Management Forecast Disaggregation and the Legal Environment:
International Evidence
Jeff Ng
School of Accountancy
The Chinese University of Hong Kong
E-mail: jeffng@cuhk.edu.hk
Albert Tsang
School of Accountancy
The Chinese University of Hong Kong
E-mail: albert.tsang@cuhk.edu.hk
Oktay Urcan
College of Business
University of Illinois at Urbana-Champaign
E-mail: ourcan@illinois.edu
October 2014
Abstract
This study examines whether, and to what extent, investors from around the world value
management forecast disaggregation (i.e., forecasts containing projections of multiple key line
items) and which forecast item do investors value the most. Using a comprehensive dataset of
international management forecasts collected from the original text of management forecasts
from 30 countries, we find that more disaggregated forecasts are positively associated with
greater stock market reaction. We also find that the positive stock market reaction associated with
management forecast disaggregation varies with forecast items contained in each forecast.
Specifically, we find that forecasts that contain sales or line items above the bottom-line net
income are associated with greater stock market reactions, suggesting that these forecasts are
perceived to be more informative by investors around the world. In addition, we also document
stronger stock market reactions associated with disaggregated and sales forecasts in countries
with stronger legal and regulatory environments. This evidence suggests that investors infer the
credibility of forecasts from the expected litigation risk associated with the issuance of such
forecasts in different countries. Further corroborating our results, we find that managers are less
inclined to issue disaggregated or sales forecasts in countries with stronger legal environment.
Keywords:
disaggregated forecasts, institutional characteristics, credibility, legal and regulatory
environment, litigation risk
* Oktay Urcan gratefully acknowledges the support of London Business School RAMD Fund. Albert
Tsang gratefully acknowledges the financial support of Research Grant Council of HKSAR. We
acknowledge the helpful comments of seminar participants at the 2014 Global Issues in Accounting
Conference hosted by the University of Chicago Booth School of Business and the University of North
Carolina Kenan-Flagler School of Business.
Management Forecast Disaggregation and the Legal Environment:
International Evidence
1. Introduction
Voluntary management forecasts are issued by managers to establish or change
market participants’ earnings expectations and are important for the functioning of capital
markets. Prior research suggests that voluntary disclosures, including management
forecasts, mitigate capital market resource misallocation by reducing the information
asymmetry between firm insiders and investors (Healy and Palepu 2001, Hirst et al. 2008,
Beyer et al. 2010). The existence and magnitude of the information asymmetry reduction
effect of voluntary disclosures, however, depend on the perceived credibility of such
disclosures (Jennings 1987; Mercer 2004; Gu and Li 2007).1
Management forecast disaggregation, i.e., forecasts containing projections of
multiple forecast items (e.g. sales, EBITDA, operating income, pre-tax earnings, earnings
before extraordinary items and discontinued operations, net income, among others), 2 is
becoming a common managerial practice both in the U.S. and around the world and
represents an important management forecast characteristic through which managers can
enhance the credibility and perceived informativeness of their forecasts (Hirst et al. 2007,
Hirst et al. 2008). However, evidence supporting the effectiveness of forecast
disaggregation in playing an effective credibility-enhancing role in the U.S. is mixed. For
1
For example, Crawford and Sobel (1982) demonstrate analytically that in equilibrium, unverifiable disclosures
are considered as untruthful and uninformative by market participants. Therefore, managers seeking to provide
informative disclosure of private information need a mechanism for credibly committing to be truthful. Similar
arguments can also be found in Stocken (2000).
2
To be concise, we use “forecast disaggregation” to refer to the degree with which management forecasts
are disaggregated and “forecast items” to refer to the specific accounting performance items included in
management forecasts throughout this paper.
1
example, while Hirst et al. (2007) experimentally show that forecast disaggregation is
perceived by market participants as more credible suggesting that forecast disaggregation
plays an effective credibility-enhancing role for management forecasts, 3 Chen et al.
(2009) find that disaggregated forecasts are no better, and sometimes could even be
worse in information quality, than aggregated earnings forecasts. Specifically, they find
that earnings forecasts disclosed with other supplemental forecast items are more biased
and less accurate than earnings forecasts without supplemental forecast items for bad
news forecasts while they do not find any difference along these dimensions between
aggregated and disaggregated good news forecasts.4
Prior studies suggest that legal and regulatory environments could play an
important role influencing managers’ forecast practices because fear of potential legal
liability associated with management forecasts is one of the major barriers deterring
managers from making self-serving forecasts (Baginski et al. 2002, Hirst et al. 2008,
Beyer et al. 2010). These studies suggest that investors may infer the informativeness
and/or credibility of disaggregated forecasts from a country’s institutional environments.
Given the mixed findings in the U.S., and the limited empirical evidence on the
effect of country-level institutions on management forecasts, one of the major objectives
of this study is to extend prior management forecast disaggregation studies (e.g., Han and
Wild 1991, Hirst et al. 2007, Chen et al. 2009, Lansford et al. 2013) to an international
3
Consistent with Hirst et al. (2007), Lansford et al. (2013) find that forecast disaggregation leads to more
timely analysts’ forecast revisions, and a larger reduction in analysts disagreement suggesting that forecast
disaggregation enhances a firm’s information environment.
4
In a sample of annual management forecasts collected between 1978 and 1982, Han and Wild (1991)
compare the stock market reaction to management earnings forecasts which include revenue forecasts with
management forecasts which include only earnings forecasts. They document that earnings forecasts
without revenue forecasts are more informative than those disclosed with revenue forecasts suggesting that
more disaggregated forecasts may not be better than management forecasts with earnings forecasts alone
(i.e., less disaggregated forecasts).
2
setting.5 Specifically, we empirically examine (1) whether and to what extent forecast
disaggregation is perceived to be credible by investors from around the world, and (2)
more importantly, whether and how country-level legal and regulatory environments
affect forecast disaggregation and its credibility-enhancing role in different countries.
While our first two research questions focus on the level of forecast
disaggregation in different countries, another important and interesting question worth
exploring is whether investors unequivocally assign similar credibility to different
forecast items. Barton et al. (2010) examine the stock market response to different
accounting performance measures internationally and find that the accounting
performance measure that investors value the most in equity valuation depends on the
ability of that measure to predict firms’ future cash flow in a country. Following the spirit
of Barton et al. (2010), we investigate the possible differential stock market reaction to
different forecast items and what explains the difference in managers’ forecast item
choices. In other words, in this study, not only we examine the differences in forecast
disaggregation, we also examine the forecast items that investors (managers) around the
world value (forecast) the most.
We examine these research questions using a comprehensive hand-collected data
covering 60,067 management forecasts containing detailed forecast disaggregation and
forecast items information issued by 8,560 unique firms from 30 countries spanning six
years from 2004-2009. Specifically, we find that, after controlling for other major
5
Employing an international study offers a chance to take advantage of a greater variation in the
institutional characteristics which could potentially affect managers’ voluntary disclosure decisions and in
the management forecast practices across countries. For example, as our results reveal, managers in
different countries not only issue management forecast with different levels of forecast disaggregation, but
there is also substantial variation in the forecast items contained in their forecasts. In addition, results from
an international setting are also likely to be more generalizable.
3
forecast properties including forecast precision, forecast horizon, forecast attribution and
loss forecasts, forecast disaggregation is generally associated with stronger stock market
reactions as measured by the absolute value of the two-day cumulative abnormal return
and volume surrounding the management forecast window. This archival evidence
supports the experimental findings of Hirst et al. (2007), suggesting that more
disaggregated forecasts are generally perceived by investors to be more informative and
credible. 6 More importantly, we find that the stock market reaction to management
forecasts varies with forecast items. Specifically, we find that management forecasts that
contain forecasts on sales or line items above the bottom-line net income (net income) are
associated with stronger (weaker) stock market reactions.
In addition, we find that the stock market response to forecast disaggregation and
sales forecasts (and also forecasts of line items above net income) are more pronounced
in countries characterized by stronger legal and regulatory regimes, i.e., where securities
regulation enforcement is high, investor protection is strong, or where class-action
lawsuits are permitted. We attribute this finding to the potentially higher expected
litigation risk that firms face when they provide these forecasts in such countries.
Finally, to further corroborating our argument, in additional forecast- and countrylevel analyses, we also find that although forecast disaggregation is perceived to be more
credible and thus elicit stronger stock market reactions in countries with stronger
expected litigation risk, managers in these countries are less likely to provide such
forecasts. Similarly, while we find that investors generally perceive management
6
Although Hirst et al. (2007) provides experimental evidence that disaggregated forecasts are perceived by
investors to be more credible, no such archival evidence exists.
4
forecasts containing sales forecasts or forecasts of other line items above net income to be
more informative, managers tend to be less likely to issue these forecasts in countries
where they are expected to face higher litigation risk after the issuance of management
forecasts.
Our paper advances the literature in several ways. First, although a large body of
research examines the determinants and market reactions to management forecasts in the
U.S., limited empirical evidence exists regarding the management forecast practices and
their consequences internationally (see Hirst et al. 2008 for a review of the management
forecast literature). 7 Ball (2001), Bushman et al. (2004), and Bushman and Piotroski
(2006) argue that difference in countries’ institutional infrastructures shape firms’
accounting and disclosure practices, suggesting that it is important to consider the effect
of country-level institutions in understanding the variation in firms’ management forecast
practices across countries. Furthermore, despite the importance of forecast disaggregation
on investors’ credibility assessment of firms’ information, few prior studies have
identified “either the circumstances under which disaggregated forecasts are provided or
the characteristics of firms that provide such forecasts” (Hirst et al. 2008, page 328).
Our study adds to this line of research by empirically examining whether and why
forecast disaggregation varies across countries with different legal and regulatory
environments.
Thus, our study sheds light on the heterogeneity in the credibility-
7
A few notable exceptions include Baginski et al. (2002) who compare management forecasts between
U.S. and Canadian firms, two otherwise similar business environments with different legal regimes and
Kato et al. (2009) who examine management forecasts practice in Japan where management forecasts are
effectively mandated. Although Radhakrishnan et al. (2012) examine variation in management forecast
activities for firms from around the world, their main objective is to identify country-level variables that
explain management forecast issuance decisions in different countries rather than the role of management
forecasts in different countries and they do not examine cross-country variations in forecast disaggregation
as we do. Specifically they find that country-level institutional factors related to business protection,
investor protection, and information dissemination affect management forecasts issuance decisions.
5
enhancing effectiveness of forecast disaggregation, which in turn could have important
practical implications.8 Our results show that a country’s legal institutions not only affect
the channels through which forecast disaggregation enhances the credibility of such
forecasts, they also explain the observed differences in management forecast practices
across countries.
Second, we extend the literature examining the information content of different
accounting line items or performance measures. Accounting research has long shown that
bottom-line earnings are informative (Ball and Brown 1968; Beaver 1968), as are the
various components that make up earnings (Fairfield et al. 1996; Bartov and Mohanram
2014). By employing an international setting, Barton et al. (2010) extend this line of
research and show that the reported financial statement performance measure that
investors value the most in different countries depends on the predictive ability of that
measure in the local context.
We further extend these studies by examining differences in stock market
reactions associated with different forecast items. By showing that the stock market
reactions associated with management forecasts vary with forecast items, our findings
complement the finding of Barton et al. (2010). Specifically, our study suggests that
while there is no global consensus on the “best” performance measure reported on
financial statements, investors around the world tend to react more strongly to certain
forecast items (e.g., forecasts containing sales and/or items above the bottom-line net
income).
8
A better understanding of the variation in the credibility-enhancing effect of forecast disaggregation in
different countries can help managers make better forecast decisions that are more likely to optimize the
value of their forecasts and help firms reap capital market benefits.
6
Finally, our results also add to the literature examining the importance of firmlevel transparency in an international setting. While Lang et al. (2012) show that firmlevel transparency matters more in countries where investor protection and disclosure
requirements are lower,9 our results suggest that firm-level voluntary disclosures, which
potentially suffer from higher self-serving or managerial opportunism concerns, could be
associated with weaker stock market reactions, especially in countries with weaker
institutional characteristics. Our results, together with the findings of Lang et al. (2012),
suggest that the quantity of voluntary disclosures itself may be insufficient in improving
stock market transparency. Rather, it is the country-level institutional regimes which
plays important roles in enhancing the credibility of additional voluntary disclosures
(such as disaggregated forecasts and forecasts of specific performance items).
The remainder of the paper proceeds as follows. We review the literature and
develop our hypotheses in Section 2. We describe our data and sample in Section 3.
Section 4 discusses our research design. Section 5 presents the main empirical results and
additional analysis. Finally, in Section 6 we summarize and conclude.
2. Literature Review and Hypotheses Development
A growing body of empirical research examines the information content of
management forecasts. For example, recent studies examine how the market reacts to
management forecasts in the U.S. and document that such forecasts have the potential to
affect stock prices (Pownall et al. 1993) and analysts’ forecasts (Baginski and Hassell
1990), and to reduce information asymmetry (Coller and Yohn 1997; Shroff et al. 2013),
9
Lang et al. (2012) measure firm-level transparency by less earnings management, better accounting
standards, higher quality auditors, more analysts following, and higher analyst forecast accuracy.
7
cost of capital (Frankel et al. 1995, Shroff et al. 2013), and firms’ expected litigation
costs (Skinner 1994, Kasznik and Lev 1995).
Recent research further examines the role of forecast disaggregation, a forecast
characteristic over which managers have substantial control, and find that the
informativeness of forecast disaggregation depends on managers’ incentives for issuing
such disclosures. For example, Hirst et al. (2007) show that by issuing disaggregated
forecasts that pre-commit managers to a specific path via which firms plan to achieve
their earnings target, managers can mitigate investors’ skepticism regarding their
credibility, which in turn increases the perceived credibility of their forecasts. Similarly,
Trueman (1986) argues that disaggregated forecasts that contain supplemental
information may signal that managers have better information or superior forecasting
ability. Both studies suggest that forecast disaggregation could enhance the credibility or
perceived informativeness of voluntarily issued management forecasts.
Evidence on the role of forecast disaggregation in the U.S., however, is
inconclusive with other studies suggesting that disaggregated forecasts may be selfserving and less informative to investors. For example, Chen et al. (2009) find that
disaggregated bad news forecasts are significantly less accurate and more optimistically
biased than aggregated earnings forecasts and Han and Wild (1991) find that
disaggregated earnings forecasts disclosed with revenue forecasts are less informative
than aggregated earnings forecasts. Taken together, it is not clear from existing research
whether disaggregated forecasts are more informative for capital market participants.
Accordingly, we derive our first hypothesis, stated in the null, as follows:
Hypothesis 1: The stock market reaction associated with management forecasts does not
vary with the degree of forecast disaggregation.
8
Management forecasts vary not only in the level of disaggregation (i.e., the
number of items included in a forecast), but also in the choice of forecast items.
However, whether different forecast items have different value relevance to investors is
largely unexplored.10 As a result, in this study we also examine whether and how the
stock market reactions vary with different forecast items.
The link between various financial statement line items and stock returns has long
been established in the accounting literature (see, for example, Holthausen and Watts
2001 for a review). 11 In an examination of the informativeness of different earnings
components, Swaminathan and Weintrop (1991) find that equity market participants react
more strongly to revenue surprises than expense surprises during earnings announcement
windows, potentially because revenues surprises are perceived to be more persistent or
because revenue manipulation is easier to detect.12 More recently, Barton et al. (2010)
examine the stock market response to different accounting performance measures using
an international setting and find that the accounting performance measure that investors
value the most for equity valuation varies around the world. However, in contrast to
Swaminathan and Weintrop (1991) who suggest that revenue should be more value
relevant, Barton et al. (2010) find that both sales and net income have relatively less
10
One exception is Wasley and Wu (2006), who show that managers tend to provide cash flow forecasts to
enhance the credibility of good news forecasts. While Barton et al. (2010) investigate the value relevance
of different accounting performance items disclosed on financial statements around the world, they do not
examine the value relevance of forecasts of different performance items.
11
For example, prior research examines the value relevance of earnings (Ball and Brown 1968), losses
(Hayn 1995), accruals and cash flows (Sloan 1996, Barth et al. 1999), revenues (Swaminathan and
Weintrop 1991, Ertimur et al. 2003), and depreciation (Kang and Zhao 2010).
12
In a follow-up paper, Jegadeesh and Livnat (2006) find that the stock market reacts to both revenue and
earnings surprises on earnings announcement days. They further document that in the second part of their
sample period (1996 to 2003), revenue surprises predict higher abnormal returns than earnings surprises.
9
significant association with stock returns then performance measures near the center of
the income statement in most countries (Barton et al. 2010, Figure 1, page 777).
Based on the discussion above, to the extent that sales manipulations are easier to
detect ex-post and represent a less noisy performance measure (Ertimur et al. 2003), one
can predict that management forecasts containing sales forecasts may be associated with
higher stock market reaction than forecast of bottom-line earnings. On the other hand,
following the finding of Barton et al. (2010), it is also possible that sales forecasts could
be valued less or indifferently from the bottom-line earnings forecasts by global
investors. Accordingly, we derive our second hypothesis, stated in the null, as follows:
Hypothesis 2: The stock market reaction associated with management forecasts does not
vary with the forecast items included in the forecast.
Prior literature has long established that expected litigation risk can potentially
reduce managers’ incentives to provide forward-looking disclosures (Graham et al. 2005,
Rogers and Van Buskirk 2009). Prior research also suggests that country-level
institutions, such as the legal environment, differ widely across countries, and could be an
important determinant of the differences in voluntary disclosures, including management
forecasts, across countries (Baginski et al. 2002). However, ex ante, whether and how
country-level legal and regulatory environments affect managers’ forecast disaggregation
and the stock market reactions associated with such forecasts is not clear.
On one hand, in a country where managers face a higher expected threat of
litigation (such as in countries where investors are better protected through stronger
public enforcement and legal environment), forecast disaggregation, which presumably
subject managers to higher litigation risk, could play a more effective role in signaling
10
the credibility of firms’ forecasts. As a result, forecast disaggregation in these countries
could elicit higher stock market reactions, which in turn increases firms’ likelihood to
issue disaggregated forecasts.13
On the other hand, a stronger legal environment imposes costs on issuing
opportunistic management forecasts.
14
As a result, aggregated forecasts may be
sufficiently credible and additional disclosures through disaggregation for signaling the
credibility of forecasts may be less necessary. 15 In addition, given that disclosure
requirements and information environments are generally rich in countries with a strong
legal environment, commitments to an increased level of disclosure, such as forecast
disaggregation, could have limited capital market benefits (Bailey et al. 2006), thereby
reducing firms’ likelihood to issue disaggregated forecasts.
In contrast, from the demand side, in countries with weaker institutions and legal
regimes where managers are more likely to issue self-serving forecasts, investors may
require more information to evaluate the credibility of management forecasts, which in
turn, leads to a higher demand for and greater stock market reactions associated with
disaggregated forecasts. Consistent with this view, Lang et al. (2012) find that financial
transparency matters more when investor uncertainty is greater.
13
Hirst et al. (2007) suggest that by providing disaggregated forecasts, managers signal the credibility of
such forecasts by limiting their ability to manage components of earnings to achieve the forecast. The idea
behind this conjecture is similar to Dutta and Gigler (2002) who analytically show that managers precommit to lower earnings management when they constrain themselves in term of opportunities for
subsequent earnings management.
14
These costs include potential litigation risk and other costs. For example, Lansford et al. (2013) show that
disaggregated forecasts create additional targets which, if missed, are associated with higher stock market
penalties than aggregated forecasts.
15
Consistent with this argument, Ball et al. (2012) show that better quality of financial reporting and
voluntary disclosure is complementary (i.e., higher quality of financial reporting could have the potential to
lend credibility to firms’ voluntary disclosure). Rogers and Stocken (2005) show that managers’ likelihood
of issuing self-serving forecasts is moderated by investors’ ability to detect such misrepresentation.
11
In the end, whether and how the stock markets’ reactions associated with
disaggregated forecasts and managers’ decision to issue disaggregated forecasts vary
across countries with different legal environments is an empirical question that we
examine in this study. Based on the above discussions, we develop our last two
hypotheses, both in null form, as follows:
Hypothesis 3: A country’s legal and regulatory environment has no effect on the
association between stock market reaction and management forecasts with different
levels of forecast disaggregation / forecast items.
Hypothesis 4: A country’s legal and regulatory environment has no effect on the
likelihood of issuing management forecasts with different levels of forecast
disaggregation / forecast items in different countries.
3. Sample and Descriptive Statistics
We obtain a comprehensive sample of management forecasts data from S&P
Capital IQ (CIQ hereafter) that provides the original text of management forecasts for
firms across a large number of countries/regions starting from year 2004 – the first year
CIQ started providing a comprehensive coverage for international firms. According to
CIQ, the raw text forecasts are extracted from various sources, such as newspapers,
regulatory filings, subscriptions and announcements of transactions. We exclude all firmyear observations with missing firm-level control variables and also exclude
countries/regions missing country-level variables. We further exclude Japan because
management forecasts in Japan are de facto mandatory (Kato et al. 2009). Our final
sample consists of 30 countries during our sample period of 2004-2009, representing
8,560 unique firms issuing a total of 60,067 individual management forecasts. 16
16
Since the data-collection process requires extensive resources and effort, our sample ends in 2009.
Examples of disaggregated forecasts can be found in the Appendix.
12
To obtain detailed information on forecast disaggregation and forecast items, we
manually identify and collect all of the performance measures included in each forecast.
Because we are interested in items that are likely to be important to investors globally, we
start with the accounting items identified by Barton et al. (2010), namely (1) SALES, (2)
EBITDA (operating earnings before interest, income taxes, depreciation, and
amortization), (3) OPINC (operating income before income taxes), (4) IBTAX (income
before income taxes), (5) IBXIDO (income before extraordinary items and discontinued
operations), and (6) NI (net income). For completeness, we also identify and add
additional forecast items, which include forecasts of capital expenditure, cash flow,
expenses, and other balance sheet items (such as debt forecast and forecast on short- or
long-term investments). 17 We then code the total number of unique performance
measures included in each forecast as NUMITEMS, where a larger value indicates a more
disaggregated forecast. Similarly, we also code an indicator variable, MULITEMS, which
equals one for each forecast that contains multiple performance measures, and zero
otherwise. 18
Panel A of Table 2 provides the descriptive statistics of our variables of interests by
country. From columns 1 and 2, we observe that 37,268 (3,543) forecasts (forecasting
firms) are from the U.S. representing 62 percent (41 percent) of the worldwide total.19
17
Our results (Table 2) show that there are indeed a non-trivial number of forecasts containing each of
these items.
18
Forecasts often include several related forecast items (e.g., earnings, earnings per share, and earnings
growth). Because the underlying performance measure of such forecast items is the same (i.e., earnings),
these are coded as one unique item. All of the forecast items are coded into one of 10 unique measures
(sales, EBITDA, operating income, pre-tax earnings, earnings before extraordinary items and discontinued
operations, net income, balance sheet items, capital expenditure, cash flow, and expenses). For our full
sample, the average NUMITEMS is 1.56, suggesting that many management forecasts contain more than
one performance measure. The worldwide average of MULITEMS is 47.85 percent, indicating that nearly
half of the management forecasts worldwide are disaggregated forecasts.
19
Our conclusions remain the same with and without the U.S. sample included in our analyses.
13
Other well-represented countries in our sample include Germany (6.1 percent), Australia
(5 percent), the U.K. (3.1 percent), France (3 percent), and Canada (2.5 percent). The
average level of forecast disaggregation (NUMITEMS in column 3) in each country
ranges from 1.11 (Hong Kong) to 1.77 (Greece) and the average percentage of forecasts
that are disaggregated (MULITEMS in column 4) ranges from 9.29 (Hong Kong) to 62.54
(Finland).
The remaining columns in Panel A of Table 2 show the likelihood with which each
item is included in a forecast in each country. Consistent with prior studies, the results
indicate that net income and sales are the two most commonly forecasted performance
measures around the world. Specifically, we find that on average, 68.8 percent of
forecasts include net income (NI in column 10) and about 59.6 percent of forecasts
include sales (SALES in column 5). Because other items are included in forecasts at a
much lower frequency on average, we combine earnings before interest, taxes,
depreciation, and amortization (EBITDA), operating income (OPINC), pre-tax income
(IBTAX), and income before extraordinary items and discontinued operations (IBXIDO),
i.e., the middle four items reported on firms’ income statements, included in a total of
18.11 percent of forecasts worldwide, into a single measure called MID4 in our analysis.
Similarly, we combine the remaining items, included in a total of 8.1 percent of forecasts,
which include balance sheet items (BS), capital expenditure (CAPEX), cash flows
(CASHFLOW), and expenses (EXPENSE) into a summary measure labeled OTHERS. 20
Panel B of Table 2 reports the descriptive statistics of major forecast variables by
industry. The Computers industry is most heavily represented with 10,397 forecasts (17.4
20
These overall statistics are in line with those documented by extant studies. For example, Hirst et al.
(2007, page 814, footnote 2) show that about 71% of forecasts (in the US) contain earnings and revenue
forecasts, with 29% of forecasts containing forecasts of other line items.
14
percent) and with the highest average level of forecast disaggregation measured by the
number of items included in each forecasts (each forecast contains 1.73 items on
average). This finding is consistent with Gu and Li (2007) who suggest that investors
have more credibility concerns regarding the voluntary disclosures made by high-tech
firms. Other well-represented industries include the Services (8.7 percent) and
Transportation (6.9 percent) industries.
The variation in the likelihood that various performance measures are included in a
forecast across industries is also notable. For example, while approximately 86.7 percent
of forecasts from firms in the Computers industry include sales (SALES) projections, only
13.5 and 19.5 percent of such forecasts are issued by firms in the Utilities and Financial
industries, respectively. More than half of firms from all industries tend to forecast net
income (NI), with firms from the Financial and Utility industries most likely do so (88.6
percent and 83.8 percent, respectively). The Extractive industry tends to have the lowest
likelihood of forecasting either sales (28.2 percent) or net income (50.1 percent), but have
the highest likelihood of forecasting capital expenditure (28.8 percent) and/or cash flows
(6.8 percent).
Panel C of Table 2 provides descriptive statistics of major forecast variables across
years. A monotonic increase in the total number of management forecasts across years
suggests that management forecasts is becoming a more important channel of voluntary
disclosure around the world during our sample period of 2004 to 2009. Consistent with
the idea that the recent financial crisis increased firms’ uncertainty regarding their future
performance, we see a decline in sales and net income forecasts in 2009 and an increase
15
in the forecast of liquidity-related measures such as capital expenditures (CAPEX) and
cash flows (CASHFLOW).
Table 3 provides correlations among our variables of interest. The significant
negative correlation between sales and net income forecasts (-0.22 for both Pearson and
Spearman) suggests that globally, firms tend to forecast either one of these two items
rather than both in their forecasts. The larger correlation between NUMITEMS
(MULITEMS) and SALES than that between NUMITEMS and NI also suggests that the
level of forecast disaggregation (the likelihood of issuing disaggregated forecasts) tends
to be more positively associated with sales forecasts than with net income forecasts. On
average, the level of forecast disaggregation is positively associated with the absolute
value of market-adjusted return (ABSCAR) during the [0,1] window, where day 0 is the
management forecast date, indicating that disaggregated forecasts are informative to
investors in general. In addition, the significantly positive (negative) correlation between
sales (net income) forecasts and ABSCAR provides preliminary evidence that investors
value sales forecasts more than net income forecasts.
4. Research Design
4.1 Forecast Disaggregation and Stock Market Reaction
To test our first hypothesis (H1) and examine the potential credibility-enhancing
effect of forecast disaggregation in a global setting, we first investigate the stock market
reaction to the level of forecast disaggregation after controlling for various other forecast
properties and estimate the following OLS regression model:
𝐴𝐵𝑆𝐶𝐴𝑅 = 𝛽0 + 𝛽1 𝑁𝑈𝑀𝐼𝑇𝐸𝑀𝑆 + 𝑂𝑡ℎ𝑒𝑟 𝐹𝑜𝑟𝑒𝑐𝑎𝑠𝑡 𝑃𝑟𝑜𝑝𝑒𝑟𝑡𝑖𝑒𝑠 +
𝑂𝑡ℎ𝑒𝑟 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠 + 𝐶𝑜𝑢𝑛𝑡𝑟𝑦, 𝑌𝑒𝑎𝑟, 𝑎𝑛𝑑 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦 𝐼𝑛𝑑𝑖𝑐𝑎𝑡𝑜𝑟𝑠 + 𝜀
16
(1)
In Equation (1), β1 is our coefficient of interest estimating the relation between the
number of unique performance items included in a forecast (NUMITEMS) and the stock
market reaction to each forecast measured by absolute value of cumulative two-day
abnormal return (ABSCAR).21 We control for other forecast characteristics because prior
research suggests they potentially affect the perceived informativeness of disaggregated
forecasts. These control variables are defined in detail in Table 1 and include: (1) FLOSS,
an indicator variable that takes the value of 1 if a firm forecasts a loss and 0 otherwise,
because Hutton et al. (2003) show that bad news forecasts tend to be more informative
than good news forecasts; (2) FPREC, a categorical variable increasing with the level of
forecast precision, because more precise (e.g., point) forecasts are generally perceived to
reflect greater managerial certainty relative to less precise (e.g., range) forecasts (Hughes
and Pae 2004); (3) FHORI, a categorical variable increasing with management forecast
horizon, because Pownall et al. (1993) suggest that interim and annual forecasts could be
associated with different informativeness; and (4) FATTR, an indicator variable that takes
the value of 1 if a forecast includes either internal or external attribution and 0 otherwise,
because Baginski et al. (2004) find that attributions affect the informativeness of
forecasts made by U.S. firms. Other control variables, identified from prior management
forecast studies, include log assets to control for firm size (LNASSET), the number of
analysts following a firm to control for overall information environment (ANALYST),
whether a firm has a Big 4 auditor to control for audit quality (BIG4), the percentage
holding of institutional investors (IO), whether a firm is in the high tech industry
(HITECH), whether a firm reports a loss (LOSS), the number of exchanges on which a
21
In a robustness test (see Table 8), we also employ a different measure of stock market reaction using
abnormal trading volume surrounding the management forecast date and find consistent conclusions.
17
stock is listed to control for market listings (STKEXCH), and whether a firm is crosslisted in the U.S. as an ADR to control for U.S. listings (ADR). We also include country,
industry, and year indicators in the model and cluster all standard errors by both firm and
year.22
4.2 Which Forecast Items Do Investors Around the World Value the Most?
To test hypothesis (H2) and examine the possible variation in the stock market
reactions to management forecasts containing different forecast items, we augment model
(1) and estimate the following model:
𝐴𝐵𝑆𝐶𝐴𝑅 = 𝛽0 + 𝛽1 𝑆𝐴𝐿𝐸𝑆 + 𝛽2 𝑀𝐼𝐷4 + 𝛽3 𝑁𝐼 + 𝛽4 𝑁𝑈𝑀𝐼𝑇𝐸𝑀𝑆 +
𝑂𝑡ℎ𝑒𝑟 𝐹𝑜𝑟𝑒𝑐𝑎𝑠𝑡 𝑃𝑟𝑜𝑝𝑒𝑟𝑡𝑖𝑒𝑠 + 𝑂𝑡ℎ𝑒𝑟 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠 + 𝐶𝑜𝑢𝑛𝑡𝑟𝑦, 𝑌𝑒𝑎𝑟,
𝑎𝑛𝑑 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦 𝐼𝑛𝑑𝑖𝑐𝑎𝑡𝑜𝑟𝑠 + 𝜀
(2)
In Equation (2), SALES, MID4, and NI are indicator variables for whether a
management forecast contains sales, the four intermediate items on the income statement
(EBITDA, OPINC, IBTAX, and IBXIDO), and net income, respectively. All other
variables are defined as in Equation (1). By including these three indicator variables in
the same model, we treat forecasts containing OTHERS as the benchmark sample.
4.3 Country-level Institutions and the Effect of Forecast Disaggregation
To formally test our third hypothesis (H3), we investigate whether, and how, a
country’s legal and regulatory environments affect the stock market reactions associated
with forecast disaggregation. Specifically, we estimate the following regression model:
𝐴𝐵𝑆𝐶𝐴𝑅 = 𝛽0 + 𝛽1 𝑁𝑈𝑀𝐼𝑇𝐸𝑀𝑆 + 𝛽2 𝑁𝑈𝑀𝐼𝑇𝐸𝑀𝑆 ∗ 𝐶𝑜𝑢𝑛𝑡𝑟𝑦 𝑉𝑎𝑟𝑖𝑎𝑏𝑙𝑒 +
𝐶𝑜𝑢𝑛𝑡𝑟𝑦 𝑉𝑎𝑟𝑖𝑎𝑏𝑙𝑒 + 𝑂𝑡ℎ𝑒𝑟 𝐹𝑜𝑟𝑒𝑐𝑎𝑠𝑡 𝑃𝑟𝑜𝑝𝑒𝑟𝑡𝑖𝑒𝑠 + 𝑂𝑡ℎ𝑒𝑟 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠 +
𝐶𝑜𝑢𝑛𝑡𝑟𝑦, 𝑌𝑒𝑎𝑟, 𝑎𝑛𝑑 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦 𝐼𝑛𝑑𝑖𝑐𝑎𝑡𝑜𝑟𝑠 + 𝜀
(3)
22
For robustness, we also cluster the standard errors by both country and year, or by both industry and year.
In all these settings, the results are quantitatively similar.
18
In Equation (3), Country Variable is alternatively measured by one of three
variables capturing the strength of a country’s legal regime from different perspectives:
1) the public enforcement index (ENFORCE), an index representing the public
enforcement of securities regulation, from La Porta et al. (2006); 2) the investor
protection index (INVPRO), an index capturing country-level disclosure requirement,
liability standards, and anti-director rights for the protection of investors, from La Porta
et al. (2006); or 3) an indicator variable for whether class-action lawsuits are permitted in
a country (CLASSACT) from Leuz (2010). Each of these variables likely captures the
strength of the legal environment in a country with a higher value indicating a higher
expected litigation risk associated with disaggregated forecasts.
4.4 Country-level Institutions and Managers’ Forecasts Activities
To examine whether and how a country’s legal and regulatory environments
affect managers’ likelihood of issuing disaggregated forecasts – measured by the number
of forecast items – and forecast item choice, we estimate the following regression model:
𝑁𝑈𝑀𝐼𝑇𝐸𝑀𝑆 (𝑂𝐿𝑆) 𝑜𝑟 𝑆𝐴𝐿𝐸𝑆/ 𝑀𝐼𝐷4/ 𝑁𝐼/ 𝑂𝑇𝐻𝐸𝑅𝑆 (𝐿𝑜𝑔𝑖𝑠𝑡𝑖𝑐) = 𝛽0 +
𝛽1 𝐶𝑜𝑢𝑛𝑡𝑟𝑦 𝑉𝑎𝑟𝑖𝑎𝑏𝑙𝑒 + 𝑂𝑡ℎ𝑒𝑟 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠 + 𝑌𝑒𝑎𝑟, 𝑎𝑛𝑑 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦 𝐼𝑛𝑑𝑖𝑐𝑎𝑡𝑜𝑟𝑠 +
𝜀
(4)
In Equation (4), in addition to the control variables discussed in Equation (1), we
further include other variables that are potentially related to firms’ decisions to issue
disaggregated forecasts. Specifically, we include global industry competition
(INTCOMP), measured by each industry’s Herfindahl index across all sample countries
in a given year multiplied by (-1) to control for firms’ proprietary costs arising from
international level competition, because theory suggests that proprietary costs are an
important deterrent to management forecasts (Verrecchia 1983). We also include
19
EQUITY, an indicator variable that is equal to 1 if a firm issues equity during a year and 0
otherwise to capture firms’ external financing needs because firms with higher external
financing needs tend to issue better quality voluntary disclosures (Dhaliwal et al. 2011).
We include LEVERAGE to control for the information demand from debt-holders.
We also include the number of geographical segments (SEGMENT) and market-to-book
ratio (MB) to control for firms’ operational complexity and uncertainty perceived by both
managers and investors. Presumably, investors demand better quality voluntary
disclosure when they face greater information uncertainty (Lang et al. 2012). However, at
the same time, managers operating in an uncertain environment have greater difficulty in
providing more disaggregated forecasts. Finally, given that a firm’s forecast behavior
might also be affected by the forecast behavior of its competitors, we include the
percentage of firms issuing disaggregated forecasts (INTFORECAST) across all firms
within the same industry around the world in a given year as an additional determinant of
disaggregated forecasts.23
5. Empirical Results
5.1 Stock Market Reaction and Forecast Disaggregation
5.1.1 Univariate Results
Panel A of Table 4 presents univariate analysis of the relation between stock market
reactions and forecast disaggregation. In particular, it tabulates the total number and
percentage of forecasts by the number of items included within a forecast (i.e., level of
forecast disaggregation), and also reports the average absolute cumulative two-day
23
In robustness, we include analyst forecast error as an additional control in Equation (4) on a sub-sample
of firms with available analyst forecast data. Our results remain quantitatively unchanged.
20
abnormal return (ABSCAR) around the forecast window for three different sample groups.
The first group reports these statistics for all forecasts in our sample (all forecasts); the
second group excludes forecasts that are bundled with earnings releases (standalone
forecasts); and the third group excludes forecasts from U.S. firms (non-U.S. forecasts).
Results show that across these three samples, a large percentage of forecasts are
disaggregated (between 40 and 48 percent). Notably, the stock market reaction to
disaggregated forecasts increases monotonically with the number of items included in the
forecast in all three groups. Thus, the univariate results provide preliminary support to the
possible credibility-enhancing effect of disaggregated forecasts as perceived by investors
from around the world.
5.1.2 Regression Results
Panel B of Table 4 reports OLS regression estimates based on Equation (1) to
formally test H1. Our results consistently show that forecast disaggregation (i.e.,
NUMITEMS) is positive and significantly related to ABSCAR across all three samples (all
forecasts, standalone forecasts, and non-U.S. forecasts), indicating that the stock market
response to a management forecast is stronger when forecasts are more disaggregated. In
terms of economic magnitude, adding one more item to a management forecast increases
ABSCAR between 0.176 and 0.336 percent.
The estimated results on other forecast properties are generally consistent with
findings from prior studies. For example, our result shows that loss forecasts, more
precise forecasts, and forecasts issued with attribution tend to be associated with stronger
stock market reactions. We also find that forecasts with a shorter horizon (i.e. interim
21
forecasts), which tend to be timelier than those with a longer horizon (i.e., annual
forecasts), are associated with stronger stock market reactions.
To better understand the relation between forecast disaggregation and the stock
market response to management forecasts, we further decompose NUMITEMS into the
specific number of unique items included in a forecast. More specifically, we create
indicator variables ITEM_EQ_2, ITEM_EQ_3, and ITEM_GE_4, where ITEM_EQ_2 and
ITEM_EQ_3 are indicator variables that take the value of 1 if a management forecast
includes exactly two and three unique items, respectively, and ITEM_GE_4 is an
indicator variable that takes the value of 1 if a management forecast includes four or
more unique items.
We re-estimate Equation (1) with NUMITEMS replaced by ITEM_EQ_2,
ITEM_EQ_3, and ITEM_GE_4, and report these estimates in Panel C of Table 4.
Consistent with Table 4 Panel B, we find that forecasts containing more performance
items elicit significantly stronger stock market reactions than forecasts containing only a
single performance item (i.e., the benchmark sample of this regression). Taken together,
results from Table 4 reject Hypothesis 1 and provide evidence which is consistent with
Hirst et al. (2007) and support the conjecture of a positive credibility-enhancing effect of
forecast disaggregation.
5.2 Stock Market Response and Specific Forecast Items
5.2.1 Univariate Results
Next, we examine whether stock market reactions vary with forecast items (H2).
In Panel A of Table 5, we tabulate the mean ABSCAR of forecasts by whether they
include SALES, MID4, NI, and OTHERS. We also report the difference in ABSCAR and
22
indicate whether such differences are significant based on a t-test of the difference in
means across the two groups. Results from Table 5 Panel A indicate that forecasts
containing SALES, MID4, or OTHERS, on average, have stronger stock market reactions
than those forecasts that do not contain any of these items while forecasts containing NI
tends to have relatively weaker stock market reactions than those that do not contain NI.
These results suggest that while all forecast items are generally informative to investors,
forecast of sales and other line items above the bottom-line net income are perceived to
be more informative than forecasts of the bottom-line net income in general.
5.2.2 Regression Results
Regression estimates of Equation (2) are reported in Panel B of Table 5.
Consistent with the univariate results, regression estimates presented in Table 5 Panel B
suggest that across all samples, management forecasts are particularly informative when
they include a sales forecast, but relatively less informative when net income is included
after controlling for forecast disaggregation. The coefficient on MID4, i.e., forecasts that
include EBITDA, operating income, pre-tax income, and income before extraordinary
and discontinued items, is positive but insignificant for the main sample, but becomes
significant when the U.S. forecasts are omitted. Likewise, the coefficient on NI becomes
insignificant when the U.S. sample is omitted.24 These results are in line with the findings
of Barton et al. (2010), suggesting that different performance measures could be of
different importance to investors in different countries.
24
We note that NUMITEMS is no longer significantly associated with stock market reaction when we
include forecast items (i.e., SALES, MID4 and NI). In additional analysis, instead of including all three
forecast items together into Equation (2), we include each of these forecast items (i.e., SALES, MID4, and
NI) one by one to examine the effect of forecast items on the estimated coefficient of NUMITEMS. We find
an insignificantly positive association between NUMITEMS and ABSCAR only when SALES is included in
Equation (2). This result suggests that the perceived higher credibility associated with forecast
disaggregation is more likely to be driven by disaggregated forecasts containing future sales projection.
23
5.3 Country-level Institutional Environments and Forecast Disaggregation
Given that forecast disaggregation (and also forecasts containing SALES and
MID4) seems to play an important role in enhancing the credibility of management
forecasts, an important question to examine next is the channel through which forecast
disaggregation achieves such a role. To do this, we examine the effect of country-level
legal environment on the credibility-enhancing effect of forecast disaggregation and
forecast items across countries (H3). Specifically, we interact NUMITEMS and forecasted
income statement items (SALES, MID4, and NI) with the country-level institutions
variables that are likely to capture differences in legal and regulatory environments across
countries (Equation (3)). In particular, we include interaction terms with ENFORCE,
INVPRO, and CLASSACT. 25
These results are reported in Table 6, Panel A. We consistently find a positive and
significant estimated coefficient on the interaction term between NUMITEMS and each of
the three different measures of country-level legal institutions, indicating a greater
credibility-enhancing effect of forecast disaggregation in countries where public
enforcement of securities litigation is stronger, where investor protection is more robust,
or where class-action lawsuits are permitted.
Results of the interactions between our income statement items and the countrylevel legal environment variables are reported in Panel B of Table 6. Consistent with
Table 5 Panel B, we find that when ENFORCE, INVPRO, and CLASSACT are higher,
forecasts that include SALES and MID4 are associated with stronger stock market
responses. However we do not find significant coefficients on NI and its interaction terms
25
We report the results using the sample of all forecasts. Our conclusion remains the same when we reestimate Equation (3) with the other two samples (i.e., standalone forecasts and non-U.S. forecasts
samples).
24
with any of the three country-level variables, suggesting that the stock market reactions
associated with forecasts containing bottom-line net income do not vary with countrylevel legal environment. These results reject Hypothesis 3 and suggest that the strength of
a country’s legal and regulatory regime does have a significant impact on the perceived
credibility of forecast disaggregation and forecasts of specific items, especially when the
forecasts contain projections on future sales or other above-the-bottom line items. Taken
together, our results suggest that it is important to take country-level institutional factors
into account when examining the implication of management forecasts.
5.4 Examining the Possible Determinants of Forecast Disaggregation
Given our findings that forecast disaggregation (and also forecasts containing
SALES and MID4) is (are) associated with stronger stock market reactions, especially in
countries with higher expected litigation risk, one may conjecture that managers from
these countries should be more likely to issue such forecasts to help firms obtain more
capital market benefits. On the other hand, it is also possible that firms tend to refrain
from issuing these forecasts because of a higher litigation cost concern associated with
such forecasts. We test this hypothesis (H4) in Table 7, employing both forecast-level
and country-level estimation.
In Panel A of Table 7, columns 1 to 3 (columns 4 to 6) estimate forecast-level
(country-level) regressions of the relation between NUMITEMS (the percentage of
forecasts that are disaggregated, i.e., the percentage of observations where MULITEMS =
1 for each country) and our legal environment variables. 26 Inconsistent with the
26
Since NUMITEMS is a count variable, we repeat the forecast-level tests using either Poisson or Tobit
regressions. Results are qualitatively similar. In country-level regressions, we replace all variables using the
mean value of each variable for all firm-years within a country. We also multiply the country-level
25
conjecture that firms may be more likely to provide disaggregated forecasts in countries
where disaggregated forecasts are more value-relevant, we find that ENFORCE,
INVPRO, and CLASSACT are all significantly and negatively associated with forecast
disaggregation after controlling for a wide range of variables that likely affect a
manager’s incentive to issue forecasts with different levels of disaggregation, suggesting
that a strong legal environment keeps managers away from issuing disaggregated
forecasts.
Results from our control variables also reveal some interesting observations. For
example, larger firms (LNASSET) and firms listed on more stock exchanges (STKEXCH),
i.e., firms with relatively richer information environments, are less likely to issue
disaggregated forecasts. Firms with larger analyst following (ANALYST) and more
institutional ownership (IO), however, are more likely to issue disaggregated forecasts.
Together, these results suggest firms with more transparent information environments
(e.g., larger firms and firms traded on more exchanges) reduce the need to supply
additional voluntary disclosures, whereas various market participants (e.g., analysts and
institutional investors) are likely to demand such disclosures.
In Panel B of Table 7, we estimate forecast- and country-level regressions of the
relation between SALES, MID4, NI, and OTHERS and country-level legal environment
measured by ENFORCE. 27 We find that despite the potential benefits of forecasts
containing SALES and MID4 items, strong regulatory enforcement is associated with
fewer SALES and MID4 forecasts, but more NI and OTHERS forecasts. The finding that
dependent variable by 100 to convert the country-level mean to percentage of firms issuing disaggregated
forecast or sales forecasts.
27
For simplicity, we only presents the results of ENFORCE. Result using either of the other two alternative
country-level legal and regulatory environment measures (INVPRO or CLASSACT) yields the same
conclusion.
26
OTHERS is positively associated with regulatory enforcement is not surprising, given that
these other forecast items tend to include information that is relatively less likely to drive
managers’ litigation risk concern. Together, these results suggest that the expected
litigation risk associated with management forecasts is a major factor deterring managers
from issuing forecasts that are perceived to be more informative by investors.
Our result also shows that various firm characteristics are associated with the
disclosure of specific forecast items. For example, larger firms are less likely to forecast
SALES and MID4, but such firms are more likely to issue NI and OTHERS. Interestingly,
firms in the high-technology industry (HITECH) and firms whose industry counterparts
worldwide issue more disaggregated forecasts (INTFORECAST) are more likely to issue
SALES and MID4 forecasts, but less likely to issue NI forecasts. When firms report a loss,
they are more likely to issue all four categories of forecast items, consistent with the
notion that uncertain firm performance increases demand for firm-specific information.
5.5 Additional Analysis and Robustness Tests
5.5.1 Additional Control Variables
Hutton et al. (2003) analyze a sample of 278 management forecasts issued
between 1993 and 1997 and find that forecast disaggregation increases stock market
reactions to good news earnings forecasts, but not bad news earnings forecasts. They
interpret these results as evidence that forecast disaggregation increases the credibility of
good news forecasts. In additional analysis, we further add five forecast properties to
Equation (4) to examine whether and how other forecast properties affect forecast
disaggregation. In particular, we add growth forecasts (FGROWTH), an indicator variable
that takes the value of 1 if a forecast is a growth forecast and 0 otherwise, loss forecasts
27
(FLOSS), forecast precision (FPREC), forecast horizon (FHORI), and forecast attribution
(FATTR). Consistent with Hutton et al. (2003), untabulated result shows that growth
forecasts tend to be more disaggregated. In addition, we also find that more precise
forecasts, forecasts with attribution, and forecasts with shorter horizons tend to be more
disaggregated. These results suggest that disaggregated forecasts tend to be associated
with better quality information.
In an additional analysis limited to a subsample of forecasts over an annual
horizon (FHORI =3), in which we are able to identify the actual earnings realization date
(i.e., earnings announcement date), we also include a forecast timeliness variable.
Specifically we add FTIME which refers to the difference in time between the forecast
date and the earnings announcement date into our regression. While we do not find that
forecast timeliness is positively associated with stock market reaction associated with
forecasts, we continue to find a significant and positive association between the variable
of interest, NUMITEMS and ABSCAR.
Prior studies also show that prior forecast accuracy affects the perceived
credibility of current forecasts (Williams 1996). Given the difficulty in matching a
management forecast’s deviation from the actual earnings realization in an international
setting because of the large variation in forecast properties, such as forecast items,
forecast horizon, and forecast precision, we use the average ABSCAR associated with all
forecasts issued by a firm during the previous year of a forecast to proxy for the
perceived forecasts accuracy (FACCU) of the firm’s forecasts. In the 2005-2009 subsample, consistent with prior studies, we find a positive and significant association
between the perceived forecast accuracy and stock market reaction (estimated coefficient
28
= 0.565, t-value =4.27). More importantly, including this variable into our regression
does not quantitatively change our result or conclusion on forecast disaggregation.
Moreover, in additional test, we also control for the possible differences in
earnings expectations of different firms in examining the stock market reaction associated
with forecast disaggregation. Specifically we include the absolute value of the change in
EPS from year t to t+1 scaled by the absolute value of EPS in year t to proxy for investors’
future earnings expectation at year t (ΔEPS). Consistent with higher expected future
earnings is associated with stronger stock market reaction, we find a postive and
significant estiamted coefficient for ΔEPS (estimated coefficient =0.058, t-value =6.40).
However, including this variable does not quantitatively change our results, and we
continue to find a positive and significant association between NUMITEMS and ABSCAR.
5.5.2 Abnormal Stock Market Trading Volume
Our primary analyses examine the relation between ABSCAR and forecast
disaggregation and various forecast items. For robustness, we also re-estimate our main
results by replacing ABSCAR with abnormal trading volume (ABNVOL), defined as the
average trading volume during the two-day forecast window [0,1] scaled by the average
trading volume over the 100-day trading window of [-120,-21]. These results are
presented in Table 8. In particular, we find that abnormal volume around the forecast date
is higher when forecasts are more disaggregated (Model 1), and also increases
monotonically with the number of items included in the forecast (Model 2). We also find
that forecasts of SALES, MID4, and NI are all positively associated with abnormal
volume with SALES (NI) forecasts exhibiting the highest (lowest) abnormal volume
(Model 3). Furthermore, we also obtain consistent results (untabulated) on the effect of
29
country-level legal and regulatory environments on the stock market reaction associated
with forecast disaggregation and specific forecast items (i.e., Sales forecasts and forecasts
of line items above the bottom-line net income) using ABNVOL.
5.5.3 Annual/Fama-MacBeth Regressions
One potential issue with pooling firms across years and countries is that the
significance levels of the regression statistics may be overstated because of serial
autocorrelation (DeFond and Hung 2004). To address this issue and control for the
potential correlation across years, we perform an additional sensitivity test by estimating
Fama-MacBeth regressions. Specifically, we re-estimate our models (Equation 1 and 2)
for each year separately and obtain the mean of the estimated coefficient across the six
yearly regressions. We then divide the mean of the estimated coefficient by the standard
error of the coefficients (Fama and MacBeth 1973) and find results consistent with our
previous findings reported in Tables 4 and 5.
6. Conclusion
Prior studies suggest that management forecast disaggregation has the potential to
alter investors’ judgments. In this study, we examine the stock market reaction to forecast
disaggregation using an international setting and find that investors around the world
generally perceive disaggregated forecasts to be more credible. We also examine the
possible differences in stock market reactions to different forecast items and find that
forecasts containing performance measures that are less aggregated and/or subjected to a
lower likelihood of earnings manipulation (i.e., sales and line items above the bottom-line
30
net income) are perceived by investors to be more informative and thus elicit stronger
stock market reactions.
In addition, we provide evidence on the cross-sectional variation of forecast
disaggregation and show that the effect of disaggregated forecasts on stock market
reaction is conditional on a country’s institutional factors, in particular, factors related to
a country’s legal and regulatory environments. We attribute this finding to a higher
expected litigation risk associated with issuing disaggregated forecasts in these countries.
Further supporting our argument, we find that managers are less inclined to issue
disaggregated forecasts in countries with strong legal and regulatory environments, even
though such forecasts are perceived to be more valuable by investors. In the same vein,
we find that a strong legal environment also deters managers from issuing forecasts
containing sales and line items above the bottom-line net income.
Understanding the variation in forecast disaggregation practices and the
determinants of this variation is important because it not only improves our
understanding of factors that affect the credibility of voluntary disclosure, but also has
important implications for managers and regulators given the important role which
voluntary disclosures play in global capital markets. Our study also responds to Hirst et
al. (2008)’s call for more research on the interaction between forecast characteristics and
forecast determinants.28
Specifically, Hirst et al. (2008, page 317) state that “Because main effect results are unlikely to hold
under all conditions, we argue that researchers should identify and test possible interactions among
antecedents or characteristics. Given the large number of studies looking at main effects, interaction tests
will push forward our knowledge and understanding of such forecasts.”
28
31
32
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35
Appendix: Examples of Disaggregated Management Forecast
Example 1
• Company Name: Ausenco Limited (ASX:AAX)
• Date: May 19, 2009
• Source: PR Newswire
• Ausenco Limited provided earnings guidance for the year ending December 31,
2009. Following a review of business in hand and expected contract awards
through the balance of the year to 31 December 2009, Ausenco is expecting 2009
sales revenue between AUD 475 and AUD 525 million and net profit after tax
between AUD 40 and AUD 43 million.
Example 2
• Company Name: Max’s Group, Inc. (PSE:MAXS)
• Date: July 2, 2008
• Source: PR Newswire
• Max’s Group, Inc. has provided earnings guidance for the year 2008. The
company expects that net income may hit PHP 60 million to PHP 90 million, or
almost 43% over a year ago with consolidated revenues reaching PHP 1.8 to PHP
2.1 billion, or 31.25% higher year on year. System-wide sales would reach PHP
2.2 billion to PHP 2.6 billion by year-end. The company has earnings before
interest, taxes, depreciation and amortization of PHP 330 million.
36
TABLE 1 Variable Definitions
Main Variables
Variable
Definition
NUMITEMS
The total number of unique performance measures contained in a forecast.
MULITEMS
An indicator variable equal to 1 if a forecast contains multiple performances measures, and 0
otherwise.
SALES
An indicator variable equal to 1 if a forecast contains sales, and 0 otherwise.
MID4
An indicator variable equal to 1 if a forecast contains any of the four performance measures
disclosed in the middle part of an income statement (i.e. EBITDA = 1, OPINC = 1, IBTAX = 1
or IBXIDO = 1), and 0 otherwise.
NI
An indicator variable equal to 1 if a forecast contains net income, and 0 otherwise.
OTHERS
An indicator variable equal to 1 if a forecast contains any other forecast item (i.e. Capital
Expenditure, Cash Flow, Expense, or Other Balance Sheet items), and 0 otherwise.
ITEM_EQ_2
An indicator variable equal to 1 if a forecast contains two performance measures (NUMITEMS
= 2), and 0 otherwise.
ITEM_EQ_3
An indicator variable equal to 1 if a forecast contains three performance measures
(NUMITEMS = 3), and 0 otherwise.
ITEM_GE_4
An indicator variable equal to 1 if a forecast contains four or more performance measures
(NUMITEMS >= 4), and 0 otherwise.
ABSCAR
The absolute value of the two-day cumulative market-adjusted abnormal return during [0, 1],
with day 0 as the management forecast date.
ABNVOL
Average trading volume during the firm’s earnings forecast announcement window [0, 1],
scaled by the average trading volume over the 100-day trading window [-120, -21].
Other Forecast Properties
FLOSS
An indicator variable equal to 1 if a firm issues a loss forecast in a given year, and 0 otherwise.
FPREC
A precision score of 1, 2, 3, or 4 is assigned to qualitative, min or max, range and point
forecast, respectively. FPREC is the mean forecast precision score for a firm in a given year.
FHORI
A forecast horizon score of 1, 2, or 3 is assigned to a firm who issues quarterly, semi-annual,
and annual forecast, respectively. FHORI is the average forecast horizon score for a firm in a
given year.
FATTR
An indicator variable equal to 1 if any management forecast issued by a firm in a year is
accompanied by either an internal or external attribution (i.e. provides further explanation of
controllable or uncontrollable reasons for the expected performance), and 0 otherwise.
All Other Variables
LNASSET
The natural logarithm of total assets at the beginning of the fiscal year.
ANALYST
The number of analysts following each firm in each year.
BIG4
An indicator variable equal to 1 if the firm is audited by a Big 4 Auditor, and 0 otherwise.
IO
The percentage of shares (end-of-year) held by institutional investors.
HITECH
An indicator variable equal to 1 if the firm is in a high-tech industry (SIC 2833-2836, 87318734, 7371-7379, 3570-3577, and 3600-3674), and 0 otherwise.
LOSS
An indicator variable equal to 1 if the firm reports a loss, and 0 otherwise.
STKEXCH
The total number of actively traded stock exchanges on which a firm is listed in each year
during the sample period (including the primary stock exchange).
ADR
An indicator variable equal to 1 if a firm is cross-listed on any stock exchanges in the U.S., and
0 otherwise.
ENFORCE
Public enforcement index of five sub-indices on public enforcement of securities regulation
(supervisor characteristics index, rule-making power index, investigative powers index, orders
index, and criminal index) taken from La Porta et al. (2006).
INVPRO
The principal component of three investor protection indices measured by disclosure, liability
standards, and anti-director rights taken from La Porta et al. (2006).
CLASSACT
An indicator equal to 1 if class-action lawsuits are available in a country, and 0 otherwise,
taken from Leuz (2010).
37
TABLE 2 Descriptive Statistics of Forecast Variables
Panel A: Forecast Statistics by Country
1
2
3
4
5
6
7
8
9
10
11
IBTAX
IBXIDO
NI
BS
MID4
Country
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
Australia
Austria
Belgium
Brazil
Canada
Denmark
Finland
France
Germany
Greece
Hong Kong
Indonesia
Ireland
Israel
Italy
Malaysia
Netherlands
New Zealand
Norway
Philippines
Singapore
South Africa
South Korea
Spain
Sweden
Switzerland
Taiwan
Thailand
UK
USA
No. of
Forecasts
No. of
Firms
NUM
ITEMS
MUL
ITEMS
%
3,022
385
301
97
1,521
1,235
985
1,823
3,690
153
700
384
205
119
624
444
457
357
166
261
315
283
419
312
328
764
436
1,180
1,833
37,268
60,067
540
57
64
52
363
128
105
302
459
60
418
107
23
34
134
248
65
70
64
75
172
122
103
78
101
147
96
222
608
3,543
8,560
1.31
1.49
1.54
1.40
1.57
1.67
1.70
1.38
1.65
1.77
1.11
1.29
1.17
1.60
1.68
1.29
1.35
1.29
1.39
1.17
1.29
1.16
1.58
1.63
1.34
1.60
1.38
1.36
1.33
1.62
1.56
25.98
43.90
44.19
34.02
46.55
54.98
62.54
34.01
56.75
60.13
9.29
25.78
15.12
53.78
50.80
27.48
32.39
24.37
33.73
16.09
27.62
14.13
52.03
47.44
31.10
51.31
33.26
33.47
28.21
52.96
47.85
SALES
EBITDA
OPINC
%
%
%
%
%
%
%
25.08
53.51
66.11
61.86
63.05
54.41
76.65
68.73
70.19
58.17
18.43
49.22
11.22
66.39
61.54
56.31
44.86
19.33
59.64
23.75
37.46
13.43
83.53
56.73
52.74
67.80
66.28
54.24
45.44
63.48
59.64
13.10
6.23
17.94
20.62
11.05
6.56
3.96
5.49
9.32
17.65
0.43
1.04
1.95
1.68
28.21
2.03
10.50
14.85
30.12
0.38
1.27
2.47
1.67
31.73
5.49
6.81
1.61
1.27
5.56
6.25
7.06
10.69
27.53
21.93
4.12
4.54
25.18
30.25
21.72
29.27
7.84
1.57
3.13
20.49
4.20
19.87
0.90
16.85
7.00
10.84
0.00
2.86
2.47
14.32
7.37
18.29
28.66
7.57
3.64
7.97
5.41
9.32
2.45
3.64
0.66
0.00
0.92
14.25
1.93
0.27
5.09
9.80
0.57
0.52
2.93
0.84
0.80
0.68
0.66
2.52
2.41
0.00
1.59
0.00
0.24
0.32
3.05
0.39
5.50
0.00
6.16
0.33
1.37
0.17
0.52
0.66
0.00
0.33
1.13
0.81
0.27
0.30
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.28
0.00
2.30
0.00
1.77
0.48
0.64
0.00
0.00
0.00
0.00
0.98
0.36
0.36
75.84
52.73
41.86
40.21
52.07
60.49
50.36
37.90
48.86
78.43
83.14
71.88
77.07
82.35
53.04
66.67
60.18
81.23
28.31
83.91
83.81
90.81
57.04
64.42
49.09
52.75
55.73
75.25
58.48
74.41
68.82
0.33
0.26
1.66
1.03
1.05
0.89
0.51
0.27
0.19
0.00
0.14
0.00
0.49
0.00
2.24
0.23
0.44
0.56
0.00
0.38
0.00
0.00
0.24
0.32
0.00
0.39
0.00
0.17
2.62
0.31
0.42
38
12
13
OTHERS
CASH
CAPEX
FLOW
%
%
1.13
1.04
2.66
9.28
14.00
0.97
2.54
0.55
0.11
0.00
5.29
2.60
0.00
0.84
0.00
1.13
0.88
2.52
4.22
4.98
0.95
2.83
0.48
0.64
2.13
0.39
1.38
0.51
2.78
5.70
4.36
0.93
1.82
0.00
0.00
8.15
1.78
2.23
1.59
0.54
0.65
0.14
0.00
0.00
2.52
0.48
0.45
0.66
0.00
0.60
0.00
0.32
1.06
0.00
0.32
1.22
0.92
0.00
0.08
1.53
2.96
2.35
14
EXPENSE
%
0.33
0.52
0.00
0.00
0.85
0.57
0.41
0.11
0.24
0.65
0.00
0.26
1.46
0.84
0.00
0.00
0.22
0.28
1.20
0.00
0.32
0.00
0.00
0.00
0.30
0.26
0.00
0.34
0.22
1.34
0.95
Panel B: Forecast Statistics by Industry
1
2
3
4
5
6
7
8
9
10
11
12
MID4
Industry
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
Mining/Construction
Food
Textiles/Print/Publish
Chemicals
Pharmaceuticals
Extractive
Manf:
Rubber/glass/etc
Manf: Metal
Manf: Machinery
Manf: Electrical
Equipment
Manf: Transport
Equipment
Manf: Instruments
Manf: Misc.
Computers
Transportation
Utilities
Retail:Wholesale
Retail: Misc
Retail: Restaurant
Financial
Insurance/Real Estate
Services
Others
No. of
Forecasts
No. of
Firms
NUM
ITEMS
13
14
OTHERS
MUL
ITEMS
SALES
EBITDA
OPINC
IBTAX
IBXIDO
NI
BS
CAPEX
CASH
FLOW
EXPENSE
%
%
%
%
%
%
%
%
%
%
%
2,032
1,767
2,867
1,808
2,478
1,214
403
280
420
251
346
325
1.42
1.49
1.57
1.53
1.58
1.32
36.22
40.12
49.25
46.52
51.41
26.69
45.28
44.43
57.87
51.05
69.94
28.17
7.04
6.11
5.79
8.46
3.75
9.80
6.74
11.94
9.14
10.40
7.79
4.78
2.36
1.08
1.53
1.49
0.73
0.16
0.34
0.85
0.38
0.28
0.12
0.00
69.44
75.72
72.34
71.79
67.96
50.08
0.54
0.51
0.45
0.28
1.05
1.15
7.23
5.32
5.79
5.37
2.02
28.83
1.62
1.81
2.58
2.65
2.42
6.75
0.89
0.40
0.87
0.50
1.61
0.74
1,413
221
1.61
54.49
64.47
7.64
7.86
1.42
0.50
71.48
0.71
4.18
1.63
1.06
1,632
2,405
266
304
1.46
1.62
39.89
53.97
47.00
68.11
6.07
3.83
11.52
14.39
1.59
2.00
0.18
0.12
69.18
66.32
0.74
0.12
5.64
4.41
2.45
1.46
0.92
0.62
2,597
336
1.62
54.87
77.63
3.31
12.75
1.12
0.27
61.03
0.23
2.04
1.96
1.08
1,713
199
1.57
47.64
68.07
3.04
12.73
1.23
0.23
62.05
0.58
3.68
3.15
0.64
3,386
558
10,397
4,166
2,215
1,937
3,857
829
3,318
1,235
5,219
1,024
60,067
352
70
1,229
576
235
307
378
68
641
333
747
273
8,560
1.72
1.64
1.73
1.50
1.21
1.55
1.51
1.60
1.25
1.39
1.69
1.39
1.56
64.50
55.73
63.44
39.63
17.11
47.13
43.14
50.78
21.34
31.42
55.82
34.96
47.85
81.66
71.33
86.73
51.13
13.54
56.07
48.17
56.21
19.50
37.49
64.59
45.12
59.64
3.28
6.45
6.41
17.52
9.75
5.89
5.44
3.38
1.45
8.02
13.72
4.79
7.06
10.66
8.78
12.39
9.34
4.20
7.69
5.29
5.19
6.45
4.86
8.12
8.20
9.32
0.83
2.33
0.82
1.75
0.45
1.65
1.22
1.33
2.47
2.75
1.67
2.05
1.37
0.18
0.00
0.29
0.43
0.32
0.46
0.36
0.36
0.45
0.40
0.80
0.49
0.36
70.32
69.00
59.67
56.82
83.79
75.17
77.44
79.01
88.55
72.47
70.78
71.09
68.82
0.47
0.18
0.16
0.38
0.36
0.52
0.36
0.24
0.48
0.40
0.48
0.59
0.42
1.59
3.05
1.90
7.15
5.06
4.29
6.40
6.76
0.69
1.62
3.89
2.83
4.36
1.68
2.15
1.91
2.88
2.48
2.32
2.20
1.81
1.15
7.04
2.74
2.54
2.35
0.74
0.54
1.16
0.96
0.14
0.57
1.09
1.45
1.36
1.30
1.07
0.29
0.95
39
Panel C: Forecast Statistics by Year
2004
2005
2006
2007
2008
2009
1
2
3
4
5
No. of Forecasts
NUMITEMS
MULITEMS
%
SALES
%
EBITDA
%
8,336
7,949
8,725
10,290
11,246
13,521
60,067
1.50
1.48
1.54
1.58
1.57
1.63
1.56
45.71
44.99
48.73
49.01
50.13
47.50
47.85
57.79
60.03
63.74
61.63
63.36
53.29
59.64
2.96
4.32
5.97
7.43
9.50
9.60
7.06
6
7
MID4
OPINC IBTAX
%
%
4.73
4.28
7.19
13.87
9.47
12.93
9.32
40
0.71
0.69
0.68
4.43
0.84
0.75
1.37
8
9
10
11
CAPEX
%
12
OTHERS
CASH FLOW
%
IBXIDO
%
NI
%
BS
%
0.05
0.09
0.07
1.22
0.07
0.50
0.36
81.23
78.12
75.05
61.52
72.34
54.34
68.82
0.29
0.00
0.06
0.45
0.02
1.32
0.42
13
EXPENSE
%
0.07
0.01
0.00
0.12
0.00
19.21
4.36
0.59
0.08
0.40
0.73
0.67
8.68
2.35
0.72
0.06
0.61
2.63
0.06
1.28
0.95
TABLE 3 Correlation Matrix
1
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
ABSCAR
NUMITEMS
MULITEMS
SALES
MID4
NI
OTHERS
FLOSS
FPREC
FHORI
FATTR
LNASSET
ANALYST
BIG4
IO
HITECH
LOSS
STKEXCH
ADR
0.08
0.09
0.11
0.01
-0.03
0.04
0.05
0.07
-0.11
0.02
-0.21
-0.14
-0.08
0.00
0.09
0.07
-0.11
-0.02
2
0.10
0.89
0.56
0.42
0.25
0.19
0.02
0.17
-0.08
0.03
-0.10
-0.02
0.00
0.08
0.11
0.04
-0.05
-0.03
3
0.10
0.97
0.62
0.30
0.27
0.05
0.02
0.17
-0.11
0.02
-0.13
-0.02
-0.01
0.08
0.14
0.06
-0.07
-0.03
4
0.12
0.61
0.62
0.06
-0.22
-0.15
-0.06
0.10
-0.15
0.00
-0.27
-0.04
-0.12
-0.02
0.25
0.09
-0.07
-0.03
5
0.01
0.37
0.30
0.06
-0.32
0.01
-0.04
0.01
0.11
0.01
-0.02
0.00
-0.03
-0.11
0.01
-0.03
0.07
0.04
6
-0.02
0.27
0.27
-0.22
-0.32
-0.21
0.13
0.12
-0.09
0.02
0.11
0.04
0.11
0.16
-0.08
0.05
-0.07
-0.02
7
0.04
0.11
0.05
-0.15
0.01
-0.21
-0.01
-0.01
0.06
0.01
0.06
-0.05
0.05
0.10
-0.04
-0.09
0.01
-0.03
8
0.04
0.02
0.02
-0.06
-0.04
0.13
-0.01
0.00
-0.09
0.04
-0.10
-0.07
-0.03
-0.07
0.06
0.26
-0.03
-0.01
9
0.10
0.17
0.17
0.10
0.01
0.12
-0.01
0.00
-0.14
0.02
0.00
-0.01
0.10
0.20
0.07
0.05
-0.08
-0.04
10
-0.13
-0.10
-0.11
-0.15
0.11
-0.09
0.06
-0.10
-0.14
-0.03
0.06
-0.01
-0.02
-0.21
-0.15
-0.10
0.10
0.06
11
0.02
0.03
0.02
0.00
0.01
0.02
0.01
0.04
0.02
-0.03
0.00
-0.02
0.00
0.00
-0.02
0.00
0.00
0.00
12
-0.20
-0.12
-0.14
-0.27
-0.02
0.11
0.07
-0.11
0.00
0.07
0.00
0.59
0.40
0.34
-0.17
-0.17
0.52
0.09
13
-0.10
0.01
0.00
-0.06
-0.01
0.09
-0.02
-0.07
0.05
-0.04
-0.01
0.59
0.35
0.20
0.07
-0.09
0.50
0.07
14
-0.05
-0.01
-0.01
-0.12
-0.03
0.11
0.05
-0.03
0.10
-0.02
0.00
0.39
0.46
0.35
0.04
-0.03
0.16
0.02
15
0.05
0.08
0.08
-0.02
-0.10
0.15
0.09
-0.07
0.19
-0.20
0.00
0.35
0.33
0.34
0.02
-0.05
0.04
-0.05
16
0.10
0.13
0.14
0.25
0.01
-0.08
-0.04
0.06
0.07
-0.15
-0.02
-0.18
0.06
0.04
0.01
0.11
0.03
-0.02
17
0.06
0.06
0.06
0.09
-0.03
0.05
-0.09
0.26
0.06
-0.10
0.00
-0.18
-0.09
-0.03
-0.06
0.11
-0.10
-0.02
18
-0.09
-0.04
-0.04
-0.06
0.07
-0.06
0.26
-0.03
-0.06
0.07
0.00
0.46
0.38
0.20
0.12
0.06
-0.09
19
-0.03
-0.03
-0.03
-0.03
0.04
-0.02
-0.03
-0.01
-0.04
0.06
0.00
0.09
0.06
0.02
-0.04
-0.02
-0.02
0.19
0.22
Table 3 reports the correlation matrix between our variables of interest. Pearson (Spearman) correlation coefficients are reported below (above) the 45 degree line. Coefficients
in bold indicate that the estimated correlation is statistically different from 0 at better than the 10% significance level.
41
TABLE 4 Forecast Disaggregation and Stock Market Reactions
Panel A - ABSCAR by Number of Forecast Items (NUMITEMS)
1
2
All Forecasts
Standalone Forecasts
Num. of
Forecast
Items
=1
=2
=3
≥4
N
%
31,325
24,549
3,628
565
60,067
52.15
40.87
6.04
0.94
ABSCAR
5.38
6.48
6.63
7.08
N
%
12,811
7,402
1,043
127
21,383
59.91
34.62
4.88
0.59
ABSCAR
5.44
6.61
6.64
9.51
3
Non-US Forecasts
N
%
13,795
7,683
1,191
130
22,799
60.51
33.70
5.22
0.57
ABSCAR
4.52
4.81
4.92
5.84
Panel B – OLS Regression Estimates of the Relation between ABSCAR and NUMITEMS
Dependent Variable =
N (Countries)
N (Total Obs.)
R-square (%)
Intercept
NUMITEMS
FLOSS
FPREC
FHORI
FATTR
LNASSET
ANALYST
BIG4
IO
HITECH
LOSS
STKEXCH
ADR
Country Indicators
Year Indicators
Industry Indicators
SE Clustering
ABSCAR
1
All Forecasts
30
60,067
11.43
2
Standalone Forecasts
30
21,383
14.36
3
Non-US Forecasts
29
22,799
8.20
Coef
t Value
Coef
t Value
Coef
t Value
9.590***
0.262***
0.418***
0.095***
-0.260***
0.344***
-0.559***
0.010***
-0.220***
-0.007***
0.085
0.360***
0.171***
0.289
28.92
5.73
2.62
4.60
-7.62
3.59
-23.54
3.40
-2.53
-5.73
0.46
3.69
5.94
1.40
10.565***
0.336***
0.120
0.242***
-0.391***
0.458***
-0.624***
-0.005
-0.129
-0.012***
0.052
0.949***
0.165***
0.072
20.95
4.00
0.46
6.66
-6.87
2.88
-16.06
-1.18
-0.88
-5.28
0.15
3.62
3.89
0.25
5.054***
0.176***
0.869***
0.181***
-0.163***
0.238*
-0.268***
-0.003
-0.368***
0.006***
1.035***
0.541***
0.135***
0.254
13.55
2.61
3.48
5.58
-2.69
1.74
-8.29
-0.88
-3.09
2.84
3.96
3.11
3.71
1.23
Yes
Yes
Yes
Firm & Year
Yes
Yes
Yes
Firm & Year
42
Yes
Yes
Yes
Firm & Year
Panel C – OLS Regression Estimates of the Relation between ABSCAR and Indicators Representing the
Number of Forecast Items
Dependent Variable =
ABSCAR
1
All Forecasts
2
Standalone Forecasts
3
Non-US Forecasts
30
60,067
11.43
30
21,383
14.37
29
22,799
8.20
N (Countries)
N (Total Obs.)
R-square (%)
Intercept
ITEM_EQ_2
ITEM_EQ_3
ITEM_GE_4
FLOSS
FPREC
FHORI
FATTR
LNASSET
ANALYST
BIG4
IO
HITECH
LOSS
STKEXCH
ADR
Country Indicators
Year Indicators
Industry Indicators
SE Clustering
Coef
t Value
Coef
t Value
Coef
t Value
9.823***
0.319***
0.407***
0.891***
0.416***
0.094***
-0.259***
0.343***
-0.558***
0.010***
-0.219***
-0.007***
0.086
0.362***
0.171***
0.288
30.19
5.25
3.23
2.68
2.61
4.57
-7.58
3.58
-23.45
3.39
-2.52
-5.76
0.46
3.71
5.93
1.40
10.898***
0.345***
0.479**
1.935***
0.117
0.241
-0.391***
0.456***
-0.624***
-0.005
-0.128
-0.012***
0.049
0.955***
0.165***
0.066
22.19
3.21
1.97
2.55
0.45
6.64
-6.86
2.87
-16.04
-1.17
-0.87
-5.28
0.14
3.64
3.88
0.23
5.234***
0.161*
0.383**
0.560
0.869***
0.181***
-0.163***
0.239*
-0.269***
-0.003
-0.368***
0.006***
1.036***
0.541***
0.135***
0.254
14.38
1.86
2.01
1.05
3.47
5.58
-2.69
1.74
-8.30
-0.88
-3.10
2.83
3.97
3.11
3.71
1.24
Yes
Yes
Yes
Firm & Year
Yes
Yes
Yes
Firm & Year
Yes
Yes
Yes
Firm & Year
Table 4 tabulates the relation between the stock market response surrounding management forecasts and forecast
disaggregation. Panel A tabulates the frequency (N) and percentage (%) of forecasts by the categorical variable indicating the
number of items included in each forecast (NUMITEMS). Panel B tabulates the OLS regression estimates of the relation
between NUMITEMS and ABSCAR. Panel C tabulates the OLS regression estimates of the relation between ABSCAR and
indicator variables representing the specific number of items included in each forecast. Standalone forecasts are forecasts NOT
bundled with earnings announcement. Non-U.S. forecasts are forecasts from non-US firms. ***, **, and * indicate that the
estimated coefficients are statistically significant at the 1%, 5%, and 10% level, respectively (two-tailed test). All firm-level
continuous variables are winsorized at the 1st and the 99th percentiles. Country, Industry and Year indicators are included in all
regressions. Standard errors are clustered by both firm and year. All variables are defined in Table 1.
43
TABLE 5 Specific Forecast Items and Stock Market Reactions
Panel A - ABSCAR by Specific Forecast Items
1
2
All Forecasts
Standalone Forecasts
3
Non-US Forecasts
Forecast Item =
Yes
No
diff (Yes - No)
Yes
No
diff (Yes - No)
Yes
No
SALES
MID4
NI
OTHERS
6.513
6.064
5.775
6.806
5.045
5.891
6.242
5.851
1.468***
0.173**
-0.467***
0.955***
6.645
6.250
5.712
6.951
5.119
5.860
6.393
5.853
1.526***
0.390***
-0.681***
1.098***
4.697
4.985
4.562
5.221
4.573
4.515
4.754
4.615
44
diff (Yes - No)
0.124*
0.470***
-0.192***
0.606***
Panel B – OLS Regression Estimates of the Relation between ABSCAR and Specific Forecast Items
Dependent Variable =
ABSCAR
1
All Forecasts
2
Standalone Forecasts
3
Non-US Forecasts
30
60,067
11.43
30
21,383
14.36
29
22,799
8.85
N (Countries)
N (Total Obs.)
R-square (%)
Coef
Intercept
SALES
MID4
NI
NUMITEMS
FLOSS
FPREC
FHORI
FATTR
LNASSET
ANALYST
BIG4
IO
HITECH
LOSS
STKEXCH
ADR
Country Indicators
Year Indicators
Industry Indicators
SE Clustering
9.590***
0.738***
0.152
-0.518***
0.140
0.418***
0.095***
-0.260***
0.344***
-0.559***
0.010***
-0.220***
-0.007***
0.085
0.360***
0.171***
0.289
t Value
28.92
6.60
1.17
-4.58
1.42
2.62
4.60
-7.62
3.59
-23.54
3.40
-2.53
-5.73
0.46
3.69
5.94
1.40
Coef
t Value
Coef
10.565***
0.773***
0.190
-0.510***
0.282
0.120
0.242***
-0.391***
0.458***
-0.624***
-0.005
-0.129
-0.012***
0.052
0.949***
0.165***
0.072
20.95
3.82
0.82
-2.53
1.57
0.46
6.66
-6.87
2.88
-16.06
-1.18
-0.88
-5.28
0.15
3.62
3.89
0.25
5.270***
0.367**
0.568***
0.093
-0.109
0.770***
0.179***
-0.134**
0.238*
-0.304***
-0.003
-0.367***
0.005**
0.975***
0.470***
0.102***
0.223
Yes
Yes
Yes
Firm & Year
Yes
Yes
Yes
Firm & Year
t Value
14.62
2.03
2.99
0.53
-0.68
3.15
5.62
-2.26
1.75
-9.47
-0.85
-3.18
2.37
3.81
2.78
2.84
1.10
Yes
Yes
Yes
Firm & Year
Table 5 tabulates the relation between the stock market response surrounding management forecasts and the inclusion of
specific performance items in a forecast. Panel A tabulates the ABSCAR when a specific performance items is included in a
forecast (Yes) and when it is not (No) and the difference in ABSCAR between these two types of forecasts. Panel B tabulates
the OLS regression estimates of the relation between NUMITEMS and an indicator variable for when sales (SALES), the
middle 4 items on the income statement (MID4), or net income (NI), is included in a forecast. Standalone forecasts are
forecasts NOT bundled with earnings announcement. Non-US forecasts are forecasts from non-US firms. ***, **, and *
indicate that the estimated coefficients are statistically significant at the 1%, 5%, and 10% level, respectively (two-tailed test).
All firm-level continuous variables are winsorized at the 1st and the 99th percentiles. Country, Industry and Year indicators
are included in all regressions. Standard errors are clustered by both firm and year. All variables are defined in Table 1.
45
TABLE 6 Country-level Institutions and the Stock Market Reactions of Forecast Disaggregation
and Specific Forecast Items
Panel A – OLS Regression Estimates of the Relation between ABSCAR and the Interaction of
NUMITEMS and Country-level Institutional Variables
Dependent Variable =
ABSCAR
1
60,067
11.45
N (Total Obs.)
R-square (%)
Coef
2
60,067
11.46
t Value
Coef
3
60,067
11.45
t Value
Coef
t Value
Intercept
11.631***
12.74
11.640***
11.86
5.639***
18.19
NUMITEMS
NUMITEMS * ENFORCE
NUMITEMS * INVPRO
NUMITEMS * CLASSACT
ENFORCE
INVPRO
CLASSACT
FLOSS
FPREC
FHORI
FATTR
LNASSET
ANALYST
BIG4
IO
HITECH
LOSS
STKEXCH
ADR
-0.317***
0.749***
-2.93
5.03
-0.226***
-2.70
-0.116*
-1.65
0.599***
5.51
Country Indicators
Year Indicators
Industry Indicators
SE Clustering
-2.440***
0.405***
0.093***
-0.258***
0.343***
-0.560***
0.009***
-0.221***
-0.007***
0.087
0.357***
0.171***
0.291
0.459***
5.28
3.826***
0.405***
0.089***
-0.252***
0.344***
-0.560***
0.009***
-0.217***
-0.007***
0.091
0.349***
0.181***
0.310
21.27
2.54
4.32
-7.36
3.59
-23.58
3.12
-2.50
-5.78
0.49
3.59
6.34
1.50
-2.53
2.54
4.49
-7.55
3.58
-23.58
3.34
-2.55
-5.71
0.47
3.66
5.94
1.41
Yes
Yes
Yes
Firm & Year
-2.229**
-2.38
0.402***
0.092***
-0.258***
0.342***
-0.560***
0.009***
-0.223***
-0.007***
0.085
0.357***
0.171***
0.289
2.52
4.46
-7.56
3.57
-23.57
3.28
-2.57
-5.71
0.46
3.66
5.96
1.40
Yes
Yes
Yes
Firm & Year
46
Yes
Yes
Yes
Firm & Year
Panel B – OLS Regression Estimates of the Relation between ABSCAR and the Interaction of
Specific Forecast Items and Country-level Institutional Variables
Dependent Variable =
1
60,067
11.52
N (Total Obs.)
R-square (%)
Intercept
SALES
SALES * ENFORCE
SALES * INVPRO
SALES * CLASSACT
MID4
MID4 * ENFORCE
MID4 * INVPRO
MID4 * CLASSACT
NI
NI * ENFORCE
NI * INVPRO
NI * CLASSACT
ENFORCE
INVPRO
CLASSACT
FLOSS
FPREC
FHORI
FATTR
LNASSET
ANALYST
BIG4
IO
HITECH
LOSS
STKEXCH
ADR
Country Indicators
Year Indicators
Industry Indicators
SE Clustering
ABSCAR
2
60,067
11.53
Coef
t Value
Coef
t Value
11.072***
-0.601***
1.244***
12.01
-3.69
5.77
-0.109
0.588**
11.177***
-0.512***
11.30
-4.13
1.083***
6.96
-0.61
2.18
-0.013
-0.09
0.428**
0.155
-0.373
0.90
-1.53
3
60,067
11.50
Coef
t Value
5.609***
-0.190*
17.56
-1.85
0.674***
0.019
5.53
0.16
2.11
0.042
0.32
-0.230
-1.29
0.382**
-0.165
2.39
-1.50
0.047
-1.633*
-1.615*
0.540***
0.103***
-0.251***
0.350***
-0.542***
0.009***
-0.187**
-0.006***
0.070
0.320***
0.158***
0.299
0.34
-1.68
3.36
4.98
-7.34
3.66
-22.68
3.02
-2.15
-5.10
0.38
3.27
5.49
1.45
0.538***
0.102***
-0.248***
0.348***
-0.540***
0.008***
-0.186**
-0.006***
0.072
0.316***
0.155***
0.293
Yes
Yes
Yes
Firm & Year
-1.71
3.35
4.96
-7.26
3.64
-22.56
2.82
-2.15
-5.05
0.39
3.23
5.41
1.43
Yes
Yes
Yes
Firm & Year
3.996***
0.532***
0.098***
-0.245***
0.350***
-0.542***
0.008***
-0.193**
-0.007***
0.077
0.321***
0.167***
0.315
20.80
3.31
4.75
-7.15
3.65
-22.64
2.88
-2.23
-5.26
0.41
3.29
5.87
1.53
Yes
Yes
Yes
Firm & Year
Table 6 reports the regression estimates of the relation between ABSCAR and the interaction between forecast
disaggregation (NUMITEMS, Panel A) or specific forecast items (Panel B) and three variables representing the strength
of regulatory enforcement (ENFORCE), the level of investor protection (INVPRO), or an indicator for whether classaction lawsuits are available in a country (CLASSACT). ***, **, and * indicate that the estimated coefficients are
statistically significant at the 1%, 5%, and 10% level, respectively (two-tailed test). All firm-level continuous variables
are winsorized at the 1st and the 99th percentiles. Country, Industry and Year indicators are included in all regressions.
Standard errors are clustered by both firm and year. All variables are defined in Table 1.
47
TABLE 7 Country-level Determinants of Forecast Disaggregation and Forecasts of Specific Items
Panel A – OLS Regression Estimates of the Relation between Country-level Institutional Variables and Forecast Disaggregation
1
2
NUMITEMS (Forecast-level)
3
4
OLS
OLS
OLS
OLS
N (Total Obs.)
60,067
60,067
60,067
30
30
30
Adj. R-sqr (%)
9.24
9.05
9.11
50.62
55.34
56.95
Dep Var =
Model
Coef
Intercept
ENFORCE
t Value
1.317***
-0.171***
39.16
-10.14
INVPRO
Coef
t Value
1.238***
38.05
-0.072***
-5.17
CLASSACT
Coef
5
% MULITEMS > 1 (Country-level)
OLS
t Value
Coef
t Value
38.87
95.972
-37.657***
1.47
-3.19
1.227***
6
Coef
OLS
t Value
51.714
0.83
-37.210***
-3.03
-0.93
-2.403
-0.74
Coef
t Value
41.003
0.96
-27.350***
-4.71
3.101
1.47
-0.069***
-7.34
LNASSET
-0.009***
-3.52
-0.008***
-3.33
-0.008***
-3.28
-2.975
ANALYST
0.002***
4.91
0.002***
4.82
0.002***
4.84
0.032
0.08
-0.028
-0.07
0.333
0.91
BIG4
0.008
0.83
0.007
0.74
0.007
0.77
-0.004
-0.04
-0.081
-0.72
-0.278**
-2.85
IO
0.002***
13.68
0.002***
12.01
0.002***
12.74
-0.076
-0.31
-0.103
-0.42
0.042
0.22
HITECH
0.045***
2.52
0.042**
2.33
0.043**
2.39
0.237
0.72
0.338
1.02
0.861***
3.04
LOSS
0.103***
10.78
0.101***
10.53
0.100***
10.40
0.416
0.74
1.035
1.71
-0.575
-1.04
STKEXCH
-0.030***
-7.90
-0.027***
-7.00
-0.026***
-6.98
-3.126
-0.44
-1.575
-0.21
-6.138
-1.11
ADR
-0.028
-1.13
-0.024
-0.96
-0.025
-0.98
-3.008**
-2.43
-3.926***
-2.90
-4.410***
-4.04
INTCOMP
0.138***
3.34
0.139***
3.37
0.134***
3.26
1.750*
1.79
0.673
0.72
1.141
1.71
EQUITY
-0.009
-0.64
-0.012
-0.91
-0.010
-0.75
0.571
0.81
0.362
0.53
1.905***
2.97
LEVERAGE
0.014
0.66
0.016
0.73
0.016
0.74
1.681**
2.47
1.297*
1.78
1.213**
2.50
SEGMENT
-0.003*
-1.66
-0.003
-1.46
-0.003
-1.29
3.612
0.78
8.316*
1.94
12.797***
3.62
MB
0.002*
1.63
0.002
1.50
0.002
1.51
3.777
0.82
7.558
1.60
4.278
1.20
INTFORECAST
0.011***
29.23
0.011***
29.06
0.011***
29.25
0.823
1.61
0.080
0.15
0.143
0.36
Year Indicators
Yes
Yes
Yes
No
No
No
Industry Indicators
Yes
Yes
Yes
No
No
No
Firm & Year
Firm & Year
Firm & Year
No
No
No
SE Clustering
48
Panel B - OLS Regression Estimates of the Relation between Country-level Institutional Variables and Inclusion of Specific Items in Forecasts
Dep Var =
1
2
SALES
MID4
3
4
5
6
NI
OTHERS
% SALES
% MID4
(Forecast-level)
Model
7
8
% NI
% OTHERS
(Country-level)
Logistic
Logistic
Logistic
Logistic
OLS
OLS
OLS
OLS
N (Total Obs.)
60,067
60,067
60,067
60,067
30
30
30
30
N (Dep Var =1)
Pesodo/ Adj. R-sqr
(%)
35,823
10,424
41,341
4,362
29.70
17.04
Coef
Pr >
ChiSq
ENFORCE
0.698***
-0.500***
LNASSET
17.89
41.22
Coef
Pr >
ChiSq
Coef
Pr >
ChiSq
0.00
0.00
-1.265***
-2.426***
0.00
0.00
-0.015
1.260***
0.88
0.00
-0.182***
0.00
-0.032***
0.00
0.131***
ANALYST
0.014***
0.00
0.001
0.73
BIG4
-0.380***
0.00
0.113***
0.00
IO
0.003***
0.00
-0.002***
HITECH
0.223***
0.00
0.178***
LOSS
0.265***
0.00
STKEXCH
-0.020**
ADR
-0.128*
INTCOMP
63.21
77.74
37.94
Coef
-7.460***
0.00
-34.172
-0.43
-43.823
-1.47
127.283*
1.79
11.076
1.08
0.00
1.944***
0.096***
0.00
0.00
-34.650***
2.371
-3.45
0.83
-44.301***
0.229
-7.45
0.14
42.337***
-6.273*
3.06
-1.95
-1.454
1.025*
-0.76
2.11
-0.004***
0.00
-0.014***
0.00
0.121
0.33
0.032
0.16
0.188
0.40
-0.125
-1.65
0.161***
0.00
0.480***
0.00
-0.342***
-3.18
-0.078
-1.40
0.232*
1.85
0.022
1.15
0.00
0.005***
0.00
0.008***
0.00
0.198
0.97
-0.310**
-2.42
-0.262
-0.97
0.084*
2.11
0.00
-0.158***
0.00
0.332***
0.00
0.864***
3.21
-0.188
-0.96
-0.567
-1.26
0.099
1.13
0.250***
0.00
0.077***
0.01
0.753***
0.00
0.209
0.42
1.479***
4.47
-0.415
-0.57
0.161
1.46
0.05
-0.001
0.96
-0.112***
0.00
-0.062***
0.00
-17.462***
-2.68
10.154***
2.64
20.574**
2.54
-2.076
-1.77
0.08
0.149*
0.06
-0.094
0.18
-0.667***
0.01
-1.640
-1.23
-1.306*
-1.80
-2.531*
-1.80
0.033
0.15
0.450***
0.00
1.011***
0.00
-0.517***
0.00
0.178
0.47
-0.407
-0.42
-0.093
-0.20
0.478
0.47
0.105
0.69
EQUITY
0.017
0.63
0.192***
0.00
-0.241***
0.00
0.149***
0.01
-0.259
-0.40
1.520***
3.88
-0.919
-1.12
0.367***
2.83
LEVERAGE
-0.359***
0.00
1.115***
0.00
-0.809***
0.00
1.018***
0.00
1.748***
2.73
0.565
1.48
-0.332
-0.44
-0.005
-0.04
SEGMENT
0.016***
0.00
-0.009
0.14
-0.029***
0.00
0.007
0.51
10.185***
2.79
2.958
1.29
-9.129
-1.72
0.526
0.68
MB
0.020***
0.00
0.005*
0.09
-0.011***
0.00
0.009*
0.07
2.574
0.66
13.679***
5.99
-0.831
-0.14
-0.196
-0.21
INTFORECAST
0.037***
0.00
0.025***
0.00
0.001
0.64
-0.005**
0.02
0.913
1.36
0.096
0.34
-0.202
-0.31
-0.268***
-2.70
Intercept
Coef
t
Value
Coef
t
Value
39.30
Pr >
ChiSq
t
Value
Coef
t
Value
Coef
Year Indicators
Yes
Yes
Yes
Yes
No
No
No
No
Industry Indicators
Yes
Yes
Yes
Yes
No
No
No
No
Firm & Year
Firm & Year
Firm & Year
Firm & Year
No
No
No
No
SE Clustering
Table 7 tabulates the regression estimates equating country-level variables and forecast disaggregation (Panel A) or forecasts of specific items (Panel B). Panel A includes countrylevel variables representing regulatory enforcement (ENFORCE), investor protection (INVPRO), and whether class-action lawsuits are available (CLASSACT). Panel B only
tabulates the results for ENFORCE, but estimates from using INVPRO or CLASSACT are consistent. In each Panel, we report the results from both forecast- and country-level
regressions. In country-level regressions, we replace all variables using the mean value of each variable for all firm-years within a country. % (MULITEMS = 1) represents the
percent of forecasts that are disaggregated in each country. Similarly, % SALES represents the percent of forecasts containing sales forecasts. ***, **, and * indicate that the
estimated coefficients are statistically significant at the 1%, 5%, and 10% level, respectively (two-tailed test).
49
TABLE 8 Robustness Test - Forecast Disaggregation and Abnormal Trading Volume
Dependent Variable =
ABNVOL
1
58,709
3.33
N (Total Obs.)
R-square (%)
Coef
Intercept
NUMITEMS
ITEM_EQ_2
ITEM_EQ_3
ITEM_GE_4
SALES
MID4
NI
FLOSS
FPREC
FHORI
FATTR
LNASSET
ANALYST
BIG4
IO
HITECH
LOSS
STKEXCH
ADR
Country Indicators
Year Indicators
Industry Indicators
SE Clustering
4.294***
0.144***
0.012
0.038***
-0.052***
0.134**
-0.220***
0.002
-0.087
-0.001
-0.098
-0.148**
-0.044***
-0.271***
2
58,709
3.33
t Value
14.98
4.91
0.12
2.85
-2.57
2.05
-14.25
1.38
-1.40
-1.42
-0.78
-2.33
-2.78
-3.42
Coef
t Value
4.410***
15.51
0.197***
0.237***
0.290**
4.93
2.68
2.05
0.011
0.037***
-0.051***
0.135**
-0.219***
0.002
-0.086
-0.001
-0.098
-0.147**
-0.044***
-0.271***
Yes
Yes
Yes
Firm & Year
3
58,709
3.33
0.10
2.78
-2.49
2.05
-14.15
1.36
-1.39
-1.47
-0.78
-2.32
-2.79
-3.42
Yes
Yes
Yes
Firm & Year
Coef
t Value
4.270***
-0.067
14.85
-1.46
0.352***
0.288***
0.190***
-0.030
0.035***
-0.050***
0.134**
-0.225***
0.002
-0.087
-0.001
-0.091
-0.141**
-0.042***
-0.266***
6.42
3.65
3.49
-0.28
2.62
-2.46
2.04
-14.14
1.50
-1.41
-1.53
-0.73
-2.23
-2.59
-3.36
No
Yes
Yes
Firm & Year
Table 8 reports OLS regression estimates of the relation between the abnormal trading volume around a forecast
(ABNVOL) and 1) forecast disaggregation (NUMITEMS, column 1), 2) indicator variables for the number of forecast
items (column 2), or 3) indicator variables for the inclusion of specific forecast items (column 3). ***, **, and * indicate
that the estimated coefficients are statistically significant at the 1%, 5%, and 10% level, respectively (two-tailed test).
All firm-level continuous variables are winsorized at the 1 st and the 99th percentiles. Country, Industry and Year
indicators are included in all regressions. Standard errors are clustered by both firm and year. All variables are defined in
Table 1.
50