Firm Opacity, Analyst
Forecasts, and Investor Reaction
Presented by
Dr Jennifer Wu Tucker
Associate Professor
University of Florida
#2015/16-06
The views and opinions expressed in this working paper are those of the author(s) and
not necessarily those of the School of Accountancy, Singapore Management University.
Firm Opacity, Analyst Forecasts, and Investor Reaction
Senyo Tse
Department of Accounting
Mays Business School
Texas A&M University
(979) 845-3784 (office)
[email protected]
Jennifer Wu Tucker
Fisher School of Accounting
University of Florida
(352) 273-0214 (office)
[email protected]
June 2015
We thank Anwer Ahmed, Linda Bamber, Michael Clement, Artur Hugon (discussant),
Darren Roulstone, Jim Vincent, and the participants at the 2014 AAA FARS Mid-year
Meeting and the Ohio State University accounting workshop. The paper was previously
titled “The effects of operating and financial reporting opacity on analyst forecast
activity.”
Firm Opacity, Analyst Forecasts, and Investor Reaction
ABSTRACT
Abstract
Investors’ demand for information on earnings arguably increases with a firm’s operating
opacity, which analysts can unravel with sufficient effort, and the firm’s reporting
opacity, which obscures the effects of economic events on reported earnings. We
investigate analysts’ responses to opacity in the information discovery phase that
precedes an earnings announcement and in the information analysis phase that begins
with the earnings announcement. We find that analyst forecast activity increases with
operating opacity in all phases of analyst activity. Forecast activity increases with
reporting opacity in the information analysis phase, but the relation is negative or
insignificant in the remaining phases. Return responses to forecast revisions increase with
both types of opacity and are strongest in the information discovery phase. Our findings
provide insights into how financial analysts respond to investors’ information demand
and when investors most value analysts’ services.
Keywords: Opacity, financial analysts, voluntary disclosure, information discovery,
information analysis.
(JEL G14; G20; D82; D83)
1. Introduction
Analysts play an important role as information intermediaries. Extant research largely
examines analysts’ information production for the population of firms as a whole with limited
attention to opaque firms. Opaque corporate information environments can arise because a firm’s
operations are complex (“operating opacity”) or because the firm’s financial reporting quality is
low (“reporting opacity”). Such information environments pose a challenge to investors because
earnings are less predictable owing to a lack of information about the firm’s activities in the case
of opaque operations or because the implications of corporate transactions for reported financial
performance are difficult to discern in the case of opaque reporting. Accordingly, investors’
demand for financial analysts’ services may increase with a firm’s opacity. Prior research finds
that analyst following increases with opacity, suggesting that analysts respond to investors’
information demand by following opaque firms (Bhushan 1989; Barth, Kasznik, and McNichols
2001; Lehavy, Li, and Merkley 2011; Lobo, Song, and Stanford 2012). Simply following opaque
firms does not guarantee that analysts actively issue forecasts or that investors value these
forecasts. In this study we examine analysts’ forecast activity after they decide to cover opaque
firms and examine return responses to analyst forecasts. We focus on the association of forecast
activity and usefulness with operating and reporting opacity across analyst information
production phases.
Analysts’ responses to investors’ demand for information about opaque firms may vary
with the type of opacity. Operating opacity arises because the scope and complexity of a firm’s
operations increase market participants’ difficulty in obtaining contemporaneous information
about its transactions. In principle, with sufficient effort analysts can obtain information about
major transactions in operationally opaque firms. In contrast, reporting opacity arises because the
1
relation between a given set of transactions and the resulting financial reports is weak. Even
analysts who are aware of the company’s transactions must still rely on the firm to determine the
effects of the transactions on financial reports.
We expect analyst forecast activity and its usefulness for opaque firms to vary across
analyst information production phases. The literature has just begun to distinguish among analyst
forecasts in distinct information production phases (Chen, Cheng, and Lo 2010; Keskek, Tse,
and Tucker 2014). In the information discovery phase that precedes an earnings announcement,
operating opacity increases the amount of new information that analysts can potentially discover
about a firm. We therefore expect analysts to respond to investors’ demand by increasing their
forecast activity as operating opacity increases. In contrast, we expect reporting opacity to be
associated with lower analyst activity in the information discovery phase because analysts may
prefer to await the firm’s earnings announcement in light of the uncertain relation between
economic events and financial reports. The information analysis phase begins with the earnings
announcement, when even opaque firms typically present market participants with a substantial
amount of new information. We expect analyst forecast activity in this phase to increase with
both operating and reporting opacity. Furthermore, we evaluate analysts’ success in meeting
investors’ demand for information about opaque firms by examining return responses to analyst
forecasts. We expect that investors value analyst forecasts for opaque firms more than they do
for other firms because the information asymmetry at opaque firms is higher than that at other
firms.
We base our empirical tests on individual analyst estimates of fiscal-year earnings in
1999-2008. We measure operating opacity using the operating complexity score introduced by
Chen et al. (2010)—a composite of firm size, the market-to-book ratio, and research and
2
development intensity. We measure financial reporting opacity using the standard deviation of a
firm’s residuals in a regression of accounting accruals on cash flows as well as other
fundamental accounting signals (Dechow and Dichev 2002; McNichols 2002). The standard
deviation varies inversely with the historical association between accruals and future cash flows,
and hence the mapping of the firm’s transactions to reported financial performance. Following
Chen et al. (2010) and Keskek et al. (2014), we designate the earnings announcement day for the
prior fiscal year or interim current-year quarter as Day 0 and identify the information discovery
phase as Trading Days -30 to -1, the information analysis phase as Trading Days 0 to 4, and the
post-analysis phase as Trading Days 5 to 29. We focus on the information discovery and analysis
phases and include the post-analysis phase for completeness. Nearly half of analyst earnings
forecasts occur on Days 0 and 1 (Keskek et al., Table 1) and we separate the information analysis
phase into “early information analysis” (Days 0 and 1) and “late information analysis” (Days 2 to
4) to gain further insights. We measure analyst forecast activity for a firm-year (comprised of
four 60-trading-day cycles) by the number of annual earnings forecasts issued during the fiscal
year and measure the usefulness of a forecast revision using the intraday stock return in the two
hours after the revision.
As we predict, we find that forecast activity in the information discovery phase increases
with operating opacity, but we find no relation with reporting opacity. The latter finding suggests
that analysts’ information discovery is insensitive to noise in reported earnings. As we predict,
analyst forecast activity early in the information analysis phase is positively associated with both
operating and reporting opacity, indicating that analysts promptly evaluate complex firms’
earnings announcements. Thus corporate disclosures at the earnings announcement date appear
to increase analyst activity relative to the level in the discovery period. Two days after the
3
earnings announcement, analyst forecast activity continues to be positively associated with report
opacity, but becomes negatively associated with reporting opacity. Overall, these results indicate
that even though prior research finds that analysts are attracted to opaque firms, analyst forecast
activity depends on the type of opacity and varies considerably across analyst information
production phases around an earnings announcement.
The level of analyst forecast activity for a firm is affected by analysts following and by
the number of forecasts each analyst issues. To shed light on our primary findings on the
association between firm opacity and analyst forecast activity, we analyze analyst following, a
firm-year measure, and forecast intensity, the number of forecasts each analyst issues during the
fiscal year and within each information analysis phase (“forecast intensity”). We find that analyst
following increases with both operating and reporting opacity, with the latter finding consistent
with Lobo et al. (2012). Forecast intensity is negatively related to both types of opacity in the
information discovery phase, increases with both in the early information analysis phase, and
declines with both types of opacity afterwards. The combined results from analyst following and
forecast intensity suggest that the increased forecast activity for operationally opaque firms in the
information discovery phase is less than we would expect based on analyst following, but that
the increase in activity right after an earnings announcement exceeds the level suggested by the
positive association between analyst following and operating opacity. The results for reporting
opacity follow a similar but more subdued pattern. The combined results from analyst following
and forecast intensity suggest that firms whose reporting is opaque attract more analysts than
other firms, but that those analysts who are attracted generally issue fewer forecasts than their
numbers would suggest they would, except immediately around earnings announcements, when
their forecast activity is more intense than analyst following would suggest. Thus, analysts vary
4
their activity predictably across their information production phases for both operating and
reporting opacity.
Analysts’ forecast activity does not necessarily correspond with their success in meeting
investors’ information demand, so we use return responses to forecast revisions to measure the
usefulness of analyst forecast revisions. We find that return responses increase with a firm’s
operating and reporting opacity in both the information discovery and analysis phases,
suggesting that analysts provide useful information in challenging information environments.
Moreover, the incremental return responses to opaque firms’ forecasts are highest in the
information discovery phase, suggesting that forecasts in this phase generate stronger return
responses than forecasts at other times. Thus, the usefulness of analyst forecasts to investors
varies predictably with opacity across analyst information production phases.
Our study contributes to the analyst forecast literature by documenting opacity-related
differences in analyst forecast activity and return responses to the forecasts. While prior research
examines the relation between analyst following and opacity, we find that analysts’ forecast
activity depends on the source of opacity and varies systematically across analyst information
production phases around an earnings announcement. Moreover, we find that analyst forecast
revisions generate stronger return responses to a given magnitude of forecast revision for opaque
firms than for other firms. This response difference is stronger in the information discovery
phase than at other times even though analysts are more active in the information analysis phase
than in other phases of their information production. Thus, although analysts are most active
immediately around an earnings announcement, investors appear to value analysts’ efforts to
discover information for opaque firms more highly than their efforts to analyze these firms’
disclosures. The above findings are surprising because one would expect analysts to concentrate
5
their forecast activity in the information discovery period, where it has the highest returns
impact. Overall, our findings provide insights into how financial analysts respond to investors’
information demand and when investors most value analysts’ services.
The rest of this paper is organized as follows. We discuss prior research in Section 2 and
develop our hypotheses in Section 3. We describe our sample selection procedures in Section 4,
define the opacity and forecast activity measures in Section 5, and present our analyses in
Sections 6 and 7. Section 8 concludes.
2. Prior Research
Our study is related to research on the association between firms’ operating and reporting
opacity and analyst following. Bhushan (1989) argues that the demand for analyst services
increases with firm size and finds that analyst following is indeed increasing in this variable.1
Barth et al. (2001) find that analyst coverage increases with a firm’s intangible assets, consistent
with the idea that intangible assets increase firms’ operating opacity and present analysts with
opportunities to assist investors. Other studies investigate the effects of reporting or disclosure
opacity on analyst following. Lobo et al. (2012) find that analyst coverage increases as accruals
quality declines. Lehavy et al. (2011) find that analyst following increases as the qualitative
disclosures in annual reports become less readable, consistent with their prediction that
investors’ demand for analyst services increases as a firm’s communication becomes less
informative. We extend this line of research by examining whether firm opacity is associated
with increased analyst forecast activity once analysts decide to follow a firm.
Our study also extends research on the characteristics of analyst forecasts and their
usefulness to investors. Gu and Wang (2005) find that analysts’ forecast errors increase with the
1
Bhushan (1989) argues that investors’ demand for analyst services in large firms arises primarily because the
profitability of trading on a given price discrepancy increases with firm size.
6
level of intangible assets, consistent with the idea that intangible assets complicate analysts’
forecasting task. Barron, Byard, Kile, and Riedl (2002) find that the private information
component of analyst forecasts increases with intangible assets, suggesting that analyst forecasts
are more valuable for these firms. Other researchers examine the effects of analyst forecasts on
information asymmetry and trading costs (Chung, McInish, Wood, and Wyhowski 1995; Ahn,
Cai, Hamao, and Ho 2005; Bhattacharya, Desai, and Venkataraman 2013). The above studies
suggest that analysts can increase the value of their information to opaque firms’ shareholders by
uncovering information despite the informational challenges they face. We extend this line of
research by directly examining return responses to analyst forecast revisions for opaque firms.
A third stream of related research compares analysts’ information discovery and
information analysis roles (Ivkovic and Jegadeesh 2004; Chen et al. 2010; Livnat and Zhang
2012). Chen et al. analyze the weeks surrounding earnings announcements and conclude that
analysts focus on information discovery before the earnings announcement and on information
analysis once earnings are announced. Furthermore, they conclude that analysts’ information
discovery role is more important than their information analysis role and that analysts’ role is
more important when operations are opaque.2 Ivkovic and Jegadeesh (2004) conclude that
analysts’ information discovery is more useful to investors than their information analysis,
whereas Livnat and Zhang (2012) conclude the opposite. We extend this line of research by
distinguishing between operating and reporting sources of opacity and examining how analysts
meet investors’ demand for information discovery and analysis for opaque firms.
3. Hypotheses
2
They find that the positive association between the size-adjusted absolute stock returns in a post-earningsannouncement week of any analyst forecast activity and the absolute stock returns at the most recent earnings
announcement event increases with operating opacity. Thus they conclude that analysts’ interpretation (analysis)
role is more important for opaque firms than for other firms.
7
Our first hypothesis examines analyst forecast activity in the information discovery
phase. Investors’ demand for information is likely to increase with both operating and reporting
opacity, but we expect analysts to respond differently to the two types of opacity. In principle,
analysts can discover information about operationally opaque firms if they exert sufficient effort,
so we expect them to respond to investors’ demand by increasing their forecast activity in the
information discovery phase as operating opacity increases. In contrast, reporting opacity
reduces analysts’ ability to discern the financial reporting effects of a given set of economic
events and increases analysts’ reliance on management for accounting information. We therefore
expect analysts to reduce their forecast activity in the information discovery phase as reporting
opacity increases. We test the following hypotheses:
H1a: In the information discovery phase, analyst forecast activity increases with a
firm’s operating opacity.
H1b: In the information discovery phase, analyst forecast activity decreases with a
firm’s reporting opacity.
In the information analysis phase, firms provide market participants with a large amount
of operating and financial information. Investors are likely to demand additional insights into the
implications of this information for future performance as operating or reporting opacity
increases. The ample disclosure at the earnings announcement relaxes the constraint on the
supply of information available to analysts; therefore, we expect analysts to increase their
forecast activity in this phase as operating opacity and reporting opacity increase. This leads to
our next hypotheses:
H2a: In the information analysis phase, analyst forecast activity increases with a
firm’s operating opacity.
H2b: In the information analysis phase, analyst forecast activity increases with a
firm’s reporting opacity.
8
Analyst forecasts are likely to be more informative in opaque information environments
than in other environments because of the higher information asymmetry for opaque firms. Thus,
we expect analyst forecasts for opaque firms to be more valuable to investors than forecasts
would be for other firms. We predict stronger return responses to analyst forecast revisions for
opaque firms than for other firms:
H3a: Return responses to analyst forecast revisions increase with a firm’s operating
opacity.
H3b: Return responses to analyst forecast revisions increase with a firm’s reporting
opacity.
4. Sample Selection
We select our sample from firms whose fiscal years end between 1999 and 2008. We
begin the sample period in 1999 because the I/B/E/S time-of-day stamps for quarterly earnings
announcements that we require for the returns tests are incomplete before 1999. To be included
in the sample for a given year, we require that firms (1) have earnings announcement dates for
the preceding fiscal year (t-1) and interim quarters of the current year (t) in IBES, (2) maintain
the same fiscal-year-end month during the year, (3) announce current- and prior-year earnings
within 90 days after the respective fiscal year ends, and (4) have realized earnings per share for
the preceding fiscal year and interim quarters of the current year available in IBES. We collect
from IBES individual analyst forecasts for Year t’s earnings issued during the fiscal year and
exclude forecasts with analyst code of “0,” which we classify as incomplete data because IBES
uses this code for unidentifiable individual analysts. We require at least one analyst for each
firm-year in the sample.
Analysts estimate fiscal-year earnings throughout the fiscal year, during which firms
typically have four earnings announcement events—announcements for the previous year and
9
the three interim quarters of the current year. We label the announcement date as Day 0 and
retain forecasts issued within 30 trading days before and 29 trading days after each
announcement.3 Following Chen et al. (2010) and Keskek et al. (2014), we label the 30 trading
days before an earnings announcement, Day -30 to Day -1, as the “information discovery phase,”
the five trading days beginning with the earnings announcement, Day 0 to Day 4, as the
“information analysis phase,” and the 25 trading days afterwards, Day 5 to Day 29, as the “postanalysis phase.” To gain further insight, we divide the information analysis phase into “early”
and “late” information analysis periods, consisting of the first two trading days (Day 0 and Day
1) and the next three trading days (Day 2 to Day 4), respectively. Thus, a firm-year has four
analyst information production cycles, each lasting for 60 trading days. We measure analyst
forecast activity for each information production phase in a year over the four cycles and pool
the observations in all the phases to measure the overall activity for a given firm-year. Finally,
we require that our sample firms have sufficient data in Compustat to calculate the operating and
reporting opacity measures, which we define in Section 5. These procedures yield a sample of
10,011 firm-years from 2,391 unique firms.
We use the TAQ database in our market response tests and measure intra-day returns,
Return, in the two hours following an analyst’s forecast revision or in the first two trading hours
on the next trading day if the revision is made after the stock market closes. We eliminate
forecast revisions that occur within two hours of an earnings announcement to avoid the earnings
announcement effect and thus eliminate some forecast observations in the early information
analysis period. Our returns sample has 334,308 forecasts with 95,581 in the information
discovery phase, 150,815 in the early information analysis period, 34,749 in the late information
3
There are 252 trading days in a typical year and an average of 62 trading days between two quarterly earnings
announcements. We eliminate the very small set of forecasts that belong to two adjacent 60-trading-day windows or
that do not belong to any 60-trading-day window.
10
analysis period, and 53,163 in the post-analysis phase. The concentration of forecasts in the first
two days of the information analysis period suggests that analysts typically complete information
analysis soon after firms announce earnings. Analyst forecast frequency in the information
discovery phase is 66% higher than that in the post-analysis phase even though the information
discovery phase is only 20% longer than the post-analysis phase, indicating that analysts are
much more active in the information discovery phase than in the post-analysis phase.
5. Opacity and Analyst Forecast Activity Measures
5.1 Opacity
We measure operating opacity following Chen et al. (2010), who conjecture that
operating opacity increases with a firm’s scale, growth opportunities, and research and
development (R&D) intensity—properties which they argue increase the complexity of the
firm’s operations.4 They construct a complexity index based on prior-year financial statement
data. They measure the scale of operations using the firm’s total assets and measure growth
opportunities using the market-to-book ratio (MB), and then transform these variables into
ordinal values by assigning the values of 1, 0.5, and 0 to observations in the top, middle, and
bottom thirds of the respective distributions. They measure the intensity of R&D, RD, using the
level of R&D expenditure scaled by net sales. They assign 1 to firms whose R&D intensity is in
the top third of the distribution and 0 to other firms, including those without data on R&D.
Finally, they sum the ordinal values to derive a complexity index that ranges from 0 to 3 in
4
Chen et al. argue that R&D investments increase complexity because they are typically firm-specific and have no
liquid markets. Other research suggests that analysts play an important role in uncovering information on high-R&D
firms. Specifically, Cheng (2005) finds that the incremental contribution of analyst forecasts to explaining future
earnings and market-to-book value ratios is higher for firms with high R&D and concludes that analysts provide
timely and contextual information for these firms that is not readily available elsewhere.
11
discrete intervals of 0.5. We refer to this operating complexity index as Operating Opacity.5
Panel A of Table 1 shows that the raw operating opacity measure in our sample is quite evenly
distributed with the first quartile of 1, mean of 1.34, median of 1.5, and the third quartile of 2.
We measure reporting opacity using the earnings quality metric introduced by Dechow
and Dichev (2002) and modified by McNichols (2002), who incorporates the fundamental
signals suggested by Jones (1991). We estimate Equation (1), where i denotes a firm and t
denotes a fiscal year.
CFOit 1
CFOit
CFOit
WC it
1
 0
 1
 2
 3
Assets it
Assets it
Assets it
Assets it
Assets it
PPE it
Revenue
 4
 5
  it ,
Assets it
Assets it
(1)
where
WCit
Revenueit
= change in working capital, or total current accruals, measured as the sum of the
increases in accounts receivable, inventory, and other assets and the decreases
in accounts payable and taxes payable, all from the statement of cash flows;6
= cash flow from operations, net of the cash flow from extraordinary items,
Compustat items OANCF and XIDOC, respectively;
= change in revenues, Compustat item SALE;
PPEit
Assetsit
= gross property, plant and equipment, Compustat item PPEGT; and
= average total assets, Compustat item AT, for Years t and t - 1.
CFOit
We estimate the model each year for each of the 48 Fama-French (1997) industry groups
to ensure that we compare firms in similar business or economic environments. We require an
industry group to have at least 15 observations in a given year. The model estimates the extent to
5
To ensure that our results are not sensitive to the precise construction of the operating complexity measure, we
define a complexity count variable that increases by one for each component of the Chen et al. complexity measure
(i.e., firm size, market-to-book ratio, and sales-deflated R&D expenditure) that is above the median for that
component. Thus each observation has a complexity count of 0, 1, 2, or 3. We replace the operating complexity
measure with the complexity count variable and find that our results are essentially unchanged. We conclude that
our results are insensitive to the precise measurement of operating complexity.
6
The corresponding Compustat items are RECCH, the decrease in accounts receivable; INVCH, the decrease in
inventory; APALCH, the increase in accounts payable and accrued liabilities; TXACH, the increase in accrued
income taxes; and AOLOCH, the net change in other assets and liabilities.
12
which the firm’s accruals are explained by lagged, current, and next-period realized cash flows
and by changes in revenue and the level of property, plant, and equipment. Conceptually, highquality accruals should have a stable and predictable relation with cash flows, while low-quality
accruals would result in large deviations of accruals from the underlying relation with cash flows
captured by the model. By construction, the residual from this model represents accruals that do
not correspond to cash flows over the three-year span and are thus less likely to be predictive of
future cash flows. The standard deviation of a firm’s residuals from the preceding five years thus
represents the quality of the firm’s total current accruals. We require a minimum of four years of
residuals for this calculation. Large standard deviations correspond to low accruals quality. We
refer to this measure as Reporting Opacity; a higher value corresponds to more opaque reporting
environments.
Panel A of Table 1 reports the raw reporting opacity measure for our sample. We observe
substantial variation in reporting opacity across our sample firms. The measure ranges from 0 to
0.260, with the three quartiles being 0.016, 0.027, and 0.045. The increments of quartiles are
quite even, suggesting that the majority of the observations are well behaved. The maximum is
much larger than the third quartile and the mean is substantially larger than the median,
suggesting the existence of large outliers.
Following Clement and Tse (2005), we transform the raw operating opacity and reporting
opacity measures to be within 0 and 1. We divide the difference between the value for each
observation and the minimum value of the sample by the difference between the maximum and
minimum values of the sample for each raw opacity measure. This scalar adjustment preserves
the variables’ distributional properties within our sample and enables us to interpret the
regression coefficients as the effects on the dependent variable when the respective opacity
13
measures increase from the lowest to the highest values. The transformation facilitates
comparisons of the coefficients on Operating Opacity and Reporting Opacity within a regression
model as well as across analyst information production phases. We refer to the transformed
variables as standardized opacity measures.
5.2 Forecast activity
We measure Forecast Activity for each firm-year as the number of analyst forecasts of
annual earnings for the firm issued during the fiscal year. We also measure Forecast Activity for
each information production phase (i.e., the information discovery, early information analysis,
late information analysis, and post-analysis phases) but for convenience continue to use the same
label.7 For example, Forecast Activity in the information discovery phase of a given firm-year is
the number of analyst forecasts of annual earnings issued during the information discovery phase
of the four analyst information production cycles (with each cycle centered around an earnings
announcement event) in that year.
Panel A of Table 1 reports that the mean Forecast Activity for a fiscal year is 49.7. To
help understand this statistic, we provide the statistics of analyst following, which is the number
of analysts who issued a forecast of annual earnings during the fiscal year. The mean analyst
following in our sample is 13.2 and the median is 11. Thus, the mean of Forecast Activity of 49.7
implies that, on average, each analyst in our sample issues three to four forecasts during the year
for each firm that he/she covers. Comparing Forecast Activity across analyst information
production phases, we observe that forecast activity is unevenly distributed during a year, with
7
We define the analyst activity phases around each of the four quarterly earnings announcements that occur in a
typical fiscal year. Consistent with our focus on analyst forecasts of annual earnings, we measure aggregate activity
for each phase around the four earnings announcements. For example, analyst activity in the information discovery
phase includes forecasts preceding the announcements of the prior fiscal year’s earnings and the earnings for the
first three quarters of the current fiscal year.
14
about half of the forecasts occurring in the early information analysis period (a mean of 22.5)
and about one quarter in the information discovery period (a mean of 14.3). The remaining
forecasts are distributed approximately evenly between the late information analysis and postanalysis phases.
5.3 Simple correlations
In Panel B of Table 1 we report Pearson and Spearman correlations among the key
variables. Our measures of operating and reporting opacity are uncorrelated, indicating that these
measures capture distinct phenomena. Operating opacity is positively correlated with Forecast
Activity in each phase of the analyst activity cycle and reporting opacity is negatively correlated
with Forecast Activity except in the early information analysis phase, where the correlation is
statistically insignificant. The correlations of opacity with forecast activity in the information
discovery phase are consistent with H1a and H1b. The positive correlation of operating opacity
with forecast activity in the information analysis phase is consistent with H2a.
The Forecast Activity measures in the information discovery and post-analysis phases are
highly positively correlated, with a Pearson correlation of 0.76, indicating similar analyst activity
in these two phases.8 The remaining pairwise correlations among forecast activity levels in the
respective phases vary from 0.11 to 0.52, suggesting that analysts vary the type and amount of
their activity systematically across their information production phases.
6. Firm Opacity and Analyst Forecast Activity
6.1 Primary analysis
8
The high correlation between Forecast Activity in the discovery and post-analysis phases is consistent with the
construction of the phases, where the end of the post-analysis phase for one quarterly announcement marks the
beginning of the information discovery phase for the next announcement. Thus, the correlation pattern suggests that
analysts engage in somewhat similar activities in the information discovery and post-analysis phases and these
activities are different from those in the information analysis phase.
15
Our hypotheses H1a and H1b predict the associations of operating opacity and reporting
opacity with
analyst forecast activity in the information discovery phase and our hypotheses
H2a and H2b predict the associations in the information analysis phase. We test these predictions
by estimating Equation (2) separately in each information production phase, where i denotes a
firm and t denotes a fiscal year. Because the dependent variable, Forecast Activity, is a count
variable with a high variance relative to its mean, we follow Rock, Sedo, and Willenborg (2001),
Bae, Tan, and Welker (2008), and Tan, Wang, and Welker (2011) and use a negative-binomial
model to estimate the equation.
Forecast Activity it  a 0  a1Operating Opacity it  a 2 Reporting Opacity it
 a 3 IO Hold it  a 4 ROA Variabilit y it  a 5 LogAssets it   it . (2)
We control for institutional investors’ information demand by including their ownership
percentage, IO Hold, as in (Bhushan 1989). On average, 76% of the shares of our sample firms
are owned by institutional investors. Earnings uncertainty may increase investors’ demand for
analyst services but may also deter analysts from estimating earnings to avoid large forecast
errors. We control for the effect of earnings uncertainty on the demand for and supply of analyst
services by including the variability of return on assets, ROA Variability, measured as the
average absolute change in annual earnings (deflated by end-of-year assets) from year t–4 to year
t–1.9 We also control for firm size by including the log of the firm’s assets.
H1a predicts a positive association of operating opacity with forecast activity in the
information discovery phase and H1b predicts a negative association of reporting opacity with
forecast activity in this phase. Column 1 of Table 2 reports the forecast activity results for the
information discovery phase (Days -30 to -1). The coefficient on Operating Opacity is 0.505,
9
Uncertainty in reported earnings may reflect a firm’s operating or reporting uncertainty. Our results are unchanged
when we exclude this variable from the model.
16
significantly positive with a z-statistic of 10.62 and consistent with H1a. The coefficient on
Reporting Opacity is -0.046, and is statistically insignificant. The insignificant coefficient
suggests that reporting opacity is not associated with information discovery activity, and is
inconsistent with H1b. We assess the economic significance of the results by reporting fitted
values of the model at standardized values of the opacity variables at 0, 0.5, and 1. We find that
operating opacity is associated with large economic effects: relative to the least operationally
opaque firm, analysts’ private information search results in about seven more forecasts per firmyear for the most operationally opaque firm (that is, 18.6 – 11.2).
H2a and H2b predict that analyst forecast activity increases with a firm’s operating and
reporting opacity in the information analysis phase. We report results for the early information
analysis period in Column 2 of Table 3 and the late information analysis period in Column 3. For
the early information analysis period, the coefficient on Operating Opacity is significantly
positive at 1.278, consistent with H2a, suggesting that analysts increase their forecasting activity
for opaque firms when a large amount of information becomes available. The coefficient on
Reporting Opacity is also significantly positive at 0.459, consistent with H2b, suggesting that
earnings announcements overcome analysts’ indifference to report opacity in the information
discovery phase. Relative to the least operationally opaque firms, analysts’ information analysis
results in 31 more forecasts for the most operationally opaque firms in the information analysis
phase (43.0 – 12.0); and relative to the firms with the least opaque reporting, analysts issue over
11 more forecasts for the firms with the most opaque reporting in the information analysis phase
(31.8 – 20.1). The operating and report opacity coefficients are significantly positive and
negative, respectively, in the late information analysis period, shown in Column 3 of the table,
suggesting that, soon after earnings are announced, analysts revert to the patterns we observe in
17
the information discovery period for operating opacity and are discouraged by reporting opacity
from issuing forecasts. We observe the same pattern in the post-analysis phase, reported in
Column 4. The pattern for the whole analyst information production cycle, reported in the last
column, is similar to pattern in the early information analysis period.10
The coefficients for the control variables vary across the information discovery and
analysis phases, but are always statistically significant and positive except for the coefficient for
ROA Variability in the late information analysis period, which is significantly negative. The
results suggest that analysts respond to institutional investor demand for information and
generally increase their forecast activity with earnings volatility and firm size.
6.2 Supplementary analyses
Our primary results suggest that analysts respond to operating and reporting opacity
differently by varying their forecasting activity during their information production cycle.
Analyst forecast activity may vary across firms because of the number of analysts following a
firm and/or because of the number of forecasts issued per analyst for a given firm. We provide
two supplementary analyses to shed light on our primary finding. In the first analysis, we
construct the variables Follow for analyst following in a given firm-year and Forecast Intensity
for the number of forecasts per analyst. Our Forecast Intensity variables correspond with the
Forecast Activity variables: Forecast Intensity is the value of Forecast Activity divided by
Follow and we construct a measure for each analyst information production phase as well as for
the whole information production cycle. We re-estimate Equation (2) after replacing Forecast
Activity with Follow and alternatively with Forecast Intensity.
10
By construction, the dependent variable for the overall forecast model is the sum of the dependent variables in the
preceding columns. Consequently, the fitted values in the last column are approximately equal to the sums of the
values in the preceding columns. The coefficients for the opacity variables in this column reflect the net effect of
analysts’ behavior in the information discovery, analysis, and post-analysis phases.
18
Table 3 reports the estimation results, where the Follow equation is estimated using a
negative binomial model and the Forecast Intensity equation is estimated using OLS. In
Column1, both operating opacity and reporting opacity are positively associated with analyst
following. We estimate the economic effects of opacity on analyst following, but do not report
the results in the table. The most operationally opaque firms are followed by about 14 more
analysts than the least operationally opaque firms, or 2.7 times as many analysts (22.0 for the
most opaque firms versus 8.1 for the least opaque firms). The most opaque reporting firms are
followed by about three more analysts than the most transparent reporting firms (15.3 for the
most opaque firms versus 12.6 for the most transparent firms).
The estimations of forecast intensity equations are reported in the remaining columns of
Table 3. The coefficient on Operating Opacity is significantly negative for the whole cycle and
in each phase except the early information analysis period, where it is significantly positive.
These results suggest that the increased analyst forecast activity for operationally opaque firms is
less than the level predicted by the positive relation between operating opacity and analyst
following, except right after an earnings announcement event, when the increase in activity
related to operating opacity exceeds the level predicted by analyst following. The coefficient on
Reporting Opacity is statistically insignificant for the whole cycle, but is significantly negative
for all information production phases except the early information analysis phase, where it is
significantly positive. Combined with the analyst following test, these results suggest that the
decreased analyst forecast activity for opaque reporting firms is the net effect of more analysts
following these firms than other firms and to fewer forecasts issued by each analyst who follows
these firms than for other firms in most analyst activity phases. The exception is the early
information analysis period, when the number of forecasts per analyst increases with reporting
19
opacity perhaps because this period is among the limited windows of available corporate
disclosure.
In our second supplementary analysis, we show that the variation in forecast activity
across firms of opacity measures and during analyst information production cycles cannot be
predicted by analyst following alone. In Figure 1 we plot the fitted values for analyst following
and forecast activity in the information discovery and analysis phases, along with the 95 percent
confidence intervals. We investigate whether the effects of opacity on forecast activity are
incremental to the differences in analyst coverage. We estimate the excess of forecasts issued
over the number we would expect if analysts issued forecasts in the same proportions in each
activity period (we omit detailed results for brevity). For example, since 2.7 times as many
analysts cover firms in the high (most opaque) operating opacity group as cover firms in the low
(least opaque) operating opacity group, we would expect 2.7 times as many forecasts for firms in
the high operating opacity group as are issued in the low operating opacity group in each period
if analysts’ activity is insensitive to both opacity and proximity to the earnings announcement.
For convenience, we use the fitted value for the number of forecasts at the low extreme of
operating opacity as the baseline and predict the expected forecast activity based on analyst
following. We obtain the unexpected activity by subtracting the expected value from the fitted
value.
We find that in the information discovery period, there are about 12 fewer forecasts for
firms with the most opaque reporting than are predicted based on analyst following. The
difference between the observed and predicted number of forecasts for extreme operating opacity
is smaller, at about four fewer forecasts. In the information analysis period, both forms of opacity
are associated with increased forecast activity, with ten more forecasts than analyst coverage
20
would suggest for extreme operating opacity and seven more forecasts for extreme reporting
opacity. In the late information analysis period, there are six fewer forecasts than expected for
extreme operating opacity but only about 2.6 fewer for extreme operating opacity. Both types of
opacity also have fewer forecasts in the post-analysis period (seven fewer for operating opacity
and 3 to four fewer for reporting opacity).
7. Investor Reaction to Analyst Forecast Revisions
7.1 Baseline analysis
A key remaining question concerns the usefulness of analyst forecasts to investors. We
estimate return responses to analyst forecast revisions and rely on the model proposed by Ivkovic
and Jegadeesh (2004) to interpret and compare forecast response coefficients across opacity
levels and analyst information production phases. We analyze forecasts of fiscal-year earnings
provided at different times of the year and express security prices as a function of the expected
fiscal-year earnings. The firm’s stock price at time s, Ps , is the sum of a perfect foresight price,
PsF , representing the price investors would set if they knew future cash flows with certainty and
two types of error, one (  s ) related to uncertainty about the earnings and a second ( s ), a pricing
error that is orthogonal to the information in the earnings:
Ps  PsF   s   s .
(3)
The first error term has a normal distribution with mean 0 and time-dependent variance
 2 ( s ). An analyst revising a forecast of annual earnings at time s conveys value-relevant
information  s that contains information about the earnings-related error in the pre-forecast
2
security price  s along with noise s , which is also normally distributed with variance   (s).
21
Thus  s   s   s . Upon receiving the revised forecast, investors update their priors based on the
forecast’s information content. The change in price is
P
new
s

|  s  Ps 
s
1    (s) /  2 (s)
2
.
(4)
Interpreting the analyst’s information,  s , as the forecast revision, the change in price is
the product of the revision and a forecast response coefficient that depends on the relative
2
2
precision of the analyst’s signal. Ivkovic and Jegadeesh refer to the ratio   (s) /   (s) as the
analyst information ratio (AIR). This ratio represents the relative precision of the analyst’s signal
versus the pre-forecast market information. The price response to the analyst’s forecast revision
is positively related to AIR, which increases as the analyst’s information becomes more precise
or the market’s information becomes less precise. We use this analytical model to interpret the
forecast revision coefficient (FRC) in our empirical model in Equations (5) and (6) below: the
model draws a link between the quality of the forecast revision and the magnitude of the FRC
and hence the usefulness of the forecast revision to investors.
We measure the forecast revision, Revision, as the difference between the analyst’s
current and prior forecasts and deflate this difference by the stock price at the beginning of the
return window. Equation (5) is the baseline model for the return response to analyst j’s forecast
for firm i at time s before we consider the effects of opacity:
Return
ijs
 b 0  b 1 Revision
ijs
 c 1 Surprise
ijs
  ijs .
(5)
We include the earnings announcement surprise, Surprise, as a control variable because earnings
announcement surprises affect stock returns. Surprise is the difference between the firm’s actual
earnings and the pre-announcement consensus forecast measured two days before the
22
announcement, with this difference deflated by the stock price at the beginning of the return
window.
We estimate the model separately for the information discovery, early information
analysis, late information analysis, and post-analysis phases as well as the whole cycle. The
results, reported in Table 4, show that the FRC declines from the information discovery phase
through the information analysis phase and then increases in the post-analysis phase. The
coefficient is 1.257 in the information discovery phase, 0.875 in the early information analysis
period, 0.268 in the late analysis period, and 0.630 in the post-analysis phase. These results are
consistent with prior research that concludes that analysts’ information discovery role is more
important than their information analysis role (Chen et al. 2010) and with research that finds low
return responses to forecasts issued after the earnings announcement window (Ivkovic and
Jegadeesh 2004). The coefficient on the earnings surprise variable is positive and significant in
the information discovery phase and the early information analysis period, but not in the late
information analysis period and the post-analysis phase, suggesting that investors incorporate
earnings news in stock prices by the end of the early information analysis period.
7.2 Primary analysis
Now we include the opacity measures in the model and use their interactions with
Revision to assess how a firm’s operating and reporting opacity affect investors’ responses to
forecast revisions.11 Equation (6) is our revised model:
11
Our results are unchanged when we include the main effects of Operating Opacity and Reporting Opacity in the
model. We exclude these variables in the version of the model we estimate because we do not expect opacity to
affect the magnitude of average stock returns at the analyst forecast or earnings announcement event. Traditional
earnings-response-coefficient studies use a similar research design.
23
 b1  b2Operating Opacityis 

Returnijs  b0  Revisionijs  
b
Reporting
Opacity

is
3


 c1  c2Operating Opacityis 
   ijs .
 Surpriseijs  
  c3 Reporting Opacityis 
(6)
The differential coefficient b2 measures the incremental effect of operating opacity on the FRC
and the differential coefficient b3 measures the incremental effect of reporting opacity on the
FRC.
Table 5 presents the estimation results. Consistent with H3a and H3b, the differential
FRCs for operating and reporting opacity are both significantly positive in the information
discovery and information analysis phases, suggesting that investors find analyst forecasts for
opaque firms to be more useful than forecasts for other firms. We make two additional
observations. First, the variation in FRCs across analyst information production phases is largely
due to opaque firms. The coefficient on Revision represents the FRC for the most transparent
firms and is 0.460 in the information discovery phase, 0.446 in the early information analysis
period, 0.373 in the late information analysis period, and 0.611 in the post-analysis phase. These
coefficients are relatively stable across the analyst information production phases, unlike our
FRC estimates in Table 4 for the full sample, and are much smaller than the coefficients for the
information discovery and early analysis periods in Table 4. Second, the differential response
coefficients for opacity vary across the information production phases: opaque firms’ FRC is
highest in the information discovery phase and declines over the next two phases, similar to the
pattern in Table 4. For example, the differential coefficient for operating opacity is 1.126 in the
information discovery phase and 0.519 in the early information analysis period, both of which
are statistically significant. The differential coefficients in the late information analysis and postanalysis phases are not significantly different from 0. The differential coefficient for reporting
24
opacity is 1.332 in the information discovery phase and 0.784 in the early information analysis
period, both significantly positive at better than the 1 percent level. The differential coefficient in
the late analysis period is not significantly different from 0, but the coefficient in the postanalysis period is significantly positive at the 1 percent level.
The results in this and the previous sections enable us to compare analysts’ activity levels
with the market impact of their forecasts. The analyst forecast results show that analysts are most
active in the early information analysis period for opaque firms and that reporting opacity is
associated with low analyst activity in the information discovery and post-analysis phases. This
pattern indicates that analysts following opaque firms focus on interpreting information that the
firm announces rather than on discovering new information, particularly for firms whose
reporting is opaque. Despite the relatively subdued activity in the information discovery phase
for firms with opaque reporting, return responses to forecasts issued in this phase are higher than
the responses to forecasts for other firms in the same phase or similar firms at other times. The
contrast indicates that the weight investors place on analyst forecasts does not correspond to
analyst activity in their information production phases.
Our regression estimations in Table 5 could be affected by the specific functional form
we assume for the relation between opacity and FRCs. We now relax the functional form
assumption. For each opacity measure we classify firms-years whose opacity measure is above
the median of the sample as “complex” firms and the remaining firms as “simple” firms. Thus,
we have four groups of firm-years based on operating and reporting opacity: simple/simple,
simple/complex, complex/simple, and complex/complex. We estimate Equation (5) in each
information production phase for each of the four groups.
25
We report the results in Table 6. For conciseness we only report the FRC, which is
significantly positive in each case and generally increases from the simple/simple to the
complex/complex groups. Furthermore, within each group the coefficient is largest in the
information discovery phase, declines in the early information analysis period, and declines
further in the late information analysis period. The coefficient is uniformly higher in the postanalysis phase than in the late information analysis period. We focus our statistical tests on the
difference between the simple/simple and complex/complex groups by stacking the observations
and using an indicator variable to distinguish simple/simple from complex/complex firms. The
differential coefficient for opacity is 0.777 (t-statistic = 8.26) in the information discovery phase,
0.511 (t-statistic = 6.78) in the early information analysis period, -0.070 (t-statistic = -0.71) in the
late information analysis period, and 0.148 (t-statistic = 1.38) in the post-analysis phase. The
difference is 0.494 (t-statistic = 10.31) for the entire 60-trading-day cycle. These patterns are
consistent with our findings from Table 5.
In Figure 2 we plot the FRCs for the simple/simple and complex/complex groups in the
various information production phases. The return responses vary much less for simple/simple
firms across the information production phase than for complex/complex firms. The FRCs for
simple/simple firms are lower than those for complex/complex firms in the information
discovery phase and in the information analysis phase. More important, the figure shows large
FRC differences between the two groups of firms in the information discovery phase, smaller
differences in the early information analysis period, convergence to roughly similar responses in
late information analysis period, and the reemergence of differences in the post-analysis phase.
Our results in Table 5 and Table 6 support H3 and show that both operating and reporting
opacity are associated with increased return responses to forecast revisions. The incremental
26
coefficients are highest in the information discovery phase, smaller in the early information
analysis period, insignificant in the late information analysis period, and significantly positive for
reporting opacity in the post-analysis phase.12
7.3 Sensitivity tests
We examine the possibility that the differences we observe in return responses to forecast
revisions are caused by nonlinearity. Prior research finds that the form of the nonlinear relation
between earnings news and returns is S-shaped, with high response coefficients near zero and
declining responses per unit of earnings news as the magnitude of news increases (Freeman and
Tse 1992).13 If the response is nonlinear and the distribution of forecast revisions differs across
the categories of firms we compare (simple versus complex firms or discovery versus postanalysis phases), then the differences we observe in estimated FRCs could be due to nonlinearity
rather than opacity.
We investigate the possible effects of nonlinearity using a nonparametric approach that
does not specify the form of nonlinearity ex ante. We focus on the simple/simple and
complex/complex groups of firms in the various information production phases. For each group,
we classify firms in 100 intervals of price-deflated forecast revisions ranging from -0.01 to
0.01.14 Thus the length of each interval is 0.0002. We calculate the mean return in each forecast12
A potential explanation for differences in any return responses across subsamples is that they are due to
underlying differences in responses to earnings or forecast news for the types of firms in each category (for example,
large versus small firms) rather than to differences in the value of analyst activity. We use the same firms in each
phase, so these time-invariant firm characteristics cannot explain the differences we observe across the phases.
Another possible explanation is that our FRC estimates are influenced by management earnings guidance. We
measure Return within two hours after an analyst forecast revision, and the short window should mitigate this
concern. In untabulated tests we find that the FRC pattern for firms that issue no guidance throughout the fiscal year
is similar to that for the full sample.
13
Nonlinearity in the earnings surprise-returns relation could arise because nonrecurring items form a large
proportion of extreme earnings surprises. If analysts restrict their forecasts to recurring items, then the effects of
nonrecurring items would be reduced or eliminated.
14
We classify firms with forecast revisions below -0.01 in the first interval and those with forecast revision above
0.01 in the 100th interval.
27
revision interval for each group. If H3a and H3b hold, the mean return would be lower for
complex/complex firms than for simple/simple firms for each negative forecast revision interval;
we expect the mean return to be higher for complex/complex firms than for simple/simple firms
for each positive forecast revision interval. We label these predictions collectively as “stronger
return responses for complex/complex firms than for simple/simple firms.”
We evaluate our predictions with a binomial test based on the number of successes (the
number of intervals that conform to our expectations and report the results for all phases in the
first row of Table 7. The tests show statistically stronger return responses for complex/complex
firms than for simple/simple firms in the information discovery and early information analysis
phases, the reverse in the late information analysis period, and no difference in the post-analysis
phase.15 These observations indicate that in the main analyst information production phases of
interest, the return responses to analyst forecast revisions are stronger for opaque firms than for
other firms.
To test the possibility that our results are driven by extreme forecast revisions, we
separate the 50 forecast revision intervals closest to 0 (referred to as “moderate revisions”) from
the remaining 50 intervals (referred to as “extreme revisions”). The results, reported in the
second and third rows of Table 7, show that the success rates are similar for moderate and
extreme revisions except for the late information analysis period, where the earlier result based
on the 100 intervals is driven by extreme revisions.
To illustrate the stronger return responses for complex/complex firms than for
simple/simple firms in the information discovery phase, we plot the mean return of each interval
separately for the two types of firms in Figure 3. With rare exceptions, returns are negative for
15
We measure the frequency of successes, but not their magnitudes, so we cannot compare the strength of the return
response in the information discovery and analysis periods.
28
downward forecast revisions and positive for upward forecast revisions. We observe that for both
downward and upward forecast revisions, returns are stronger for complex/complex firms than
for simple/simple firms. The analyses in this subsection indicate that our previous findings of
differential FRC related to opacity are not due to nonlinearity in investors’ responses to forecast
revision news.
8. Conclusion Financial analysts respond to investors’ demand for timely corporate information by
engaging in information discovery before firms announce their earnings and in information
analysis once firms announce their earnings. The extent and success of analysts’ information
discovery and analysis activities are likely to be influenced by the nature of the firm’s
information environment, with distinct effects of operating opacity (which increases the
difficulty of discovering information about transactions) and reporting opacity (which increases
the difficulty of inferring the financial statement impact of the firm’s transactions). We find that
in the information discovery phase, operating opacity is associated with increased analyst
forecast activity, but reporting opacity is unrelated to forecast activity. Both types of opacity are
associated with increased analyst forecast activity in the information analysis phase. These
patterns suggest that analysts delay their effort on firms whose reporting is opaque until the
company announces earnings. The large flow of corporate information at the earnings
announcement stimulates increased forecast activity for opaque firms, regardless of the source of
opacity, consistent with investor demand for analysts’ insights into those firms’ earnings. Return
responses to forecast revisions of annual earnings increase with opacity and are higher in the
information discovery phase than in the information analysis phase. These patterns suggest that
29
investors value analysts’ efforts to follow opaque firms. Furthermore, investors value analysts’
efforts to discover information more highly than they do their efforts to analyze information.
Overall, our study shows that analysts respond to investors’ information demand about
opaque firms by varying their efforts in analyst information production phases around earnings
announcements and that analyst forecasts generate return responses that vary systematically with
opacity levels as well as across the phases. These findings further our understanding of how
financial analysts respond to investors’ information demand and when investors most value
analysts’ services.
30
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Appendix 1
Summary of Empirical Predictions and Findings
Information Discovery
Phase (Days -30 to -1)
Hypo. Predict Find
Information Analysis
Phase (Days 0 to 4)
Hyp Predict
Find
o.
Post-analysis Phase
(Days 5 to 29)
Hy Predict
Find
po.
Forecast Activity:
Operating Opacity
H1a
+
+
H2a
+
+
+
Reporting Opacity
H1b
–
Insig.
H2b
+
+a
–
Operating Opacity
H3a
+
+
H3a
+
+b
Insig.
Reporting Opacity
H3b
+
+
H3b
+
+b
+
Return Response:
a
: We find a significant positive coefficient in the early information analysis period (Days 0 to 1) and a
negative coefficient afterwards.
b
: We find such results only in the early information analysis period (Days 0 to 1).
H1a: In the information discovery phase, analyst forecast activity increases with a firm’s
operating opacity.
H1b: In the information discovery phase, analyst forecast activity decreases with a firm’s
reporting opacity.
H2a: In the information analysis phase, analyst forecast activity increases with a firm’s operating
opacity.
H2b: In the information analysis phase, analyst forecast activity increases with a firm’s reporting
opacity.
H3a: Return responses to analyst forecast revisions increase with a firm’s operating opacity.
H3b: Return responses to analyst forecast revisions increase with a firm’s reporting opacity.
33
Appendix 2
Variable Definitions
Forecast Activity
Log Assets
MB
RD
Operating Opacity
Reporting Opacity
IO Hold
ROA Variability
Follow
Return
Revision
Surprise
= the number of analyst forecasts of annual earnings issued during the
fiscal year. This variable is calculated separately for the information
discovery phase (Trading Days -30 to -1), information analysis phase
(Days 0 to 4), and post-analysis phase (Days 5 to 29), where each
analyst information production cycle runs from 30 trading days before
an earnings announcement event (Day 0) to 29 trading days after it.
We further separate the information analysis phase into early
information analysis (Days 0 to 1) and late information analysis (Days
2 to 4).
= the natural logarithm of total assets at the beginning of the year.
= the market-to-book ratio at the beginning of the year, winsorized at
10.
= the ratio of research and development expense to sales in the
previous year.
= the composite score of the firm’s total assets, MB and RD, following
Chen et al. (2010). We standardize the raw measure by dividing the
value for each observation minus the minimum value of the sample by
the difference between the maximum and minimum values of the
sample. The standardized value ranges from 0 to 1 with 1 being the
most opaque.
= a measure of the historical correspondence between the firm’s
accounting accruals and cash flows. We standardize the raw measure
by dividing the value for each observation minus the minimum value
of the sample by the difference between the maximum and minimum
values of the sample. The standardized value ranges from 0 to 1 with 1
being the most opaque.
= the percentage ownership of institutional investors according to the
most recent report before the earnings announcement for the previous
year.
= the average absolute annual ROA change in the four previous years,
where ROA is GAAP earnings deflated by end-of-year total assets.
= the number of analysts that provide estimates of fiscal year earnings.
= the intra-day stock return in the two hours subsequent to the time
stamp for an analyst forecast revision.
= the difference between the analyst’s forecast and the same analyst’s
prior forecast, scaled by the stock price at the beginning of the return
window.
= the difference between the firm’s actual earnings and the preannouncement consensus forecast (both obtained from IBES summary
data file) measured two days before the announcement, scaled by the
34
Complex
Simple
Forecast Intensity
stock price at the beginning of the return window.
= 1 if the firm-year’s opacity measure is above the median of the
sample and 0 otherwise. We define this variable separately for
operating and reporting opacity.
= 1 if the firm-year’s opacity measure is at or below the median of the
sample and 0 otherwise. We define this variable separately for
operating and reporting opacity.
= Forecast Activity divided by Follow.
35
Figure 1
Forecast activity for operating and reporting opacity levels in the information discovery and analysis phases
Note: Each figure shows fitted values from the respective negative binomial models in Tables 2 and 3 at different levels of operating and reporting
opacity, with 95% confidence intervals marked. Both opacity measures are standardized to range from 0 to 1.
36
Figure 2
Forecast response coefficients across opacity levels during an analyst activity cycle
1.8
1.6
1.4
1.2
1.0
Simple/simple firms
0.8
Complex/complex firms
0.6
0.4
0.2
0.0
‐30 to ‐1
0 and 1
2 to 4
5 to 29
Time period in days relative to quarterly earnings announcement
Note: We classify firms separately for operating and reporting opacity and identify a firm as “simple” if its opacity measure is at or below the
median of the sample and as “complex” if the opacity measure is above the median. “Simple/simple” firms are those classified as “simple” on both
opacity measures; “complex/complex” firms are those classified as “complex” on both opacity measures.
37
Figure 3
Mean returns to simple versus complex firms across forecast revision levels in the information discovery period
0.020
0.015
0.010
Intra‐day returns
0.005
0.000
Simple/simple
‐0.005
Complex/complex
‐0.010
‐0.015
‐0.020
‐0.025
Price‐deflated forecast revision
Note: We classify firms separately for operating and reporting opacity and identify a firm as “simple” if its opacity measure is at or below the
median of the sample and as “complex” if the opacity measure is above the median. “Simple/simple” firms are those classified as “simple” on both
opacity measures; “complex/complex” firms are those classified as “complex” on both opacity measures. For each group, we sort firms in 100
intervals of price-deflated forecast revisions ranging from -0.01 to 0.01(with the length of an interval being 0.0002). The figure shows the mean
return in each forecast-revision interval for each group in the information discovery phase.
38
Table 1
Descriptive statistics
Panel A: Summary statistics
Variable
Mean
Min
Q1
Median
Q3
Max
Dependent Variables:
Overall Forecast Activity
Information Discovery Activity
Early Info. Analysis Activity
Late Info. Analysis Activity
Post-analysis Activity
49.7
14.3
22.5
4.9
7.9
2
0
0
0
0
23
4
10
1
2
37
9
17
3
5
63
18
30
7
10
349
154
131
63
122
Explanatory Variables:
Operating Opacity (raw)
Reporting Opacity (raw)
Operating Opacity (standardized)
Reporting Opacity (standardized)
1.34
0.035
0.45
0.24
0.00
0.000
0.00
0.00
1.00
0.016
0.33
0.10
1.50
0.027
0.50
0.18
2.00
0.045
0.67
0.31
3.00
0.260
1.00
1.00
Control Variables:
IO Hold
ROA Variability
0.76
0.059
0.00
0.000
0.64
0.013
0.82
0.027
0.97
0.061
1.00
0.725
Descriptive Firm Characteristics:
Log Assets
MB
RD
Follow
7.18
3.42
0.071
13.2
1.89
0.11
0.000
1
5.96
1.77
0.000
7
7.10
2.65
0.001
11
8.33
4.21
0.087
17
12.05
10.00
0.815
56
Other:
Overall Forecast Intensity
3.67
1.00
2.86
3.50
4.25
16.00
39
Panel B: Pairwise correlations (Pearson in the lower diagonal and Spearman in the upper diagonal)
1
1. Overall Forecast
Activity
2. Information
Discovery Activity
3. Early Info. Analysis
Activity
4. Late Info. Analysis
Activity
5. Post-analysis Activity
2
3
4
5
6
7
8
9
10
0.839
0.828
0.396
0.732
0.390
-0.120
0.188
-0.050
0.466
0.530
0.316
0.640
0.259
-0.168
0.085
-0.083
0.452
0.068
0.421
0.416
0.007
0.257
0.032
0.319
0.377
0.083
-0.169
-0.015
-0.146
0.267
0.255
-0.179
0.015
-0.078
0.443
-0.014
-0.044
0.081
0.342
0.095
0.440
-0.446
0.026
-0.117
0.900
0.788
0.519
0.445
0.374
0.106
0.814
0.759
0.419
0.411
6. Operating Opacity
(standardized)
7. Reporting Opacity
(standardized)
8. IO Hold
0.350
0.208
0.448
0.100
0.191
-0.106
-0.134
0.009
0.132
-0.141
0.001
0.142
0.074
0.207
0.054
0.043
-0.033
0.049
9. ROA Variability
-0.062
-0.080
0.008
-0.111
-0.067
0.087
0.411
-0.013
10. Log Assets
0.422
0.370
0.328
0.213
0.355
0.331
-0.411
-0.098
-0.411
-0.345
Note: The sample is comprised of 10,011 firm-year observations. Panel A presents summary statistics. Panel B presents Pearson and Spearman
correlations, where correlations that are statistically significant at the 5 percent level are in bold. See variable definitions in Appendix 2.
40
Table 2
The relation between firm opacity and analyst forecast activity
Forecast Activityit  a0  a1Operating Opacityit  a 2 Reporting Opacityit  a3 IOHold it  a 4 ROA Variabilityit  a5 LogAssetsit   it
Information
Discovery
0.264***
(5.01)
0.505***
(10.62)
-0.046
(-0.81)
0.747***
(19.43)
0.358***
(3.12)
2.476***
(41.16)
Early Information
Analysis
0.724***
(19.19)
1.278***
(38.15)
0.459***
(11.23)
1.011***
(36.39)
0.424***
(5.34)
1.364***
(32.41)
Late Information
Analysis
0.655***
(11.55)
0.267***
(5.13)
-0.346***
(-5.58)
0.363***
(8.57)
-0.716***
(-5.70)
1.064***
(16.12)
Post-Analysis
Whole Cycle
0.037
(0.71)
0.464***
(9.67)
-0.239***
(-4.11)
0.488***
(12.46)
0.610***
(5.21)
2.303***
(38.36)
1.745***
(52.66)
0.832***
(27.54)
0.124***
(3.41)
0.770***
(31.33)
0.367***
(5.08)
1.787***
(47.07)
Pseudo R2
3.9%
5.1%
1.5%
4.0%
4.8%
Fitted values for
dependent variable
Op. Opacity = 0.0
Op. Opacity = 0.5
Op. Opacity = 1.0
11.2
14.4
18.6
12.0
22.7
43.0
4.4
5.0
5.7
6.3
8.0
10.0
33.1
50.1
76.0
Intercept
Operating Opacity
Reporting Opacity
IO Hold
ROA Variability
Log Assets
Rep. Opacity = 0.0
14.5
20.1
5.3
8.3
48.2
Rep. Opacity = 0.5
14.1
25.3
4.5
7.4
51.2
Rep. Opacity = 1.0
13.8
31.8
3.8
6.6
54.5
Note: This table reports the relation between firm opacity and analyst forecast activity in analyst information production phases around earnings
announcements, estimated in negative binomial models. We report fitted values for the dependent variable at specified levels of the standardized
opacity measures. See Appendix 2 for variable definitions. In the model, i denotes a firm and t denotes a fiscal year. We report z-statistics in
parentheses. ***, **, and * denote statistical significance at the 1 percent, 5 percent, and 10 percent levels, respectively. The number of firm-year
observations is 10,011.
41
Table 3
The relation between firm opacity and analyst following and forecast intensity
 a 0  a1Operating Opacity it  a 2 Reporting Opacity it


Follow or Forecast Intensity it  




a
IO
Hold
a
ROA
Variabilit
y
a
LogAssets

3
it
4
it
5
it
it


Intercept
Operating Opacity
Reporting Opacity
IO Hold
ROA Variability
Log Assets
Pseudo R2
Adjusted R2
Follow
Information
Discovery
0.759***
(26.93)
1.004***
(41.17)
0.196***
(6.58)
0.522***
(25.41)
0.167***
(2.91)
1.420***
(46.39)
0.477***
(7.10)
-0.414***
(-6.35)
-0.130**
(-2.13)
0.087
(1.33)
0.273**
(1.99)
1.113***
(9.73)
Early
Information
Analysis
0.924***
(4.23)
0.473***
(5.01)
0.348***
(3.76)
0.761***
(9.32)
0.409*
(1.87)
-0.229
(-1.47)
6.6%
9.7%
Forecast Intensity
Late
Post-Analysis
Information
Analysis
0.780***
0.469***
(17.73)
(7.65)
-0.316***
-0.248***
(-10.01)
(-6.13)
-0.191***
-0.146***
(-5.30)
(-2.73)
***
-0.084
-0.093**
(-3.19)
(-2.33)
-0.266***
0.237**
(-4.09)
(2.28)
-0.164***
0.507***
(-2.77)
(6.23)
Whole Cycle
2.650***
(15.55)
-0.506***
(-3.77)
-0.119
(-1.06)
0.671***
(4.85)
0.654**
(2.12)
1.227***
(8.23)
7.9%
4.8%
5.0%
4.2%
Note: This table reports the relation between firm opacity and analyst following and forecast intensity in analyst information production phases
around earnings announcements. The estimation for Follow uses the negative binomial model. The estimation for Forecast Intensity uses OLS
with the standard errors clustered at the firm and year levels. See Appendix 2 for variable definitions. In the model, i denotes a firm and t denotes a
fiscal year. We report t-statistics in parentheses. ***, **, and * denote statistical significance at the 1 percent, 5 percent, and 10 percent levels,
respectively. The number of firm-year observations is 10,011.
42
Table 4
Return responses to forecast revisions – the baseline model
Return ijs  b0  b1 Revision ijs  c1 Surprise ijs   ijs
Information
Discovery
Late
Information
Analysis
Days 2 to 4
PostAnalysis
Whole Cycle
Days -30 to -1
Early
Information
Analysis
Days 0 and 1
Days 5 to 29
Days -30 to 29
Intercept
-0.001***
(-2.64)
0.000
(0.81)
0.000
(1.11)
-0.000
(-1.63)
-0.000*
(-1.65)
Revision
1.257***
(7.74)
0.875***
(8.60)
0.268***
(5.41)
0.630***
(6.14)
0.922***
(8.88)
Surprise
0.305***
(4.14)
2.5% 95,581
0.521***
(5.53)
1.6%
150,815
0.061
(0.91)
0.3%
34,749
-0.033
(-0.73)
0.8%
53,163
0.273***
(6.23)
1.6%
334,308
R 2 Obs.
Note: This table estimates return responses to forecast revisions issued in analyst information production
phases around earnings announcements (Information Discovery, Early Information Analysis, Late
Information Analysis, Post-Analysis, and the overall 60-trading-day cycle). OLS estimations are used.
See variable definitions in Appendix 2. In the model, i represents a firm, j represents an analyst, and s
represents the time a forecast is issued within the respective analyst information production phases. We
report t-statistics in parentheses. ***, **, and * denote statistical significance at the 1 percent, 5 percent,
and 10 percent levels, respectively.
43
Table 5
The relation between firm opacity and return responses to forecast revisions
Returnijs  b0  Revisionijs  b1  b2 Operating Opacityis  b3 Report Opacityis 
 Surpriseijs  c1  c2 Operating Opacityis  c3 Report Opacityis    ijs
Information
Discovery Early
Late
Post-Analysis Whole Cycle
Information Information
Analysis
Analysis
Days -30 to -1 Days 0 and 1 Days 2 to 4 Days 5 to 29 Days -30 to 29
Intercept
-0.001***
(-2.58)
0.000
(0.67)
0.000
(1.17)
-0.000**
(-1.66)
-0.000*
(-1.69)
Revision
0.460*
(1.87)
0.446***
(3.22)
0.373***
(2.94)
0.611***
(3.29)
0.481***
(4.27)
Revision × Operating Opacity
1.126**
(2.29)
0.519**
(2.36)
-0.172
(-0.77)
-0.349
(-1.21)
0.543***
(2.67)
Revision × Reporting Opacity
1.332***
(3.65)
0.784***
(2.57)
-0.135
(-0.42)
0.907***
(2.88)
0.863***
(5.05)
Surprise
-0.024
(-0.18)
0.260
(1.23)
0.184
(0.90)
-0.121
(-1.05)
0.052
(0.57)
Surprise × Operating Opacity
0.476**
(1.65)
0.173
(0.46)
-0.337
(-1.27)
0.102
(0.36)
0.200
(1.33)
Surprise × Reporting Opacity
0.666
(1.20)
2.75
95,581
0.879**
(1.99)
1.7%
150,815
0.036
(0.08)
0.3%
34,749
0.214
(0.86)
0.8%
53,163 0.675***
(2.69)
1.7%
334,308
R2
Obs. Note: This table measures return responses to forecast revisions issued in analyst activity periods around
earnings announcements (Information Discovery, Early Information Analysis, Late Information
Analysis, Post-Analysis, and the overall 60-trading-day cycle) and the interaction of forecast revisions
(Revision) with operating and reporting opacity. OLS estimations are used, with standard errors clustered
at the firm and year levels. See variable definitions in Appendix 2. In the model, i represents a firm, j
represents an analyst, and s represents the time a forecast is issued within the respective analyst
information production phases. We report t-statistics in parentheses. ***, **, and * denote statistical
significance at the 1 percent, 5 percent, and 10 percent levels, respectively.
44
Table 6
Return responses to forecast revisions for complex versus simple firms
Return
ijs
 b 0  b 1 Revision
Operating
Reporting
Opacity
Opacity
Information discovery period: Days -30 to -1
Simple
Simple
Simple
Complex
Complex
Simple
Complex
Complex
Complex/Complex – Simple/Simple
Early information analysis period: Days 0 and 1
Simple
Simple
Simple
Complex
Complex
Simple
Complex
Complex
Complex/Complex – Simple/Simple
Late information analysis period: Days 2 to 4
Simple
Simple
Simple
Complex
Complex
Simple
Complex
Complex
Complex/Complex – Simple/Simple
Post-analysis period: Days 5 to 29
Simple
Simple
Simple
Complex
Complex
Simple
Complex
Complex
Complex/Complex – Simple/Simple
All observations
Simple
Simple
Simple
Complex
Complex
Simple
Complex
Complex
Complex/Complex – Simple/Simple
ijs
 c 1 Surprise
ijs
 
ijs
Revision
coefficient
t-statistic for
revision coeff.
R2
0.859
1.498
1.162
1.636
0.777
17.10
18.38
22.64
20.57
8.26
2.1%
3.6%
2.2%
2.7%
2.5%
18,831
12,759
39,430
24,561
43,392
0.604
0.932
0.819
1.115
0.511
13.20
13.85
18.49
18.58
6.78
1.4%
2.0%
1.4%
1.9%
1.8%
26,506
24,194
57,117
42,998
69,504
0.310
0.278
0.243
0.240
-0.070
4.93
3.23
4.54
3.16
-0.71
0.5%
0.4%
0.3%
0.2%
0.3%
8,093
5,704
12,876
8,076
16,169
0.605
0.865
0.453
0.753
0.148
9.06
9.96
8.36
8.97
1.38
1.1%
1.5%
0.4%
0.8%
0.9%
11,103
7,246
21,679
13,135
24,237
0.680
1.046
0.834
1.173
0.494
24.78
25.33
31.62
29.91
10.31
1.5%
2.2%
1.4%
1.8%
1.7%
64,533
49,903
131,102
88,770
153,303
N
Note: This table measures return responses to forecast revisions issued in analyst information production
phases around earnings announcements (Information Discovery, Early Information Analysis, Late
Information Analysis, Post-Analysis, and the overall 60-trading-day cycle), using the baseline model.
See Appendix 2 for variable definitions. In the model, i represents the firm, j represents the analyst, and s
represents the time a forecast is issued within the respective analyst information production phases. We
report t-statistics in parenthesis. Classification as simple or complex is based on the median: for each
opacity measure, the observations above the median are classified as “complex” and the others are
classified as “simple.”
45
Table 7
Nonparametric test of the difference in return responses to forecast revisions for complex
versus simple firms
Number of forecast-revision intervals with stronger return
responses for complex/complex firms than for simple/simple firms
Number
Information
Early
Late
Post-Analysis
of
Discovery
Information
Information
intervals
Analysis
Analysis
Days -30 to -1 Days 0 and 1
Days 2 to 4
Days 5 to 29
All revisions
100
80
(5.90)
77
(5.30)
40
(-2.10)
59
(1.70)
Moderate
revisions
50
41
(4.38)
40
(4.10)
22
(-0.99)
29
(0.99)
Extreme
revisions
50
39
(3.82)
37
(3.25)
18
(-2.12)
30
(1.27)
Note: This table provides a nonparametric test of differences in return responses to forecast revisions for
firms in different information environments. We classify firms separately for operating and reporting
opacity and identify a firm as “simple” if its opacity measure is at or below the median of the sample and
as “complex” if the opacity measure is above the median. “Simple/simple” and “complex/complex”
firms are those classified as “simple” or “complex” for both opacity measures. For each group, we
classify firms in 100 intervals of price-deflated forecast revisions ranging from -0.01 to 0.01, so the
length of an interval is 0.0002. In a regression of returns on forecast revisions, “stronger return response”
corresponds to a higher mean return in the positive revision region or a lower mean return in the negative
revision region. The binomial test in the parenthesis in Row 1 examines whether the number of forecast
revision intervals with stronger return responses for complex/complex firms than for simple/simple firms
is statistically larger than what would occur by chance. We report z-statistics in parenthesis. In Rows 2
and 3 we split the 100 intervals into the 50 intervals close to zero (“moderate revisions”) and the 50
intervals farther from zero (“extreme revisions”).
46