the change in
forecast error. As predicted the coefficient on the press release variable (DPR) is significantly
negative (p value <.01). Thus, for firms that issue nonfinancial voluntary disclosures, the
decrease in forecast error is significantly greater in quarters in which these firms issue relative
to when they do not issue a nonfinancial voluntary disclosure.
Table 6, panel B reports the results of the regressions to test the change in forecast
dispersion. The coefficient on the press release variable (DPR) is significantly negative (p value
<.01). Thus, for firms that issue nonfinancial voluntary disclosures, the decrease in forecast
error is significantly greater in quarters in which these firms issue relative to when they do not
issue a nonfinancial voluntary disclosure.
To examine whether more information is being created for nonfinancial voluntary
disclosures at the first analyst forecast date, we examine the percentage of returns and trading
volume that come in the three days beginning with the first analyst forecast relative to the
entire event window. If the information in BE and PA press releases is harder to process and the
market relies on analysts, then we would expect the difference between the information
24
impounded on the first forecast revision date and the information at the press release date to
be larger for BE and PA firms. In order to assess the significance of these dependent variables,
we collect a sample of 688 press releases from Capital IQ identified as management issued
guidance that meet the same sample requirements as the business expansion and product
announcements.
Table 7 reports descriptive statistics for the variables of interest in the market reaction
analyses. |RET%| and VOL% are both significantly different from zero for business expansion
and product announcements, and greater than management issued guidance (not reported).
So, in the univariate setting, more information is impounded into price at the first analyst
forecast date than at the press release date for business expansions and product
announcements relative to management issued guidance. This is consistent with analysts
fulfilling their role as information intermediaries. The information in the press release also
increases analysts’ accuracy across all of the types. Firms that provide press releases about
business expansion and product announcements are larger and have a larger analyst following
than management guidance firms. There is also less uncertainty about future annual earnings
for MIG than BE and PA. Analysts react more quickly to management issued guidance than they
do to business expansion and product announcement (7.0 days compared to 8.5 and 8.1 days).
These numbers are inflated because we remove observations for which the first analyst
forecast occurs on the day of or the day following the press release.
Table 7, panel C details correlations between our variables of interest. BE and PA are
positively correlated with |RET%| and VOL% while MIG is negatively correlated with both of
25
these. This provides initial univariate evidence that relatively more information comes at the
first analyst forecast for BE and PA than for MIG.
We test this in a multivariate setting in Table 8. We expect a positive coefficient on the
indicator variables BE and PA and a positive coefficient on the interaction between the absolute
value of analyst forecast revision and BE and PA. We find the expected positive coefficients on
both BE and PA and we also find a statistically significant positive coefficient on the
interactions.7 This indicates that the market impounds more information on the days following
the first analyst forecast and in the analyst forecast revision when it was a BE or PA
announcement.
5. Conclusion
In this paper, we examine how analysts respond when firms issue nonfinancial
information about their products and business expansion plans through press releases.
Nonfinancial disclosures are a common and important way managers communicate with capital
markets, consistent with Regulation FD which recommends press releases as a primary avenue
for timely disclosure of material information to market participants. Although research
examines how analysts respond to earnings announcements, management earnings guidance,
and conference calls, we are aware of no study that examines how analysts respond to
nonfinancial disclosures through press releases. Our study fills this void in the literature.
We find that analysts forecasting activity increases substantially at the date of the
nonfinancial disclosure, nearly doubling in intensity. This suggests that nonfinancial disclosures
increase the demand for analysts’ services, and analysts respond by providing more reports.
7 We also correct standard errors for clustering by industry, firm, and press release year and obtain similar results.
26
Nonfinancial disclosures lead to more accurate and less dispersed forecasts. This indicates that
nonfinancial disclosures provide credible information with implications for future streams of
sales and earnings, and that analysts are able to identify these implications. Finally, we show
that nonfinancial disclosures do not preempt the analyst report. Compared to management
issued guidance, the market’s reaction to nonfinancial disclosures occurs less at the disclosure
date and is more heavily concentrated at the forecast revision date. This suggests that market
participants have difficulty in identifying the valuation implications of nonfinancial disclosures,
and rely on the revised forecasts of analysts even though the information is already publicly
available.
Overall, our evidence confirms that nonfinancial disclosures are an important event that
improves the quality and quantity of publicly available information. However, instead of
preempting analysts’ reports and reducing the value of analysts’ services, publicly available
nonfinancial disclosures appear to increase the demand for analyst research. This suggests that
Regulation FD did not unambiguously impair the role of analysts in capital markets when it
eliminated selective disclosure and encouraged more public disclosure, as would be suggested
in light of the information intermediary and information provider roles that analysts play.
27
App
endix.Exam
ples
ofKe
yDevelop
men
tsfrom
CapitalIQ
Com
pany
H
eadl
ine
Key
Dev
elop
men
t So
urce
C
ypre
ss
Sem
icon
duct
orC
orpo
ratio
n (N
YSE
:CY)
Cyp
ress
and
UP
EK
P
artn
er o
n B
iom
etric
S
ecur
ity R
efer
ence
D
esig
n fo
r Saf
e,
Con
veni
ent A
cces
s to
U
SB
Fla
sh D
rives
Cyp
ress
Sem
icon
duct
or C
orpo
ratio
n an
d U
PE
K, I
nc. i
ntro
duce
d a
refe
renc
e de
sign
for
US
B F
lash
Driv
es (U
FDs)
pro
tect
ed b
y fin
gerp
rint a
uthe
ntic
atio
n te
chno
logy
, del
iver
ing
the
indu
stry
's s
trong
est s
ecur
ity b
y au
then
ticat
ing
user
s on
the
UFD
sys
tem
inst
ead
of
on a
PC
. The
new
CY4
665
refe
renc
e de
sign
use
s U
PE
K's
Tou
chS
trip
Fing
erpr
int
Aut
hent
icat
ion
Sol
utio
n al
ong
with
Cyp
ress
's E
Z-U
SB
NX
2LP
-Fle
x U
SB
con
trolle
r to
addr
ess
the
grow
ing
need
from
cor
pora
te IT
man
ager
s to
allo
w e
nter
pris
e da
ta to
be
porta
ble
and
acce
ssib
le o
fflin
e w
hile
kee
ping
sen
sitiv
e da
ta s
ecur
e. W
ith th
e U
FD
refe
renc
e de
sign
from
Cyp
ress
and
UP
EK
, man
ufac
ture
rs o
f UFD
s an
d pe
riphe
ral
supp
liers
can
add
con
veni
ent b
iom
etric
sec
urity
that
allo
ws
only
thos
e w
ho re
gist
er th
eir
finge
rprin
ts to
acc
ess
data
on
the
driv
es w
ith th
e sw
ipe
of a
fing
er. T
he re
fere
nce
desi
gn
feat
ures
har
dwar
e-ba
sed
auth
entic
atio
n fo
r the
stro
nges
t dat
a an
d pr
ivac
y pr
otec
tion
avai
labl
e, w
hile
ena
blin
g co
mpl
ete
mob
ility
acr
oss
devi
ces
and
mul
tiple
ope
ratin
g sy
stem
s.
Bus
ines
s W
ire
AT&
T, In
c.
(NYS
E:T
) A
T&T
Intro
duce
s U
-ve
rse
in B
ay A
rea
AT&
T In
c. a
nnou
nced
the
initi
al la
unch
of A
T&T
U-v
erse
(SM
), w
hich
util
izes
AT&
T's
fiber
-ric
h ne
twor
k th
at e
xten
ds o
ptic
al c
onne
ctio
ns d
eepe
r int
o ne
ighb
orho
ods.
U-v
erse
se
rvic
es a
re in
itial
ly a
vaila
ble
in li
mite
d ar
eas
acro
ss th
e S
an F
ranc
isco
-Oak
land
-Fr
emon
t met
ropo
litan
sta
tistic
al a
rea
(MS
A),
incl
udin
g pa
rts o
f the
citi
es o
f San
Ram
on
and
Dan
ville
. AT&
T pl
ans
to e
xpan
d to
add
ition
al a
reas
on
an o
ngoi
ng b
asis
. AT&
T U
-ve
rse
offe
rs c
usto
mer
s a
com
bina
tion
of n
ext-g
ener
atio
n di
gita
l tel
evis
ion-
incl
udin
g m
ore
than
25
Hig
h D
efin
ition
(HD
) cha
nnel
s-an
d hi
gh s
peed
Inte
rnet
acc
ess.
The
aw
ard-
win
ning
AT&
T U
-ver
se T
V in
clud
es c
uttin
g-ed
ge fe
atur
es th
at a
re u
nmat
ched
in th
e m
arke
t, w
hile
the
new
U-v
erse
ena
bled
AT&
T Ya
hoo!
(R) H
igh
Spe
ed In
tern
et b
uild
s on
A
T&T'
s po
sitio
n as
the
natio
n's
lead
ing
prov
ider
of b
road
band
DS
L.
PR
New
swire
Inte
rnat
iona
lFl
avor
s &
Fr
agra
nces
Inc.
(N
YSE
:IFF)
Inte
rnat
iona
l Fla
vors
&
Frag
ranc
es A
nnou
nces
O
peni
ng o
f Sha
ngha
i C
reat
ive
Cen
ter
Inte
rnat
iona
l Fla
vors
& F
ragr
ance
s In
c. a
nnou
nced
the
open
ing
of it
s ne
w S
hang
hai
Cre
ativ
e C
ente
r. Th
e ce
nter
incl
udes
cre
atio
n an
d ap
plic
atio
n fa
cilit
ies,
as
wel
l as
a co
nsum
er in
sigh
ts c
ente
r tha
t allo
ws
IFF
and
its c
usto
mer
s to
gai
n de
eper
un
ders
tand
ing
of th
e ra
pidl
y-ch
angi
ng n
eeds
and
pre
fere
nces
of t
his
dyna
mic
regi
on.
Loca
ted
at T
he N
orth
in th
e P
utuo
dis
trict
of S
hang
hai,
the
cent
er p
rovi
des
an a
ttrac
tive
and
cust
omer
-focu
sed
envi
ronm
ent f
or fl
avor
and
frag
ranc
e cr
eatio
n. T
he fa
cilit
y st
reng
then
s IF
F's
pres
ence
in G
reat
er A
sia
and
is p
art o
f an
ongo
ing
glob
al p
rogr
am to
ex
pand
and
mod
erni
ze it
s fa
cilit
ies
to c
apita
lize
on s
trate
gic
grow
th o
ppor
tuni
ties.
Bus
ines
s W
ire
99 C
ents
Onl
y S
tore
s(N
YSE
:ND
N)
99 C
ents
Onl
y S
tore
s A
nnou
nces
Ope
ning
of
Its N
ewes
t 99c
Onl
y S
tore
s on
Feb
ruar
y 12
th, 2
009,
in G
ilber
t, A
rizon
a
99 C
ents
Onl
y S
tore
s an
noun
ced
open
ing
of it
s ne
wes
t sto
re lo
cate
d in
Gilb
ert,
Ariz
ona
on T
hurs
day,
Feb
ruar
y 12
th, 2
009
at 8
am. T
he A
pple
4G
B iP
od N
ano
will
be
sold
for
only
99
cent
s to
the
first
9 c
usto
mer
s an
d th
e ne
xt 9
9 cu
stom
ers
can
purc
hase
a s
coot
er
for o
nly
99 c
ents
at t
he G
ilber
t 99c
Onl
y S
tore
s(R
). Th
e ne
w s
tore
is 1
8,61
5 sq
uare
feet
an
d lo
cate
d at
750
N. G
ilber
t Roa
d on
the
sout
hwes
t cor
ner o
f Gua
dalu
pe R
oad.
Thi
s w
ill b
e th
e fir
st 9
9c O
nly
Sto
res(
R) i
n th
e G
ilber
t com
mun
ity.
Bus
ines
s W
ire
28
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30
time
Endqu
artert1
Beginn
ingqu
artert
Press
Release
Endqu
artert
Beginn
ingqu
artert+1
FEitP
OST
DISP i
tPOST
FEitP
REa
DISP i
tPRE
1+1
+2First
Analyst
Forecast
FirstA
nalyst
ForecastWindo
w(ABSRE
T_FA
F)
TotalEvent
Windo
w(ABSRE
T_PR
2FAF)
FIGURE
1Timelineof
Even
tsan
dMeasuremen
tofD
epen
dent
Variables
31
ERRO
R=(FE itPOST
FEitP
RE)/P it1
forecasterrorbasedon
analysts’forecastsmadeor
review
edinthepe
riod
followingthe
pressreleaselessforecasterrorat
thebe
ginn
ingof
quartert,de
flatedby
priceat
the
beginn
ingof
quartert;
DISP=(DISP i
tPOST
DISP itPRE)/P it1
dispersion
basedon
analysts’forecastsmadeor
review
edinthepe
riod
followingthe
pressreleaselessdispersion
atthebe
ginn
ingof
quartert,de
flatedby
priceat
the
beginn
ingof
quartert.
aFE
ith=
|Fith–E it|,the
absolute
valueof
theconsen
susanalystforecaste
rror
forfirm
ifor
annu
alta
thorizon
h(i.e.,
PRE,PO
ST).F ithisthemeanconsen
susanalystforecastfor
firm
ifor
annu
alta
thorizon
has
calculated
using
I/B/E/Sadjusted
detailfile.E itistheactualearnings
forannu
alta
srepo
rted
ontheI/B/E/Sadjusted
actuals
file;and
DISP ith
=standard
deviationof
analysts’forecastsof
firm
i’searnings
forannu
alta
thorizon
h(i.e.,PRE
,POST).
Thus,
FEitP
RE=
consen
susforecasterrorfor
firm
ifor
annu
alte
arningsat
thebe
ginn
ingof
thequ
arter;
DISP iPR
E=
standard
deviationof
analysts’forecastsof
firm
i’searnings
forannu
alta
tthe
beginn
ingof
thequ
arter;
FEitP
OST
=consen
susforecasterrorfor
firm
ifor
annu
alte
arningsas
ofthepe
riod
followingthepressreleaseto
the
endof
thequ
arter;
DISP iPO
ST=
standard
deviationof
analysts’forecastsof
firm
i’sannu
alearnings
forannu
alta
sof
thepe
riod
following
thepressreleaseto
theen
dof
thequ
arter;
ABSRE
T_PR
2FAF
=Absolutevalueof
returnsbe
ginn
ingthetradingdaypriorto
thepressreleaseandextend
ingthroughthe
second
tradingafterthefirstanalystforecast.
ABSRE
T_FA
F=
Absolutevalueof
returnsbe
ginn
ingthedayof
thefirstanalystforecasta
ndextend
ingthroughthesecond
tradingafterthe
firstanalystforecast.
32
Table 1Sample Selection
Panel A: Analyst Reaction Analyses
Quarters with actual earnings available on I/B/E/S (2002 2008) 74,667Firm data not available on CRSP (13,296)Have Post forecast revision and pre forecast, and returns around press releaseand first analyst forecast revision. 61,381Less:a) Independent variables not available (8,875)Sample with all available information: 52,506
Less:a) Top and bottom 1 percent of dependent variable observations (1,734)
Analsyt Reaction Sample: 50,772Firm quarters with a BE or PA: 11,701Within firm tests: all quarters for firms with at least one BE or PA: 40,334
Panel B: Forecast Activity and Market Reaction Analyses
Single Dow Jones /PR Newswire/ 8k Press Releases 53,682Less: Data not available to measure post forecast revision and pre forecast,and returns around press release and first analyst forecast revision. (22,308)Have Post forecast revision and pre forecast, and returns around press releaseand first analyst forecast revision. 31,374Less:a) Observations that have an earnings announcement 3 days prior to the
press release or prior to the first analyst forecast.(9,756)
b) Observations for firms that issue another press release prior to the firstanalyst forecast.
(9,227)
Forecast Activity Sample: 12,391Less:a) Observations for which the first analyst forecast occurs on the day of or the
day after the press release.(4,617)
Market Reaction Full Sample: 7,774Less:a) Observations with revisions within returns window and first revision made
beyond 6 trading days after press release(3,618)
Market Reaction Test Sample: 4,156
33
Table 2Sample Composition
Panel A: Analyst Forecast Sample Composition by Year and Type a
All DPR=0 DPR=1Year N N N2002 5,686 3,583 2,1032003 8,176 5,356 2,8202004 8,758 4,510 4,2482005 9,257 4,730 4,5272006 10,253 5,579 4,6742007 8,578 4,690 3,8882008 64 15 49
Total 50,772 28,463 22,309
Panel B: Market Reaction Sample Composition by Year and Type
All BE PAYear N N N2002 1,038 87 9512003 1,202 229 9732004 1,276 289 9872005 1,470 360 11102006 1,475 355 11202007 1,203 343 8602008 110 31 79Total 7,774 1,694 6,080
34
Table 2
Panel C: Market Reaction Sample Composition by Sector Name and Type
All BE PASector b N N NBASIC INDUSTRIES 205 111 94CAPITAL GOODS 301 85 216CONSUMER DURABLES 174 52 122CONSUMER NON DURABLES 265 61 204CONSUMER SERVICES 1,107 569 538ENERGY 227 79 148FINANCE 687 278 409HEALTH CARE 1,474 122 1,352MISCELLANEOUS 79 78 1PUBLIC UTILITIES 106 0 106TECHNOLOGY 3,010 209 2,801TRANSPORTATION 139 50 89TOTAL 7,774 1,694 6,080
Table 2 notes.a Variable definitions:DPR = 1 if firm fiscal quarter contains a press release, 0 otherwise.BE = 1 if business expansion press release, 0 otherwise.PA = 1 if product announcement press release, 0 otherwise.b Sector as defined by IBES.
35
Table 3Frequency of Analyst Forecasts by Type
(N=12,391)
Days 5 4 3 2 1 0 1 2 3 4 5BE a 0.134 0.133 0.134 0.121 0.124 0.192 0.190 0.169 0.156 0.138 0.1261 b 0.068 0.066 0.045 0.032 0.014 0.0022 0.072 0.070 0.049 0.036 0.017 0.0053 0.058 0.056 0.035 0.023 0.004 0.0084 0.059 0.058 0.036 0.024 0.005 0.0075 0.058 0.056 0.035 0.022 0.004 0.009
PA 0.128 0.125 0.129 0.121 0.119 0.221 0.209 0.159 0.144 0.141 0.1401 0.102 0.090 0.040 0.026 0.022 0.0212 0.100 0.088 0.038 0.023 0.020 0.0183 0.092 0.079 0.030 0.015 0.012 0.0104 0.096 0.083 0.034 0.019 0.016 0.0145 0.094 0.081 0.031 0.017 0.014 0.012
Table 3 notes:
* denotes p < .10; ** denotes p < .05; *** denotes p < .01.
a Each cell in the first row of each press release type contains the proportion of firms that had atleast one analyst forecast on this trading day relative to the press release (day 0). Each cell inrows two through six contains the difference between the column trading day and row tradingday.b Bold if probability is less than .05 based on the test of two proportions.
36
Table 4Descriptive Statistics for Error and Dispersion Tests
Panel A: Sample Means
All DPR=0 DPR=1ERROR a 0.0053 *** 0.0057 *** 0.0048 ***DISP 0.0006 *** 0.0006 *** 0.0007 ***DPR 0.4394 *** 0.0000 1.0000SIZE 13.9349 *** 13.7268 *** 14.2003 ***SURPt 0.0045 *** 0.0040 *** 0.0051 ***SURPt 1 0.0059 *** 0.0067 *** 0.0050 ***AGE 104.0367 *** 108.8248 *** 97.9278 ***
FEpre 0.5736 *** 0.8004 *** 0.2842 ***MIG 0.3165 *** 0.0168 *** 0.6988 ***QTR1 0.2335 *** 0.2280 *** 0.2404 ***QTR2 0.2405 *** 0.2415 *** 0.2394 ***
QTR3 0.2526 *** 0.2533 *** 0.2517 ***
QTR4 0.2734 *** 0.2772 *** 0.2685 ***
N 50,772 28,463 22,309
***significant at <0.01 ** significant at < 0.05 * significant at < 0.10
37
Table4
Pane
lB:C
orrelation
s(N=50,772)
PearsonAbo
ve(Spe
arman
Below)
ERRO
RDISP
DPR
SIZE
SURP
tSU
RPt1
AGE
FEPR
EMIG
QTR
1QTR
2QTR
3QTR
4ER
ROR
1.00
0***
0.04
0***
0.03
1***
0.20
2***
0.00
60.00
9**
0.00
9**
0.01
5***
0.03
5***
0.00
40.00
7*
0.00
10.01
0**
DISP
0.00
11.00
0***
0.01
1**
0.04
0***
0.00
10.00
00.04
4***
0.00
40.00
70.01
8***
0.00
40.01
1**
0.00
3DPR
0.01
4***
0.06
8***
1.00
0***
0.13
8***
0.00
30.00
70.24
4***
0.01
7***
0.72
8***
0.01
5***
0.00
20.00
20.01
0**
SIZE
0.14
9***
0.05
1***
0.12
7***
1.00
0***
0.01
4***
0.03
8***
0.01
9***
0.04
2***
0.05
9***
0.02
2***
0.02
0***
0.00
8*
0.00
6SU
RPt
0.08
2***
0.02
5***
0.01
9***
0.04
4***
1.00
0***
0.33
5***
0.00
60.00
10.00
40.00
40.00
10.00
00.00
4SU
RPt1
0.05
8***
0.00
60.02
5***
0.04
5***
0.50
6***
1.00
0***
0.01
0**
0.01
3***
0.00
50.00
10.00
10.00
70.00
6AGE
0.01
5***
0.07
3***
0.25
4***
0.01
8***
0.00
7*
0.01
3***
1.00
0***
0.00
20.23
8***
0.07
2***
0.11
8***
0.03
5***
0.01
0**
FEPR
E0.53
5***
0.02
2***
0.05
8***
0.01
7***
0.08
9***
0.03
6***
0.02
5***
1.00
0***
0.01
3***
0.00
7*
0.00
30.00
00.01
0**
MIG
0.01
2***
0.04
6***
0.72
8***
0.06
5***
0.02
0***
0.03
0***
0.24
6***
0.04
0***
1.00
0***
0.02
5***
0.01
5***
0.00
40.01
2***
QTR
10.00
7*
0.02
0***
0.01
5***
0.02
3***
0.00
9**
0.02
7***
0.08
6***
0.18
4***
0.02
5***
1.00
0***
0.31
1***
0.32
1***
0.33
9***
QTR
20.00
30.03
2***
0.00
20.01
9***
0.00
8*
0.02
8***
0.12
1***
0.09
7***
0.01
5***
0.31
1***
1.00
0***
0.32
7***
0.34
5***
QTR
30.01
1**
0.01
0**
0.00
20.00
8*
0.00
30.00
8*
0.02
6***
0.03
1***
0.00
40.32
1***
0.32
7***
1.00
0***
0.35
7***
QTR
40.00
60.02
2***
0.01
0**
0.00
40.01
4***
0.00
8*
0.00
8*
0.23
7***
0.01
2***
0.33
9***
0.34
5***
0.35
7***
1.00
0***
***significanta
t<0.01
**significant
at<0.05
*significant
at<0.10
Table4no
tes.
aVa
riablede
finition
s:ER
ROR
=(FE P
OST
FEPR
E)divide
dby
priceat
beginn
ingof
quarter.
DISP
=(DISP P
OST
DISP P
RE)d
ivided
bypriceat
beginn
ingof
quarter.
DPR
=1,iffirm
fiscalquarter
contains
apressrelease,0,ifno
t.SU
RPt
=(EPS
tEPS t
4)/p
tr,whe
reEPS=IBES
EPS,p t
4=en
ding
priceat
quartert4.
SIZE
=Naturallogof
marketvalue
ofeq
uity
defin
edas
thenu
mbe
rof
shares
outstand
ingmultip
liedby
shareprice,tw
odayprior
topressrelease.
AGE
=Average
ageof
annu
alforecastsintheeven
tquarter
lesstheaverageageof
forecastsmadeintheprepe
riod
.FE
PRE
=consen
susforecasterrorforannu
alearnings
asof
thebe
ginn
ingof
thefiscalquarter.
MIG
=1iffirm
hadmanagem
entissue
dguidance
inadditio
nto
apressreleaseinthefiscalquarter,0
othe
rwise.
QTR
1=1ifpressreleasemadeinfirstfiscalquarter,0
othe
rwise.
QTR
2=1ifpressreleasemadeinsecond
fiscalquarter,0
othe
rwise.
QTR
3=1ifpressreleasemadeinthirdfiscalquarter,0
othe
rwise.
QTR
4=1ifpressreleasemadeinfourth
fiscalquarter,0
othe
rwise.
38
Table 5Error and Dispersion Tests
Panel A: Changes in Forecast Error
Variable a Coefficient p valueIntercept 0.0312 .0001DPR 0.0008 .0001SIZE 0.0018 .0001SURPt 0.0008 .0217SURPt 1 0.0005 .3305AGE 0.0000 .0003
FEPRE 0.0000 .0001MIG 0.0015 .0001QTR1 0.0003 .0878QTR2 0.0005 .0047QTR3 0.0003 .0664Adjusted R2 = 0.0427N=50,772 firm quarters
Panel B: Changes in Forecast Dispersion
Variable a Coefficient p valueIntercept 0.0030 .0001DPR 0.0003 .0001SIZE 0.0001 .0001SURPt 0.0000 .8181SURPt 1 0.0001 .7042AGE 0.0000 .0001
DISPPRE 0.0001 .0001MIG 0.0004 .0001QTR1 0.0002 0.001QTR2 0.0000 .3325QTR3 0.0000 .3788Adjusted R2 = 0.0053N=50,772 firm quarters______________________a See table 4 for variable definitions.b The p values reported are two tailed..
39
Table 6Within Firm Error and Dispersion Tests
Panel A: Changes in Forecast Error
Variable a Coefficient p valueIntercept 0.0270 .0001DPR 0.0012 .0001SIZE 0.0017 .0001SURPt 0.0002 .5904SURPt 1 0.0018 .0093AGE 0.0000 .9175
FEPRE 0.0044 .0001MIG 0.0012 .0001QTR1 0.0006 .0003QTR2 0.0002 .2143QTR3 0.0001 .6743Adjusted R2 = 0.1217N=40,334 firm quarters
Panel B: Changes in Forecast Dispersion
Variable a Coefficient p valueIntercept 0.0041 .0001DPR 0.0003 .0001SIZE 0.0002 .0001SURPt 0.0000 .8193SURPt 1 0.0002 .3102AGE 0.0000 .0001
FEPRE 0.0067 .0001MIG 0.0002 .0001QTR1 0.0001 .3614QTR2 0.0002 .0026QTR3 0.0001 .2363Adjusted R2 = 0.1490N=34,457 firm quarters______________________a See table 4 for variable definitions.b The p values reported are two tailed..
40
Table 7Market Reaction Descriptive Statistics
Panel A: Full SampleAll BE PA MIG
AR a 0.001 0.002 0.004 *** 0.013 ***AR_faf 0.002 *** 0.004 *** 0.001 0.002|RET%| 0.315 *** 0.313 *** 0.318 *** 0.298 ***VOL% 0.322 *** 0.320 *** 0.325 *** 0.312 ***AFREV 0.002 *** 0.001 *** 0.002 * 0.007 ***ERR_chg 0.004 *** 0.001 *** 0.004 *** 0.008 ***SIZE 14.431 *** 14.736 *** 14.503 *** 13.557 ***SDAF_pre 0.100 *** 0.100 *** 0.105 *** 0.073 ***NUMAF_pre 10.9 *** 11.1 *** 11.8 *** 6.1 ***NUMAF_post 1.5 *** 1.5 *** 1.5 *** 1.8 ***TDays_af 8.0 *** 8.5 *** 8.1 *** 7.0 ***N 8,866 1,694 6,080 1,092
Panel B: Test SampleAll BE PA MIG
AR a 0.000 *** 0.003 ** 0.003 *** 0.019 **AR_faf 0.001 ** 0.002 0.001 ** 0.002|RET%| 0.412 *** 0.426 *** 0.417 *** 0.371 ***VOL% 0.414 *** 0.422 *** 0.418 *** 0.388 ***AFREV 0.004 *** 0.002 ** 0.002 0.012 ***ERR_chg 0.004 *** 0.002 *** 0.003 *** 0.010 ***SIZE 14.653 *** 15.020 *** 14.823 *** 13.373 ***SDAF_pre 0.106 *** 0.104 *** 0.112 *** 0.081 ***NUMAF_pre 12.2 *** 12.4 *** 13.4 *** 6.2 ***NUMAF_post 1.6 *** 1.5 *** 1.6 *** 2.1 ***TDays_ af 3.5 *** 3.6 *** 3.6 *** 3.1 ***N 4,844 877 3,279 688
41
Table7
Pane
lC:C
orrelation
s(N=4,844)
PearsonAbo
ve(Spe
arman
Below)
***significanta
t<0.01
**significant
at<0.05
*significant
at<0.10
AR
AR
_faf
AB
SRET
_PER
CV
OL_
PER
CA
FREV
ERR
_chg
MIG
BE
PASI
ZESD
AF_
pre
NU
MA
F_pr
eN
UM
AF_
post
TDay
s_af
AR
1.00
0**
*0.
123
***
-0.0
28**
-0.0
43**
*0.
079
***
0.04
4**
*-0
.092
***
0.01
4
0.05
7**
*0.
001
0.
025
*0.
002
-0
.034
**0.
032
**A
R_f
af0.
009
***
1.00
0**
*0.
068
***
0.03
9**
*0.
145
***
0.11
1**
*0.
000
0.
004
-0
.004
-0
.013
-0
.012
-0
.020
-0
.055
***
0.01
0
AB
SRET
_PER
C-0
.035
***
0.06
9**
*1.
000
***
0.60
7**
*-0
.004
0.
013
-0
.094
***
0.03
7**
*0.
039
***
0.06
0**
*-0
.011
0.
055
***
0.05
4**
*-0
.376
***
VO
L_PE
RC
-0.0
42**
*0.
055
***
0.60
1**
*1.
000
***
-0.0
29**
0.00
4
-0.0
81**
*0.
028
*0.
038
***
0.05
1**
*-0
.030
**0.
046
***
0.09
5**
*-0
.472
***
AFR
EV0.
124
***
0.14
9**
*0.
028
**0.
031
**1.
000
***
0.02
7*
-0.0
78**
*0.
020
0.
041
***
0.09
4**
*0.
490
***
0.04
4**
*-0
.048
***
0.02
3
ERR
_chg
0.02
9**
0.01
2
0.03
0**
0.02
3
0.05
5**
*1.
000
***
-0.0
59**
*0.
022
0.
026
*0.
093
***
-0.5
78**
*0.
052
***
-0.0
51**
*-0
.010
M
IG-0
.074
***
0.01
7
-0.0
93**
*-0
.085
***
-0.1
12**
*-0
.153
***
1.00
0**
*-0
.191
***
-0.5
89**
*-0
.264
***
-0.0
19
-0.2
84**
*0.
098
***
-0.1
30**
*B
E0.
031
**0.
004
0.
040
***
0.03
0**
0.03
4**
0.03
1**
-0.1
91**
*1.
000
***
-0.6
81**
*0.
087
***
-0.0
02
0.01
1
-0.0
33**
0.02
3
PA0.
030
**-0
.016
0.
036
**0.
039
***
0.05
5**
*0.
089
***
-0.5
89**
*-0
.681
***
1.00
0**
*0.
125
***
0.01
6
0.20
3**
*-0
.046
***
0.07
8**
*SI
ZE0.
026
*0.
008
0.
063
***
0.07
5**
*0.
142
***
0.15
5**
*-0
.265
***
0.09
3**
*0.
121
***
1.00
0**
*-0
.018
0.
686
***
0.08
4**
*0.
016
SD
AF_
pre
0.00
4
-0.0
39**
*0.
018
-0
.025
*-0
.021
-0
.045
***
-0.1
45**
*0.
035
**0.
079
***
0.13
0**
*1.
000
***
-0.0
16
0.00
0
0.01
2
NU
MA
F_pr
e0.
006
-0
.001
0.
069
***
0.06
9**
*0.
087
***
0.10
0**
*-0
.319
***
0.03
7**
0.20
8**
*0.
724
***
0.16
4**
*1.
000
***
0.15
7**
*-0
.011
N
UM
AF_
post
-0.0
55**
*-0
.026
*0.
037
***
0.07
5**
*-0
.057
***
-0.0
86**
*0.
096
***
-0.0
14
-0.0
61**
*0.
141
***
0.10
4**
*0.
184
***
1.00
0**
*-0
.055
***
TDay
s_af
0.01
1
0.00
8
-0.3
78**
*-0
.510
***
0.04
3**
*0.
026
*-0
.138
***
0.02
1
0.08
5**
*0.
023
0.
005
0.
007
-0
.080
***
1.00
0**
*
42
Table7no
tes.
aVa
riablede
finition
s:AR
=Threedaysize
adjusted
returnscentered
arou
ndthepressreleasedate.
AR_
faf
=Threedaysize
adjusted
returnsbe
ginn
ingon
thefirstanalystforecastd
ate.
|RET%|
=Absolutethreedaysize
adjusted
returnsbe
ginn
ingon
thefirstanalystforecastd
atedivide
dby
thetotalabsolute
size
adjusted
returnsbe
ginn
ingthedaypriorto
thepressreleasedate
andrunn
ingthroughtw
odays
followingthe
firstanalystforecast.
VOL%
=Tradingvolumeover
threedaype
riod
beginn
ingon
thefirstanalystforecastd
atedivide
dby
thetradingvolume
beginn
ingthedaypriorto
thepressreleasedate
andrunn
ingthroughtw
odays
followingthefirstanalystforecast.
AFREV
=Meananalysts’forecastm
adeinthreedaywindo
wbe
ginn
ingon
thedate
ofthefirstanalystforecastrevisionless
themeanof
thosesameanalystsintheprior60
tradingdaype
riod
,ifavailable,andallanalystsifno
tavailable.
ERR_
chg
=Thedifferen
cebe
tweentheanalysts’forecaste
rror
inthepreandpo
stpressreleasepe
riod
.Analysts’forecasterror
isde
fined
asthemeananalystsforecastlesstheactualvalue,scaled
bypricetw
odays
priorto
pressrelease.
SIZE
=Naturallogof
marketvalue
ofeq
uity
defin
edas
thenu
mbe
rof
shares
outstand
ingmultip
liedby
shareprice,tw
oSD
AF_pre
=Standard
deviationof
allanalystsmakingaforecastinthesixtytradingdays
priorto
thepressrelease.
NUMAF_pre
=Thenu
mbe
rof
analystsmakingaforecastinthesixtytradingdays
priorto
thepressrelease.
NUMAF_po
st=Thenu
mbe
rof
analystsmakingaforecastafterthepressreleaseinthethreedaype
riod
beginn
ingwith
thefirst
TDays_a f
=Thenu
mbe
rof
tradingdays
until
thefirstforecastrevision
.MIG
=1ifmanagem
entissue
dguidance
pressrelease,0othe
rwise.
BE=1ifbu
sine
ssexpansionpressrelease,0othe
rwise.
PA=1ifprod
ucta
nnou
ncem
entp
ressrelease,0othe
rwise.
43
Table 8Market Reaction
Panel A:
Variable a Coefficient p value Coefficient p value Coefficient p valueINTERCEPT 0.492 .0001 0.485 .0001 0.493 .0001BE 0.081 .0001 0.078 .0001PA 0.071 .0001 0.065 .0001|AFREV| 0.035 .6003 0.064 .3308 0.273 .0487|AFREV|*BE 0.255 .5131|AFREV|*PA 0.450 .0038SIZE 0.006 .0001 0.002 0.061 0.002 .0729SDAF_pre 0.003 .5216 0.006 0.27 0.010 0.06NUMAF_post 0.003 0.04 0.004 0.001 0.004 .0011TDays_af 0.048 .0001 0.051 .0001 0.051 .0001
N 4,844 4,844 4,844Adj R square 0.1456 0.1642 0.1658
Panel B:
Variable a Coefficient p value Coefficient p value Coefficient p valueINTERCEPT 0.507 .0001 0.502 .0001 0.506 .0001BE 0.060 .0001 0.057 .0001PA 0.055 .0001 0.051 .0001|AFREV| 0.086 .0634 0.108 .0187 0.120 .2119|AFREV|*BE 0.071 0.791|AFREV|*PA 0.295 0.006SIZE 0.004 .0001 0.001 .2038 0.001 .2243SDAF_pre 0.010 .0089 0.012 .0017 0.015 .0001NUMAF_post 0.005 .0001 0.006 .0001 0.006 .0001TDays_af 0.044 .0001 0.046 .0001 0.046 .0001
N 4,844 4,844 4,844Adj R square 0.2309 0.2513 0.2522______________________a See table 7 for variable definitions.b The p values reported are two tailed.