How do managers selectively disclose? Evidence from book-to-bill
ratios
Kimball Chapman*
Olin Business School
Washington University in St. Louis
Zachary Kaplan
Olin Business School
Washington University in St. Louis
Chase Potter
Olin Business School
Washington University in St. Louis
December 2015
Abstract
We study the determinants and consequences of disclosing book-to-bill (“BTB”), the ratio of
orders received in the quarter (“booked”) to orders shipped in the quarter (“billed”). Orders
received in a quarter can be voluntarily disclosed, but unlike managerial forecasts, do not relate
precisely to a future mandatory disclosure. This makes it challenging for shareholders to identify
strategic disclosure. We find evidence that managers use their discretion to upwardly bias BTB
by (i) withholding bad news BTB ratios, (ii) rounding disclosed BTB ratios upward and (iii)
providing positive qualitative characterizations of BTB. We find that when managers do not
disclose BTB, they forecast earnings more frequently, suggesting that BTB substitutes for other
disclosures. Examining the consequences of BTB disclosures, we find no evidence that
disclosing, withholding or that managers’ qualitative characterizations of BTB generate
mispricing. Our evidence contributes to the debate on how managers shape the information
environment using voluntary disclosure.
*Corresponding author: [email protected]. This paper benefited from helpful feedback from workshop
participants at Syracuse University and the University of Minnesota. We thank Chandra Kanodia, Rich Frankel, John
Donnovon, Josh Madsen and David Harris for comments. The authors gratefully acknowledge financial support
provided by the Olin School of Business.
1
Introduction
A common assumption is that managers would like to make disclosures which positively affect
investors’ opinions of managers and their companies. However, managers face several disciplining
forces that constrain their ability to influence investors’ posterior beliefs. First, managers face
potential sanctions under the fraud statutes for lying. Second, managers are disciplined by the
extent to which the information they disclose is reconciled to current or future financial statement
items. For example, a manager otherwise inclined to optimistically bias an earnings forecast may
not do so because the manager may be disciplined ex-post when future earnings are reported.
Third, investors discipline managers for withholding disclosure (or providing imprecise
disclosure) by inferring bad news from non-disclosure (or imprecise disclosure). A prior literature
on voluntary disclosure, focused on managerial earnings forecasts, has generally found that
managerial forecasts convey bad news, contrasting with the presumed incentive to disclose good
news. We investigate the determinants and consequences of voluntary disclosure in a setting
where, in contrast to the managerial forecast setting, the information disclosed cannot be
reconciled to items reported in future financial statements.
Specifically, we examine the voluntary disclosure of “book-to-bill,” the ratio of orders received
in the current quarter (“booked”) to orders fulfilled during the current quarter (“billed”). A high
(low) ratio indicates that new orders arrive faster than shipments, implying higher (lower) expected
future earnings and revenue. Book-to-bill (“BTB”) has a number of unique properties relative to
other voluntary disclosures. First, while the denominator of the ratio (fulfilled orders) is fully
revealed in financial statement items, the numerator (booked orders) is not. Second, BTB is a ratio
so it is comparable cross-sectionally and inter-temporally. Third, because managers know the
value of orders received during the quarter, BTB is a historical fact at the time of disclosure, not a
1
prediction of an uncertain future outcome. This allows us to rule out any correlation between firm
performance and uncertainty as to whether the manager has access to the information as an
explanation for non-disclosure (Dye 1985).
We begin our analysis by investigating whether managers disclose BTB strategically, which we
define as imparting a positive spin or bias to disclosed BTB information relative to the BTB
information managers privately observe.
We document strategic disclosure along several
dimensions. First, eighty-eight percent of disclosed book-to-bill ratios are equal to or exceed one.
As over the life of the firm orders booked must equal orders billed, this high proportion of reported
ratios equal to or above one suggests managers withhold BTB ratios below one. Second, we
document that managers strategically round BTB ratios. Managers disclose ratios that cannot be
rounded up (numbers ending in ‘1,’ ‘2,’ ‘3,’ or ‘4’) more frequently than those which can be
rounded up (numbers ending in ‘6,’ ‘7,’ ‘8’ or ‘9’). This suggests managers use their discretion to
round when doing so increases the reported BTB ratio. Third, we demonstrate that when managers
withhold BTB, future revenue and earnings grow at a lower rate than when managers disclose the
ratio which is consistent with managers possessing negative private information about future
earnings when they do not disclose. Fourth, we demonstrate that managers use more positive
words and fewer negative words to characterize BTB disclosures than earnings disclosures,
suggesting managerial disclosure discretion allows managers to characterize BTB more positively
than mandatory disclosure items.
Next, we investigate the frequency of non-strategic imprecise disclosure, which we define as
lowering the precision or withholding privately observed BTB information without doing so to
impart a positive spin or bias to disclosed BTB information. We would expect the ratio of nonstrategic imprecise disclosure to be proportional to the ratio of strategic imprecise disclosure
2
because non-strategic imprecise disclosure decreases the ability of investors to infer managers’
private information from the imprecise signal. An inability to infer the manager’s private
information from withholding increases the range of values for which managers can inflate
investors’ posterior beliefs through imprecise disclosure (Grossman 1981; Dye 1985). Thus,
conditional on finding evidence of strategic imprecision we would also expect to find non-strategic
imprecision. However, there are a number of reasons we would not predict non-strategic imprecise
disclosure in our setting. First, it is unclear whether the frictions alleged to explain withholding
disclosure could also explain imprecise disclosure. For instance, for proprietary costs to explain
imprecise disclosure, proprietary costs would have to be sufficiently strong to deter precise
disclosure, but not strong enough to lead to complete withholding (Verrecchia 1983). Second,
models of information acquisition predict that when managers have incentives to align investors’
information with their private information,1 managers benefit from disclosing the most precise
signal possible even when managers also have incentives to bias (Crawford and Sobel 1982). This
result suggests managers have an incentive to make precise disclosures.
We document that managers withhold BTB or provide imprecise BTB disclosures frequently.
First, thirty percent of firms who disclose a BTB ratio last quarter do not disclose one this quarter.
Second, over half of all disclosed BTB ratios have a zero in the hundredths place, which is
consistent with managers rounding BTB to make the disclosed information less precise.2 Third,
Managers could benefit from aligning investors’ beliefs with the underlying value of the firm by improving liquidity
and/or cost of capital.
2
In untabulated analysis we find that 39% of BTB ratios are reported as a whole number, 25% at the tenths place and
36% at the hundredths place.
1
3
managers disclose a qualitative characterization of BTB while withholding the ratio in one-sixth
of BTB disclosures.3
Given the theoretical uncertainty as to the forces leading managers to characterize BTB, we
conduct tests on the determinants and consequences of qualitatively characterizing BTB. First, we
examine whether qualitative characterizations of BTB convey more information about future
earnings and revenue when the manager also discloses a BTB ratio. If managers withhold the BTB
ratio in order to allow themselves to inaccurately characterize BTB, we would expect
characterizations in the absence of a BTB ratio to provide less information about future
performance. Alternatively, we might expect qualitative characterizations to convey similar
information in the presence or absence of BTB if managers withhold the BTB ratio when it will
mislead investors, because it mischaracterizes the managers’ private information about future
sales.
For instance, managers have information about negotiations with customers which will
likely convert into future sales, but have not yet been “booked” because the contracts have not yet
been signed. In this situation, withholding the BTB ratio and supplying a qualitative ratio could
increase the quality of the information flowing to the market by removing a misleading signal.
Consistent with managers using qualitative characterizations of BTB to accurately signal, rather
than to obfuscate, we find that qualitative disclosures with and without BTB ratios convey similar
information about future revenue and earnings.
Second, we examine whether managers withhold the BTB ratio more when they positively
characterize BTB news. We find managers negatively characterize BTB more frequently when
3
We regard quantitative ratios as verifiable, in the sense that a booking is an agreement to provide goods and services,
and those agreements could potentially be subpoenaed as part of a court proceeding. In contrast, several qualitative
characterizations such as “strong” or “healthy” can be true of nearly any BTB value while others, such as “improved”
could be true of a wide range of BTB values.
4
they do not disclose the ratio, which is additional evidence that qualitative characterizations
provide information to, rather than conceal information from, the market.
Although managers often withhold BTB information from the market, the withholding does not
in and of itself imply these disclosure choices degrade the information environment. Managers
could compensate for the non-disclosure of BTB by increasing disclosure of other information.
We document in firm-fixed effect regressions that when managers withhold BTB they increase
their provision of other disclosures. First, when managers do not disclose BTB, they are more
likely to disclose a managerial forecast. Second, when managers do not disclose BTB, their
conference calls and earnings press releases are longer (statistically insignificant). Third, we
demonstrate that when managers do not disclose BTB they discuss earnings less. The second and
third finding collectively raise the possibility that managers substitute away from a standard review
of quarterly performance, historical earnings and customer orders, when they feel they have more
important information to disclose.
Lastly, we examine whether prices reflect the information in BTB and in qualitative
characterizations of BTB during the announcement window or whether they generate drift. To the
extent that BTB and qualitative characterizations of BTB are uniquely informative leading
indicators of future sales, we expect investors will price these disclosures immediately. Our results
are consistent with this prediction; we find several disclosures have a significant reaction during
the announcement window, consistent with the market pricing BTB disclosures. If managers
succeed in misleading the market as a result of the positive bias in BTB disclosures, we expect to
observe return reversals. In contrast to this prediction, we find no evidence that either (i) the value
of the disclosed ratio, (ii) the decision to withhold/disclose a ratio or (iii) qualitative
characterizations of BTB predict future returns.
5
Our findings contribute to prior literature documenting conditions under which managers
prioritize voluntary disclosures containing good news or bad news. Our finding that managers tend
to withhold bad news BTB disclosures provides empirical support for the common assumption
that managers disclose to increase the share price. Our findings contrast with evidence from the
managerial forecast setting where: (1) managers tend to disclose negative news (Skinner 1994;
Kaznik and Lev 1995) and (2) withholding a forecast does not trigger a stock price decline (Sletten
2012). One feature of managerial forecasts, which might lead to different results in the forecasting
settings is that after disclosure, the forecast becomes a benchmark used to evaluate the performance
of the manager (McNichols and Trueman 1994). This could cause managers to use their discretion
to convey bad news and withhold good news, or bias the forecast downwards (Skinner and Sloan
2002; Richardson et al. 2004; Graham et al. 2005; Mergenthaler 2012). In contrast, although BTB
is correlated with future revenue and earnings, it never becomes an explicit benchmark that can be
used to evaluate the manager.
We document several findings, such as the frequency of imprecise disclosure, which map less
well into the extant theoretical literature. One economic force that could potentially explain
imprecise disclosure is that it aids managers in their stewardship of the firm. This could arise
because either (i) external shareholders are reticent to mount a campaign to replace the manager
without precise information (Gentzkow and Kamenica 2012) or (ii) because imprecise information
increases the ability of managers to consume perquisites (Nagar et al. 2003). Another potential
force is that managers feel constrained to make only a certain number of disclosures during
conference calls. If present, a constraint on the quantity of disclosure creates an opportunity cost
of disclosing BTB. The opportunity cost varies with the number of firm activities the manager
would like to report, leading to non-strategic non-disclosure.
6
2
Hypothesis development and literature review
In this section, we first discuss some background information and prior research on the
disclosure of information similar to BTB such as agreements to exchange goods and services for
cash at a subsequent date. Second, we review the theoretical literature on voluntary disclosure.
We focus our discussion on two strands of literature: (i) the decision to disclose or withhold a
verifiable signal, which relates to the managers’ decision to disclose BTB and (ii) the
circumstances in which managers can credibly disclose soft information, which relates to the
decision to disclose qualitative or imprecise book-to-bill information. Third, we discuss empirical
voluntary disclosure studies. Fourth, we develop our hypothesis.
2.1
Background
When the dollar value of goods a firm has pledged to provide to customers is material, SEC
regulations require the disclosure of “order backlog” in the footnotes (Rajgopal et al. 2003).
Backlog is commonly defined as “customer orders that have been received but are either
incomplete or in the process of completion.” In contrast to backlog, which refers to the stock of
customer orders, “book-to-bill” refers to the flow of orders -- the dollar value of orders booked
divided by the dollar value of orders shipped during the current quarter. Because “book-to-bill”
is a voluntary measure, companies use a variety of definitions to calculate it. Extracting the
definitions of book-to-bill from the 10-K footnotes for those firms that disclose it, we find that
many companies define bookings as only covering sales expected to occur over the next twelve
months whereas backlog includes longer duration sales as well. As a result, book-to-bill will have
more information about shorter horizon revenues and will not be affected by shifts in the horizon
over which firms’ contract. Eighty-six percent of book-to-bill disclosures come from firms who
do not have order backlog disclosures in Compustat.
7
Prior research shows that order backlog has information about future sales (Lev and
Thiagranjan 1993) and that the change in order backlog negatively predicts future returns
(Rajgopal et al. 2003). A current working paper shows the market reacts positively to change in
order backlog (Feldman et al. 2014). Our search found a single prior paper on book-to-bill
disclosures, which focuses on industry-level disclosure of book-to-bill information; Chandra et al.
(1999) show the disclosure of industry-wide BTB information increases the volatility of daily
returns and causes analysts to revise forecasts.4
2.2
Voluntary disclosure theory
In this section, we discuss two types of disclosure models which relate to the disclosure choice
of managers: (i) voluntary disclosure models in which a manager decides to disclose or withhold
a verifiable signal and (ii) cheap-talk models in which the manager chooses a non-verifiable
message to transmit to an investor and the investor makes a decision based on the message.
When a manager can disclose or withhold a verifiable signal to investors, investors will infer
information from the signal if the manager discloses or the absence of disclosure if the manager
withholds. The inference investors will make in the absence of disclosure depends on the
manager’s incentives. If the manager’s utility increases with price, the manager’s strategy will be
to disclose all news which increases price. If there are no frictions which prevent the manager
from disclosing, an unraveling occurs in which the manager fully reveals the signal because the
market infers the worst news from the absence of disclosure (Grossman 1981; Milgrom 1981).
However, if the manager cannot make good news disclosures with a positive probability, the
market participant cannot distinguish strategic withholding from non-strategic withholding. Non-
4
Chandra et al. (1999) report the trade association discontinued the disclosure of industry-wide BTB information in
1997.
8
strategic withholding makes strategic withholding incentive compatible for some managers, and
the rate of strategic withholding will be proportional to the rate of non-strategic withholding (Dye
1985). The frictions most commonly hypothesized to prevent full disclosure are (i) uncertainty as
to whether the manager possesses the information (Dye 1985), (ii) proprietary costs of disclosure
(Verrecchia 1983) and (iii) managers’ incentives to withhold information because disclosure limits
perquisite consumption (Nagar et al. 2003). If the frictions are serially correlated within a firm,
the market will infer the magnitude of the friction from prior disclosure decisions (Einhorn and
Ziv 2008).
An alternative setting where disclosure has been studied is one in which the manager can
communicate to the market costlessly and the manager can disclose truthfully or lie to the market
(“cheap-talk”). In a cheap talk setting, the ability of managers to communicate with the market
will depend on the managers’ incentives. If the manager only has incentives to maximize the share
price, he will issue the most optimistic signal in all circumstances. The “signal” will not reveal
any information to market participants, as the manager will make the same disclosure regardless
of his private information. If the manager only has incentives to align investors’ expectations with
the managers’ private information, any statement the manager makes will be credible because he
has no incentive to lie (Ajinkya and Gift 1984). If the manager has incentives to both (i) maximize
the share price and (ii) align investors’ expectations with the managers’ private information, the
manager cannot communicate to the market a credible precise signal.5 However, there are
equilibria in which investors update their expectations in response to imprecise managerial
The reason precise communication is impossible is if the manager’s message always revealed the true state and the
investor believed him, then the manager would have the incentive to exaggerate the state (in state θ he would report
bias + θ). If the manager’s message revealed the true state with bias and the investor had rational expectations, the
investor would unbias the manager’s message before valuing the firm. If the manager knew the investor would unbias
his communication, he would increase the bias in his message further (Crawford and Sobel 1982). Because managers
and investors will never agree on the amount of bias, precise communication is impossible.
5
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disclosures (Crawford and Sobel 1982). All revealing managerial disclosures are imprecise and
provide only a range of possible outcomes. The manager’s utility decreases with the width of the
disclosure ranges – the manager would be better off if he could make precise disclosures, but he
cannot credibly do so because of his incentive to bias. For this reason, the manager’s utility would
increase if he were able to opt out of a cheap-talk setting and into a setting where he could make
verifiable disclosures.
The equilibrium strategy of the manager in a cheap-talk setting will change if the market can
discipline the manager ex-post (Milgrom and Roberts 1986). Two economic forces can reduce the
incentive of the manager to mislead ex-ante when investors can verify disclosure ex-post: (1) the
investor can extract a penalty from the manager for misleading information and (2) the investor
can discipline the manager by not believing his communication in the future.
2.3
Empirical voluntary disclosure studies
Prior empirical literature documents various determinants and consequences of voluntary
disclosure. Most studies in this literature focus on the managerial forecast setting. Ajinkya and
Gift (1984) show that managers issue forecasts more frequently when market expectations deviate
from actual earnings. Studies also show that managers tend to forecast bad news more frequently
(Skinner 1994; Krasznik and Lev 1995). It is unclear whether this result occurs because managers
wish to accelerate negative news into the share price or because managers want to affect market
expectations, enabling them to subsequently disclose good news by beating market expectations
(Richardson et al. 2004; Graham et al. 2005). Research also shows that (i) managers strategically
vary the precision of their news, (ii) strategically bias their forecast upward when the market cannot
detect bias (Rodgers and Stocken 2005) and (iii) bundle managerial forecasts with earnings reports
(Anilowski et al. 2007; Rodgers and Van Buskirk 2013).
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2.4
Hypothesis development
In this section, we develop predictions for when managers will strategically disclose and the
consequences of strategic disclosure. We formulate our predictions combining the specific
features of the BTB setting with the extant empirical and theoretical literature. We first discuss
managers’ incentives to provide good news to the market and how these incentives are likely to
influence managers’ behavior given the unique characteristics of the BTB setting.
Managers have incentives to maximize the share price of their firms (Dye 1985) and therefore
have incentives to disclose information that will increase investors’ posterior beliefs about
managers and their firms. Thus, we expect managers will positively bias their BTB disclosures.
We expect that positive bias may arise in various forms including withholding negative BTB ratios,
positively characterizing BTB ratios or rounding up BTB ratios.
H1a: Managers disclose (withhold) good (bad) BTB news and when they have the
opportunity, positively bias their BTB disclosures
An important force disciplining strategic disclosure, is the market’s ability to infer the private
information of the manager from non-disclosure. If all managers are strategic in their disclosure
then the market will infer from an absence of disclosure that the manager privately observed the
worst signal (Grossman 1981; Milgrom 1981). Even a manager with the second worst signal, will
disclose to distinguish himself from a manager with the worst signal. A sufficient frequency of
non-strategic withholding or imprecise disclosure is a necessary condition for strategic nondisclosure to be effective at disguising bad news. Thus, we predict that if we observe strategic
imprecise disclosure we will also observe non-strategic imprecise disclosure.
H1b: If we find evidence of strategic withholding or imprecision, we will also find evidence
of non-strategic withholding or imprecision
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Next we analyze whether managers provide qualitative descriptions of BTB rather than ratios
to mislead investors or to align investors’ beliefs with managers’ private information. While BTB
ratios are verifiable, qualitative descriptions of BTB often are not. For instance, many values of
BTB investors would consider negative news could be characterized as “strong,” a word with
positive connotations. If managers provide qualitative descriptions of BTB instead of an actual
ratio in order to put a positive spin on their private information, we would expect characterizations
in the absence of a BTB ratio to provide less information about future performance.
Alternatively, if managers have incentives to align investors’ beliefs with the managers’ private
information, managers might withhold the BTB ratio when it does not characterize their private
information. The value of the BTB ratio will not include in-process negotiations, which will affect
the future revenues the firm expects to realize. Because the BTB ratio reflects the manager’s
private information with noise, when the noise is large managers may improve the information
environment by withholding the BTB ratio and qualitatively characterizing their private
information. Ex-ante, we are unsure as to which economic force will dominate, so we express our
next hypothesis in null form.
H2: Managers’ qualitative descriptions in the absence of a BTB ratio align (do not align)
investors’ beliefs with managers’ private information.
We also investigate whether managers adjust other disclosures based on the decision to disclose
or withhold BTB. Ceteris paribus, if managers withhold the BTB ratio, they decrease the
information in price. The decrease in the information in price potentially diminishes liquidity
and/or increases cost of capital (Glosten and Milgrom 1985; Amihud 2002). Managers can
potentially supply another disclosure to the market conditional on withholding the BTB ratio to
decrease the impact of withholding on the information in price.
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H3: BTB disclosures substitute for earnings guidance
Finally, we consider the capital market consequences of BTB disclosures. How quickly the
market impounds BTB disclosures into stock prices depends on whether bias in BTB disclosures
is detected and unraveled by investors (which would predict an immediate reaction with no
subsequent price reversal). In an efficient market, market participants will quickly adjust their
expectations in response to the presence or absence of all information including possible positive
bias in BTB. Contrary to this prediction, recent research finds a number of settings where
disclosures generate post-announcement returns (an example would be post-earnings
announcement drift). We expect that investors will adjust for bias in BTB disclosures such that
information in BTB will be impounded into stock prices during the earnings announcement
window and not generate post-announcement drift. This leads to our third hypothesis:
H4: Book-to-bill disclosures are impounded into price at the announcement window and are
not associated with post earnings announcement returns
3
3.1
Sample and descriptive statistics
Sample construction
We collect managers’ provision of book-to-bill ratios from earnings conference calls and
earnings announcements. We hand collect all available earnings conference call transcripts from
the CQ FD Disclosure news service for the years 2003 (the earliest year transcripts are available)
through 2013 (which was the last full year available at the time of our data collection). We then
collect all earnings press releases from EDGAR. We require each firm-quarter in our sample to
have both an earnings conference call and earnings press release. We also require data from
Compustat and CRSP for each observation leaving us with an initial sample of 93,941 firm-quarter
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observations between 2003 and 2013. We further refine our sample by limiting it to the firms
where book-to-bill disclosures are relevant. For many firms disclosing book-to-bill is not an
option, because they do not enter into forward-looking contracts in their normal course of business.
To avoid noise from firms that do not have the option to disclose book-to-bill, we limit our sample
to the 480 firms that disclose at least one book-to-bill ratio during our sample period. We include
all available firm-quarter observations for these 480 firms, resulting in a final sample of 14,363
firm-quarter observations.
We collect book to bill disclosures by using textual analysis software to search across
conference call transcripts and earnings announcements for following terms: “book to bill”, “bookto-bill”, “bb ratio”, “book over bill”, “bo to bi ratio.” This search yields 30,706 observations from
conference call transcripts and 3,027 observations from earnings announcements. Second, we
build an algorithm consisting of 21 distinct rules using regular expressions in textual analysis
software in order to collect the numeric value of the ratio disclosed and any qualitative descriptions
of the ratio provided by managers. The first function of the rules is to convert common word
patterns in book to bill disclosures into standard formats and to convert numbers in word format
into numerical format. For example, the unaltered text “our book to bill ratio was one point one”
would be converted into “our book-to-bill ratio was 1.1”. Next, the rules collect the numeric value
of the ratio along with the presence of common modifiers of the ratio, such as “greater than 1” or
“>1”. In detecting the book to bill ratio, we take care to distinguish it from other numbers that may
be presented in the same sentence. In order to analyze the qualitative descriptions of book-to-bill
disclosures, our collection algorithm also identifies whether BTB sentences contain positive or
14
negative qualitative descriptions.6 We use this approach to later construct our measures of
managers’ qualitative characterizations of book-to-bill performance.
Our algorithm also uses several unique features of the book-to-bill ratio in order to collect the
ratio as accurately as possible. The collection rules utilize the fact that the book-to-bill ratio is
commonly transcribed as a non-integer ratio (for example “1.05”) while other numbers that
typically appear near BTB phrases are integers or are preceded by a dollar sign (for example “$60
million dollars”). We extract the non-integer ratio not preceded by a dollar sign appearing closest
to the BTB phrase, and assume that is the BTB ratio disclosed. One limitation of our algorithm is
that some book-to-bill numbers will reflect a specific product or segment, rather than the company
as a whole in cases in which multiple BTB ratios are disclosed.
We take several steps to validate our collection algorithm. First, we identify 135 observations
of book-to-bill ratios that seem unusually high (above 2.0) or low (below 0.7) and set these aside
for manual verification. We read each of these and determine whether (i) the firm provided a book
to bill ratio (or whether our algorithm incorrectly identified another number as the book to bill
ratio) and (ii) if it was provided, whether the book to bill ratio was accurately captured by our
algorithm. We find that our algorithm correctly captured the book-to-bill ratio in 61% of these
cases. The vast majority of errors made by our algorithm were cases in which the firm used another
non-integer ratio in the same sentence as the book-to-bill ratio, for example (“our book-to-bill ratio
was 1.1 over the past 2.7 months”). We update our sample to exclude cases in which the ratio was
Positive words are: “strengthening, improving, improved, well over, increased, exceed, exceeded, above, good,
strong, pleased, happy, positive, outstanding, healthy, impressive”. Negative words are: “deteriorating, deteriorate,
negative, shrunk, shrink, decreased, declining, below, weakening, weakened, bad, weak, disappoint, disappointing,
not good, underwhelming, negative”.
6
15
not collected and we manually update observations to the correct book to bill ratio provided by
managers in these cases.
As a second step to validate our collection algorithm, we read 200 observations from the nonextreme partition of book-to-bill sentences. We find that our algorithm correctly identifies bookto-bill disclosures and captures the book-to-bill ratio in 93.5% of these cases.
3.2
Descriptive statistics
Table 1 provides descriptive statistics. A book-to-bill disclosure is provided in 26.4% of firm-
quarters (Book-to-Bill Indicator). Conditional on disclosure, the average ratio is 1.128 (Book-toBill Ratio), which is consistent with managers providing the ratio more often when the ratio is high
(we would expect that in the absence of opportunistic disclosure the mean book-to-bill ratio would
be closer to one, or perhaps 1.03, the approximate growth rate of GDP).
We also provide descriptive statistics for the qualitative descriptions managers use to modify
BTB disclosures. The manager negatively characterizes BTB in 4.2% (Negative Description) of
firm-quarters, significantly less than the 14.9% (Positive Description) of firm-quarters in which
the manager positively characterizes BTB.7
Panel B provides a correlation matrix of variables used in our empirical analyses. Consistent
with the notion that the book-to-bill ratio is a leading indicator of firm performance, the BTB ratio
is positively associated earnings announcement returns (Ann. Return), change in future revenue (Δ
Fut Revenue) and change in future earnings (Δ Fut Earnings). Panel C describes the frequency of
book-to-bill disclosures across our sample by Fama-French 12 industry classification. Business
7
If the manager uses both positive and negative words to modify a BTB phrase, we subtract the number of negative
words from the number of positive words. If there are more positive (negative) words we set the positive (negative)
dummy to one and the negative (positive) dummy to zero.
16
equipment and manufacturing firms provide book-to-bill disclosures at the greatest frequency.
Panel D shows that book-to-bill disclosures have been made at a fairly consistent rate between the
years 2003 to 2013, although the frequency of disclosure increased slightly between 2010 and
2012.
3.3
Transition matrix
Table 2 provides a transition matrix for four types of book-to-bill disclosures. We classify all
firm-quarters into one of four categories, decreasing in the precision of BTB information provided:
(i) BTB ratio, (ii) Qualitative Description (i.e. a positive or negative adjective modifying BTB but
no ratio), (iii) BTB mention (i.e. BTB is mentioned, but no numeric ratio or qualitative description
is provided) and (iv) no BTB mention.
For firms that provided a book-to-bill ratio in period t-1 (BTB ratio t-1), by far the most
common disclosure choice in the current period is to provide a ratio (69.2%). The persistence of
the disclosure choice suggests it is a disclosure policy, but that firms deviate from that disclosure
policy about 30% of the time. For firms that provide a qualitative description in period t-1, the
plurality of firms make no mention of BTB in period t. The second most frequent disclosure choice
in period t is to provide a BTB ratio. The frequency with which managers deviate from
qualitatively characterizing BTB suggests that qualitatively characterizing BTB is not a persistent
disclosure policy, but rather a one-time decision.
17
4
Research design and empirical results
4.1
Is there positive bias in quantitative and qualitative book-to-bill disclosures?
In this section, we test (i) whether managers strategically round book-to-bill ratios when they
report a quantitative ratio and (ii) whether managers put a positive spin on qualitative descriptions
of BTB. As a benchmark for the amount of spin we might expect, we compare qualitative
descriptions of BTB to those of earnings. Collectively these tests provide evidence on whether
managers insert positive bias or spin into BTB disclosures. We first provide evidence of the extent
to which managers non-strategically round (i.e. report numbers to the tenths place regardless of
the value of the digit in the hundredths place). The use of non-strategic rounding by some
managers increases the benefit to rounding strategically because it prevents the market from
unraveling strategic rounding.
4.1.1 Do managers non-strategically round book-to-bill ratios?
Figure 1 shows a histogram of the book-to-bill ratios in our sample. The large spikes at the
tenths places suggest that managers frequently round book-to-bill ratios. The values spike
approaching the tenths place both from higher values and lower values. The non-strategic
rounding of some managers makes it difficult for investors to identify strategic rounding. Figure
2 shows the distribution of book-to-bill ratios around the value of 1.1 to provide a more granular
picture of the data. In untabulated analysis we find that 1.1, 1.0 and 0.9 are disclosed at a
significantly higher frequency than the ratios surrounding those points without a zero in the
hundredths column.
18
4.1.2 Do managers strategically round book-to-bill ratios?
When the number of decimal places to which a manager should make a disclosure is unclear,
managers can use their discretion to round disclosures.8 If managers are rounding up (on average),
we should observe values in the hundredths place which can be rounded up (6, 7, 8 and 9) less
frequently than values which cannot (1, 2, 3, and 4), because values which can be rounded upward
are replaced in the observed distribution with values rounded to the nearest tenth. We test this
prediction in Table 3. In row (5) of Panel A, we show that across our full sample, 53% of all the
disclosures reported to the hundredths place (that do not include a five), 9 cannot be rounded up.
The difference between the frequency of book-to-bill ratios that cannot be rounded up and those
that can is statistically significant. In rows (1) through (4), we report comparisons between
individual hundredths place values (i.e. X.X9 vs. X.X1). We find across all our comparisons,
managers’ report numbers which cannot be rounded up more frequently than those which can.
In Panels B and C of Table 3, we split our sample based on whether the firm disclosed a ratio
with hundredths-place precision the last time the ratio was given (Panel B) or whether the firm did
not disclose with hundredths-place precision the last time the ratio was given (Panel C). We expect
that firms who have rounded in the past will report more strategically in the future. Our results
are consistent with this prediction. In row (5) of Panel B, we see that the firms who maintain a
hundredths-place precision from the prior disclosure of book-to-bill disclosure do not strategically
8
Example of strategic rounding: at many college campuses career services advises students to report GPAs, for which
the decimal in the hundredths place can be rounded up to a higher tenths place, to one significant digit. They advise
students whose GPAs cannot be rounded up to a higher tenths place to report two significant digits (i.e. only
hundredths)
9
We exclude disclosures with the number five in the hundredths place from our analysis because it is unclear whether
those numbers can be rounded up to a higher tenths place (i.e. 1.048 if reported to the tenths place would be rounded
down to 1.0 and 1.052 would be rounded up to 1.1). It is also worth noting that the transcription of conference calls
is not sufficiently precise to allow us to distinguish between 1.10 (a number reported to the hundredths place) and 1.1
(a number reported to the tenths place).
19
round up. However, in row (5) of Panel C we show that firms not disclosing a ratio with
hundredths-place precision previously more frequently report numbers that would be rounded
down, implying that they rounded strategically (on average) in the current period.
4.1.3 Are managers’ qualitative characterizations of book-to-bill more positive than those of
earnings?
As our second test of positive bias in book-to-bill disclosures, we compare managers’
characterizations of book-to-bill ratios to their characterizations of earnings. By comparing
managers’ characterization of book-to-bill, a voluntary disclosure that does not directly reconcile
to financial statement items, to the characterization of earnings, we provide evidence on the role
of mandatory reporting in disciplining managers’ characterizations.
We compare the tone of BTB characterizations to earnings characterizations by estimating
the following OLS regression with year-quarter and firm fixed effects and standard errors clustered
by firm:
Net Positive =
β0 + β1BooktoBillIndicatori,t + β2Pre-Ann. Returni,t
+ β3UnexpEarningsi,t + β4UnexpRevenuei,t + β5BooktoMarketi,t
+ β6MarketCapi,t + β7EpRatioi,t + εi,t
Our regression model includes two observations per firm quarter. One in which the dependent
variable (Net Positive) is computed for sentences including the phrase “book-to-bill” and one for
sentences including an earnings phrase.10 We calculate Net Positive for BTB (earnings) as the net
10
An earnings statement is any sentence containing at least one of the terms: “earnings”, “EPS” or “net income”.
20
(1)
number of positive statements, calculated as the number of positive words less the number of
negative words, modifying a BTB phrase (earnings phrase) divided by the total number of BTB
phrases (earnings phrases). Our variable of interest, book-to-bill indicator, is set equal to one
(zero) for book-to-bill (earnings) observations.
To the extent that managers more positively
characterize book-to-bill than earnings, we expect a positive association between the book-to-bill
indicator and net positive (leading to a prediction of 𝛽1>0). We also select only firm-quarters with
both an earnings sentence and a BTB sentence, to hold constant unobservable changes in firm
performance. Our regression model includes firm-fixed effects, so our 𝛽1 coefficient captures
within-firm variation in net positive qualitative descriptions of BTB relative to earnings. Other
variables are defined in Appendix 1.
Table 4 provides the results of estimating Equation (1). We find that Book-to-Bill is positively
associated with Net Positive, consistent with our prediction that managers are more positive in
their qualitative descriptions of book-to-bill ratios relative to those of earnings. One difference in
the properties of the disclosures that could contribute to the difference in characterizations is that
earnings must be reported on financial statements, while BTB does not have to be. Mandatory
reporting could discipline managers’ characterizations.
4.2
Do managers withhold bad news book-to-bill ratios?
We next test whether managers strategically withhold book-to-bill disclosures when the
manager possesses negative information about the future financial performance of the firm by
modeling the determinants of the decision to provide a book-to-bill ratio. We estimate the
following OLS regression with year-quarter fixed effects and standard errors clustered by firm.
21
BooktoBillIndicatori,t =
β0 + β1 (Δ Fut Revenuei,t OR Δ Fut Earningsi,t) + β2BooktoBillIndicatori,(t-1)
+ β3 BooktoBillIndicatori,(t-2) + Β4 BooktoBillIndicatori,(t-3)+
+ Β5Pre-Ann. Returni,t + β6UnexpEarningsi,t + Β7UnexpRevenuei,t
+ β8BooktoMarketi,t + β9MarketCapi,t + β10EpRatioi,t + εi,t
Where BooktoBillIndicatori,t is an indicator variable that is equal to one if a book to bill ratio
is given in the conference call. Because prior disclosure patterns are likely to affect the decision
of whether or not to provide book-to-bill information in the current period, we include three lags
of our dependent variable. If managers strategically withhold book-to-bill disclosures that contain
bad news, we would expect the provision of book-to-bill information to be positively associated
with future revenue and future earnings, leading to a prediction of 𝛽1 > 0 in Equation (2). The 𝛽1
coefficient is estimated separately for future revenue and future earnings because they are likely
to be collinear. We calculate Δ Fut Revenue (Earnings) as the value of next quarter’s revenue
(earnings) minus the current quarter’s revenue (earnings), divided by the sum of the absolute value
of the current and next quarter’s revenue (earnings).11 Because the decision to provide book-tobill disclosures may also be influenced by factors such as prior stock return, current firm
performance, relative firm valuation and firm size, we include these as control variables. We also
include unexpected earnings (revenue) in all regressions, to control for the contemporaneous effect
of firm performance on disclosure (Miller 2002). Detailed definitions of control variables are
provided in Appendix 1.
11
In untabulated analysis we show our results are robust to calculating change in revenue and earnings using annual
rather than quarterly amounts. Specifically, we calculate future revenue (earnings) as the sum of the next four quarter’s
revenue (earnings) minus the most recent four quarter’s revenue (earnings), divided by the sum of the absolute value
of the next four quarter’s and the most recent four quarter’s revenue (earnings).
22
(2)
Table 5 columns (1) and (2) report the results of Equation (2). Column (1) presents the results
when Δ Fut Revenue is the independent variable of interest and column (2) presents the results for
Δ Fut Earnings. Coefficient estimates of both variables are positive and significant, suggesting
when future revenue is higher, managers are more likely to provide book-to-bill disclosures. The
significant coefficient suggests managers provide BTB ratios when their private information
indicates future revenues will be higher. Because of the intuitive relationship between the value
of the BTB ratio and future revenue and earnings growth (established formally in the next table),
we expect the withheld BTB ratio would likely have conveyed bad news.
Our control variables provide information about other determinants of BTB disclosure. All
three lags of BooktoBillIndicator load significantly, suggesting the decision to disclose is
persistent. We find market capitalization is positively associated with BooktoBillIndicator. We
find neither unexpected earnings nor unexpected revenue has a significant relationship with BTB,
suggesting current performance does not drive BTB disclosure choice.
In columns (3) and (4) we estimate equation (2) using firm fixed effects rather than including
lags of the BooktoBillIndicator. The firm fixed effects should capture unobserved time-invariant
firm characteristics, that effect the decision to disclose the book-to-bill ratio in the current quarter.
For both Δ Fut Revenue and Δ Fut Earnings the coefficient values are still positive and significant
with little variation from the results in columns (1) and (2). For parsimony, all additional tests are
performed using firm fixed effects rather than using three quarters of lags of the
BooktoBillIndicator.
23
4.3
Do quantitative and/or qualitative book-to-bill disclosures contain information about future
revenue or future earnings?
In this section, we examine whether quantitative and qualitative BTB disclosures convey
information about future revenue and future earnings. To test for an association, we estimate the
following OLS regression with year-quarter and firm fixed effects and standard errors clustered
by firm.
ΔFut Revenuei,t or ΔFut Earningsi,t =
β0 + β1BooktoBillRatioi,t + β2BooktoBillIndicatori,t
+ β3Positive Descriptioni,t + β4Negative Descriptioni,t
+ β5Positive Description X BooktoBillIndicatori,t
+ β6Negative Description X BooktoBillIndicatori,t
+ β7Pre-Ann. Returni,t + β8UnexpEarningsi,t
+ β9UnexpRevenuei,t + β10BooktoMarketi,t
+ β11MarketCapi,t + β12EpRatioi,t + εi
Where Positive Description is a dummy variable equal to one if the book to bill disclosure
contains positive words and Negative Description is a dummy variable equal to one if the book to
bill disclosure contains negative words.
In Table 6 Panel A, we first validate that ΔFut Revenue (ΔFut Earnings) has a positive
association with the BooktoBillRatio. In order to establish the association, we omit all qualitative
variables (i.e. β3 – β6) and regress ΔFut Revenue (ΔFut Earnings) on the BooktoBillRatio,
BooktoBillIndicator and control variables in column (1) (column (2)). Consistent with variation in
the book-to-bill ratio mapping into variation in future revenue, we find a positive association
between ΔFut Revenue and BooktoBillRatio in column (1). Column (2) shows a similar result of
24
(3)
a slightly lower magnitude for ΔFut Earnings. These results suggest that the value of the BTB
ratio, when disclosed, is positively related to both future revenue and earnings. 12 Our analysis in
columns (1) and (2) supports our prediction that variation in the level of BTB has information
about the change in future revenue and earnings.
Our second prediction is that qualitative descriptions of book-to-bill are informative, which we
investigate by testing whether positive (negative) descriptions are positively (negatively)
associated with future earnings and revenue, leading to a prediction of β3 > 0 and β4 < 0 in Equation
(3). Columns (3) and (4) of Table 6 Panel A report the results of this test. We find that for both
dependent variables (ΔFut Revenue and ΔFut Earnings), coefficient estimates of both of the
qualitative characterizations of BTB (Positive Description and Negative Description) are
significant in the expected directions. We interpret this as evidence consistent with our prediction
that managers use qualitative characterizations to convey information to the market rather than to
obfuscate or bias BTB disclosures.
We provide further insight into the information content of qualitative characterizations of
BTB by examining the interaction between qualitative disclosure and quantitative
characterizations. We estimate Equation (3) after including an interaction term for each of the
qualitative characterizations of BTB and BooktoBillIndicator. Including these interactions allows
us to test the extent to which the qualitative descriptions remain informative in the presence or
absences of a BTB disclosure. If managers withhold BTB in order to inaccurately characterize
BTB, we might expect qualitative characterizations in the absence of disclosed ratios to convey
12
The inclusion of the control for the decision to disclose BTB (BooktoBillIndicator) controls for the fact that the
Booktobillratio is not missing at random (i.e. withholding is negatively correlated with growth in earnings and
revenue). In untabulated analysis, we obtain similar results excluding all firm-quarters without a BTB ratio (i.e.
BTBindicator = 0).
25
little information. In column (5) and (6), we find only weak evidence of an interaction effect. The
interaction between Positive Description and BTB Indicator are significantly negative at the 10%
level, suggesting qualitative characterizations convey less information in the presence of a BTB
ratio, inconsistent with the BTB ratio disciplining the disclosure choice of managers.
In columns (7) and (8) we include the quantitative ratio. This analysis allows the BTB ratio
to compete with qualitative characterizations for explanatory power. The results suggest that
qualitative characterizations have information about future revenue and earnings in the presence
and absence of a BTB disclosure.
4.4
Are managers’ qualitative characterizations more or less positive in the presence or absence
of a book-to-bill ratio?
In Table 6 panel B, we use a two-proportion z-test to compare the proportion of positive to
negative characterizations when the BTB ratio is disclosed to when the BTB ratio is not disclosed.
If managers are using the discretion created by not disclosing BTB to mislead the market, we
would expect mangers to disclose more positive news in the absence of a BTB disclosure.
We find that when managers disclose the BTB ratio, descriptions are positive 82.3% of the
time. When managers do not disclose the BTB ratio, only 66.1% of descriptions are positive.
These differences are statistically significant at the 99% level. These results suggest that managers
provide more negative characterizations of the book-to-bill ratio when they do not provide the ratio
in the conference call.
Overall, our evidence suggests that managers make BTB disclosures less precise when their
BTB information is negative.
Sometimes managers disclose their negative news using a
qualitative description and sometimes they withhold disclosure completely.
26
4.5
Do book-to-bill disclosures substitute for other disclosures?
Our third hypothesis is that book-to-bill disclosures substitute for earnings guidance. To test
this hypothesis, we estimate the following OLS regression with year-quarter and firm fixed effects
and standard errors clustered by firm.
Disclosure Measure i,t =
β0 + β1BooktoBillIndicatori,t + β2Pre-Ann. Returni,t
+ β3UnexpEarningsi,t + β4UnexpRevenuei,t + β5BooktoMarketi,t
+ β6MarketCapi,t + β7EpRatioi,t + εi,t
In our tests of H3, we use manager forecast precision as our dependent variable. We also
estimate regression (4) with four other dependent variables, to capture how the decision to disclose
the BTB ratio simultaneously affects the decision to disclose other items:
# of backlog sentences
is the number of sentences in the conference call that contain the word
“backlog”.
# of bookings sentences
is the number of sentences in the conference call that contain the word
“bookings”.
# of non-earnings
sentences
is the total number of sentences in the conference call less the number of
sentences with earnings related words.
EA plus PM length
is the combined length (in total number of sentences) of the earnings
announcement plus the Prepared Remarks section of the conference call.
Manager Forecast
Precision
is an index variable that is equal to 4 if the manager provides a point
forecast, 3 if the the manager provides a range forecast, 2 if the manager
indicates that earnings will be up or down from the prior quarter, 1 if a
forecast appears in the First Call Database without any quantitative
information, and 0 if there is no record of any forecast in the First Call
Database.
In Table 7, columns (1) and (2) we examine how disclosing the BTB ratio affects the # of
backlog sentences and # of bookings sentences. Bookings and backlog convey similar information
27
(4)
as BTB, because both provide information about customer orders yet to be fulfilled. We might
expect the disclosure of BTB to complement booking and backlog disclosures if managers attempt
to communicate information about the order book using all three measures when they decide to
discuss the order book in the conference call. In contrast, we might expect a substitutive
relationship if managers disclose BTB in the form of a ratio when the ratio is high but make a
booking or backlog disclosure instead of a BTB disclosure when the ratio is low. Evidence of a
substitute relationship perhaps suggests the strategic withholding documented in Table 5 does not
really degrade the information environment, because managers still provide other metrics to help
investors understand customer order information. In both columns (1) and (2) we find a strong
significant positive relationship between BTB and discussion of order backlog and bookings.
In column (3), we examine whether when managers disclose BTB, the conference call takes
more or less time. We might expect the direct effect of disclosing BTB is a longer conference call
because the manager must describe the ratio and the economic forces contributing to it. However,
an indirect effect could exist, if managers act to hold the time of the conference call and/or length
of the press release constant across quarters. In such a situation, managers might be more apt to
mention standard ratios, such as BTB, more frequently when they have less other news to disclose.
We find an insignificant negative association between the BTB ratio and length.
In column (4), we test H3 that managers issue managerial forecasts less frequently when they
disclose BTB. We find a significant negative association, consistent with mangers providing
additional information to the capital market when they provide less information about the order
book. In column (5), we provide additional support for a substitutive relationship between
discussion of earnings and BTB, by demonstrating that when managers disclose book-to-bill their
EAs and conference calls future fewer words related to earnings. Overall, our evidence suggests
28
that when managers provide more information about the order book, they provide less information
about earnings.
4.6
Do prices impound the information in book-to-bill disclosures during the announcement
window?
Our fourth hypothesis is that investors immediately impound information in book-to-bill
disclosures into stock prices. In order to test this prediction, we estimate the following OLS
regression with year-quarter and firm fixed effects and standard errors clustered by firm.
Ann. Returni,t or Post Ann. Returni,t =
β0 + β1BooktoBillRatioi,t + β2BooktoBillIndicatori,t
+ β3Positive Descriptioni,t + β4Negative Descriptioni,t
+ β5Pre-Ann. Returni,t + β6UnexpEarningsi,t
+ β7UnexpRevenuei,t + β8BooktoMarketi,t
+ β9MarketCapi,t + β10EpRatioi,t + εi,t
Where Ann. Return is the market adjusted return over the three-day window centered on the
announcement date and Post Ann. Return is the market-adjusted return calculated over the seventyfive trading days beginning three days after the earnings announcement. Other variable definitions
are provided in Appendix 1. If book-to-bill information is impounded into prices during the
announcement window, we expect to find a positive relationship between the book-to-bill ratio
and Ann. Return, and no relationship between the book-to-bill ratio and Post Ann. Return. Thus,
we predict β1 > 0 in Equations (4) when Ann. Return is the dependent variable and β1 = 0 when
Post Ann. Return is the dependent variable. To the extent that qualitative descriptions of book-tobill information are impounded into price during the announcement window, we expect a positive
relationship between positive qualitative descriptions and announcement-period returns and a
29
(5)
negative relationship between negative qualitative descriptions and announcement-period returns.
Thus, we predict β3 > 0 and β4 > 0 when Ann. Return is the dependent variable. Our hypothesis
that BTB disclosures does not generate drift predicts β3 and β4 = 0 when Post Ann. Return is the
dependent variable.
Table 8 reports the results of estimating Equation (5). In columns (1) and (2), we omit the
qualitative descriptions and indicator term from equation (4) in order to test the market reaction to
the book to bill ratio. Consistent with the prediction the market prices BTB during the
announcement window, in column (1) we find that the book-to-bill ratio is positively associated
with announcement period returns. In column (2) we use post-announcement returns as our
dependent variable and find no evidence that the BTB ratio generates post drift. In columns (3)
and (4) we include the book-to-bill indicator and qualitative descriptions while omitting the BTB
ratio. In column (3) we find the coefficients for Positive Description and Negative Description
are statistically significant and have the expected sign for the announcement period returns. We
find no evidence of post announcement drift (column (4)), as all variables are insignificant and
have t-stats with an absolute value of less than 1.0. Taken together, we consider these findings as
evidence in support of our prediction that both quantitative and qualitative information in bookto-bill disclosures are impounded immediately in stock prices and do not result in subsequent price
drift.
30
5
Conclusion
We analyze the strategic disclosure of book-to-bill, a novel voluntary disclosure that differs
from other common forms of voluntary disclosure (such as managerial earnings forecasts) in that
it does not relate precisely to a future mandatory disclosure.
We find evidence of strategic disclosure along several dimensions. First, we find evidence
firms strategically round up reported book to bill ratios. Second, we find managers characterize
book-to-bill more positively than they characterize earnings, suggesting the voluntary nature of
BTB disclosures allow managers to put a positive spin on disclosure. Third, we show that managers
withhold disclosure when future earnings and revenue growth are low, consistent with managers
withholding bad news.
We also find evidence that a lot of the imprecision in disclosure cannot be attributed entirely to
strategic motivations. First, managers disclose 64% of BTB ratios rounded to the nearest tenth.
Second, we document that managers disclose qualitative characterizations of BTB when they do
not disclose the ratio. The theoretical reasons for managers to decrease the precision of disclosure
are somewhat unclear, as most theory suggests that if managers wish to align investors’
expectations with the managers’ private information, managers should make the most precise
disclosure possible. We provide two plausible reasons for imprecise disclosure: (i) it decreases
the amount of time managers spend discussing BTB, potentially increasing the amount of time
they can discuss other things or (ii) it increases managerial power by degrading the information of
outsiders. We do not distinguish between these mechanisms in our analysis and leave further
exploration of these forces to future research.
31
We also examine how the presence or absence of BTB disclosures impact the information
environment. We first show that managers change the information they supply when they make a
BTB disclosure, by showing a negative relationship between BTB disclosures and managerial
forecasts. Second, we find no evidence that BTB disclosures or their qualitative characterizations
lead to mispricing. We find that prices impound information in BTB disclosures within the
announcement window. This paper contributes to our understanding of how managers’ use the
discretion they have over voluntary disclosures to shape the information environment.
32
References
Ajinkya, B. B., & Gift, M. J. (1984). Corporate managers' earnings forecasts and symmetrical
adjustments of market expectations. Journal of Accounting Research, 425-444.
Amihud, Y. (2002). Illiquidity and stock returns: cross-section and time-series effects. Journal of
financial markets, 5(1), 31-56.
Anilowski, C., Feng, M., & Skinner, D. J. (2007). Does earnings guidance affect market returns?
The nature and information content of aggregate earnings guidance. Journal of Accounting
and Economics, 44(1), 36-63.
Chandra, U., Procassini, A., & Waymire, G. (1999). The Use of Trade Association Disclosures by
Investors and Analysts: Evidence from the Semiconductor Industry*. Contemporary
Accounting Research, 16(4), 643-670.
Crawford, V. P., & Sobel, J. (1982). Strategic information transmission. Econometrica: Journal
of the Econometric Society, 1431-1451.
Dye, R. A. (1985). Disclosure of nonproprietary information. Journal of accounting research, 123145.
Einhorn, E., & Ziv, A. (2008). Intertemporal dynamics of corporate voluntary disclosures. Journal
of Accounting Research, 46(3), 567-589.
Feldman, R., Govindaraj, S., Livnat, J., & Suslava, K. (2014). Market Reaction to Quantitative
and Qualitative Order Backlog Disclosures. Available at SSRN 2534810.
Gentzkow, M., & Kamenica, E. (2011). Competition in persuasion (No. w17436). National Bureau
of Economic Research.
Glosten, L. R., & Milgrom, P. R. (1985). Bid, ask and transaction prices in a specialist market with
heterogeneously informed traders. Journal of financial economics, 14(1), 71-100.
Graham, J. R., Harvey, C. R., & Rajgopal, S. (2005). The economic implications of corporate
financial reporting, Journal of accounting and economics, 40(1), 3-73.
Grossman, S. J. (1981). The informational role of warranties and private disclosure about product
quality. Journal of law and economics, 461-483.
Kasznik, R., & Lev, B. (1995). To warn or not to warn: Management disclosures in the face of an
earnings surprise. Accounting review, 113-134.
Lev, B., & Thiagarajan, S. R. (1993). Fundamental information analysis. Journal of Accounting
research, 190-215.
McNichols, M., & Trueman, B. (1994). Public disclosure, private information collection, and
short-term trading. Journal of Accounting and Economics, 17(1), 69-94.
Mergenthaler, R., Rajgopal, S., & Srinivasan, S. (2012). CEO and CFO career penalties to missing
quarterly analysts forecasts. Available at SSRN 1152421.
33
Milgrom, P. R. (1981). Good news and bad news: Representation theorems and applications. The
Bell Journal of Economics, 380-391.
Milgrom, P., & Roberts, J. (1986). Price and advertising signals of product quality. The Journal of
Political Economy, 796-821.
Milgrom, P., & Roberts, J. (1986). Relying on the information of interested parties. The RAND
Journal of Economics, 18-32.
Miller, G. S. (2002). Earnings performance and discretionary disclosure. Journal of Accounting
Research, 40(1), 173-204.
Nagar, V., Nanda, D., & Wysocki, P. (2003). Discretionary disclosure and stock-based incentives.
Journal of accounting and economics, 34(1), 283-309.
Rajgopal, S., Shevlin, T., & Venkatachalam, M. (2003). Does the stock market fully appreciate
the implications of leading indicators for future earnings? Evidence from order backlog.
Review of Accounting Studies, 8(4), 461-492.
Richardson, S., Teoh, S., & Wysocki, P. (2004). The Walk-down to Beatable Analyst Forecasts:
The Role of Equity Issuance and Insider Trading Incentives. Contemporary Accounting
Research, 21(4), 885-924.
Rogers, J. L., & Stocken, P. C. (2005). Credibility of management forecasts. The Accounting
Review, 80(4), 1233-1260.
Rogers, J. L., & Van Buskirk, A. (2013). Bundled forecasts in empirical accounting research.
Journal of Accounting and Economics, 55(1), 43-65.
Skinner, D. J. (1994). Why firms voluntarily disclose bad news. Journal of accounting research,
38-60.
Skinner, D. J., & Sloan, R. G. (2002). Earnings surprises, growth expectations, and stock returns
or don't let an earnings torpedo sink your portfolio. Review of accounting studies, 7(2-3),
289-312.
Verrecchia, R. E. (1983). Discretionary disclosure. Journal of accounting and economics, 5, 179194. Grossman, S. J. (1981). The informational role of warranties and private disclosure
about product quality. Journal of law and economics, 461-483.
34
Appendix 1: Variable definitions
Variable Label
Definition
Book to Market
Book value of equity divided by market value of equity. Book value of equity is calculated as
CEQ (AT - LT if missing). Market value of equity is obtained from CRSP. Transformed to
percentile ranks by year for regression analyses.
An indicator variable that is equal to one if a book to bill ratio is given in the quarterly earnings
conference call, zero otherwise. Subscripts denote lags of prior quarters.
Earnings to price ratio. Price obtained from CRSP. Earnings calculated using last four
quarters. (Beginning with the current quarter). Transformed to percentile rank by year for
regression analyses.
A binary variable equal to one if the book-to-bill sentences contain one of the following words:
“deteriorating, deteriorate, negative, shrunk, shrink, decreased, declining, below, weakening,
weakened, bad, weak, disappoint, disappointing, not good, underwhelming, negative”.
A binary variable equal to one if the book-to-bill sentences contain one of the following words:
“strengthening, improving, improved, well over, increased, exceed, exceeded, above, good,
strong, pleased, happy, positive, outstanding, healthy, impressive”.
The market adjusted return to the firm around the announcement date. Calculated as the firm's
cumulative unadjusted returns beginning the trading day before the earnings announcement and
ending two trading days after the earnings announcement subtracted by the cumulative returns
for the value-weighted portfolio over the same period
The market adjusted return to the firm in the post-announcement period, calculated as the firm's
cumulative unadjusted returns beginning 3 trading days after the earnings announcement and
ending 77 trading days after the earnings announcement subtracted by the cumulative returns
for the value-weighted portfolio over the same period.
The market adjusted return to the firm in the pre-announcement period. Calculated as the firm's
cumulative unadjusted returns beginning 31 trading days before the earnings announcement and
ending two trading days before the earnings announcement subtracted by the cumulative returns
for the value-weighted portfolio over the same period.
Current quarter's actual EPS minus same quarter last year’s EPS, scaled by the share price at the
end of the fiscal quarter. (Compustat variable EPSFXQ). Transformed to percentile rank by
year for regression analyses.
The difference between the current quarter's revenue and the revenue for the same quarter last
year. Transformed to percentile rank by year for regression analyses.
The sum of the next quarter’s earnings minus the current quarter’s earnings, divided by the sum
of the absolute value of next quarter’s and the current quarter’s earnings. Transformed to
percentile rank by year and divided by 100 for regression analyses.
The sum of the next quarter’s revenue minus the current quarter’s revenue, divided by the sum
of the absolute value of next quarter’s and the current quarter’s revenue. Transformed to
percentile rank by year and divided by 100 for regression analyses.
The number of sentences in the conference call that contain the word “backlog”
Book-to-Bill
Indicator
Ep Ratio
Negative
Description
Positive
Description
Ann. Return
Post Ann. Return
Pre-Ann. Returns
Unexp Earnings
Unexp Revenue
Δ Fut Earnings
Δ Fut Revenue
# of backlog
sentences
# of bookings
sentences
# of non-earnings
sentences
EA plus PM length
Manager Forecast
Precision
The number of sentences in the conference call that contain the word “bookings”
The total number of sentences in the conference call less the number of sentences with earnings
related words
The combined length (in total number of sentences) of the earnings announcement plus the
Prepared Remarks section of the conference call
An index variable that is equal to 4 if the manager provides a point forecast, 3 if the manager
provides a range forecast, 2 if the manager indicates that earnings will be up or down from the
prior quarter, 1 if a forecast appears in the First Call Database without any quantitative
information, and 0 if there is no record of any forecast in the First Call Database.
35
Figure 1: Distribution of disclosed book-to-bill ratios (full sample)
2000
# of observations
1500
1000
500
0
0.70
0.80
0.90
1.00
1.10
1.20
Book-to-Bill Ratio
1.30
1.40
1.50
Figure 2: Distribution of disclosed book-to-bill ratios (from 1.01 to 1.19)
450
400
350
# of observations
300
250
200
150
100
50
0
1.01
1.02
1.03
1.04
1.05
1.06
1.07
1.08
1.09
1.1
1.11
Book-to-Bill Ratio
37
1.12
1.13
1.14
1.15
1.16
1.17
1.18
1.19
Table 1: Descriptive statistics
Panel A: Summary Statistics
Variable
N
Mean
25%
Median
75%
Std Dev
Book-to-Bill Ratio
3,581
1.128
1.000
1.000
1.160
0.405
Book-to-Bill Indicator
14,363
0.264
0.000
0.000
1.000
0.441
Positive Description
14,363
.149
-
-
-
.356
Negative Description
14,363
.042
-
-
-
.200
Ann. Return
14,363
0.003
-0.049
0.003
0.055
0.101
Post Ann. Return
14,363
0.028
-0.093
0.020
0.135
0.233
Pre-Ann. Returns
14,363
0.012
-0.060
0.007
0.076
0.145
Δ Fut Revenue
14,360
0.011
-0.020
0.015
0.046
0.089
Δ Fut Earnings
14,258
0.033
-0.172
0.030
0.231
2.353
Unexp Revenue
14,356
55.028
-2.667
7.398
43.519
314.298
Unexp Earnings
14,356
0.001
-0.005
0.002
0.008
0.063
Book to Market
14,363
0.723
0.316
0.500
0.756
1.050
EP Ratio
14,363
-0.031
-0.016
0.033
0.056
0.224
Marktet Cap
14,363
4,752,063
230,205
712,882
2,702,520
14,924,425
This table provides summary statistics for key variables used in the analysis. Variable definitions are provided in
Appendix 1.
38
Panel B: Pearson Correlation Coefficients
Book-toBill Ratio
Book-toPositive
Negative
Bill
Description Description
Indicator
Ann.
Return
Post
Pre-Ann. Δ Fut
Δ Fut
Ann.
Returns Revenue Earnings
Return
Unexp
Revenue
Unexp
Earnings
Book to
Market
EP Ratio
Book-to-Bill Ratio
1
Book-to-Bill Indicator
-
1
Positive Description
0.032
0.492***
1
Negative Description
-0.022
0.160***
0.186***
1
0.051**
0.011
0.029***
-0.020*
1
Post Ann. Return
-0.025
-0.040***
-0.032***
-0.0151
0.016
1
Pre-Ann. Returns
0.014
-0.030***
-0.007
-0.025**
-0.036***
0.009
Δ Fut Revenue
0.120***
0.021*
0.047***
-0.041***
0.067***
0.088*** 0.086***
1
Δ Fut Earnings
0.038*
0.017*
0.008
-0.004
0.026**
0.053*** 0.024**
0.136***
1
Unexp Revenue
0.006
-0.009
0.001
-0.008
0.013
-0.029***
-0.012
-0.031***
-0.007
1
Unexp Earnings
0.021
0.027**
0.021*
-0.018*
0.062***
-0.029***
0.013
0.043***
-0.058***
0.048***
1
Book to Market
0.001
-0.0278***
-0.017*
-0.003
-0.006
0.082*** -0.044*** -0.061***
-0.005
0.044***
-0.103***
1
EP Ratio
0.004
0.025**
0.025**
0.018*
0.008
-0.198*** -0.065***
-0.004
-0.053***
0.101***
0.149***
-0.138***
1
-0.040*
-0.004
0.019*
0.033***
-0.006
-0.032***
-0.004
-0.006
0.353***
0.002
-0.088***
0.108***
Ann. Return
Marktet Cap
39
Marktet
Cap
1
-0.011
1
Panel C: Frequency of book-to-bill by Fama French 12-industry classification
Fama-French 12 Industries
% Obs with Book-to-Bill
N
Business Equipment
30%
8,516
Chemicals
7%
243
Consumer Durables
9%
291
Energy
7%
28
Finance
5%
22
Healthcare
11%
177
Manufacturing
23%
3,498
Other
22%
1,055
Telecom
15%
130
Utilities
8%
39
Wholesale/retail
29%
364
Total
14,363
This table describes the number of observations and frequency of book-to-bill disclosures in our main sample by
industry, using the Fama-French 12-industry classification. N indicates the number of observations from each industry.
% Obs with book-to-bill indicates the percentage of those observations that contain book-to-bill disclosures.
Panel D: Frequency of book-to-bill by year
Year
% Obs with Book-to-Bill
N
2003
23%
1,640
2004
27%
1,601
2005
27%
1,531
2006
24%
1,466
2007
24%
1,431
2008
24%
1,455
2009
26%
1,456
2010
31%
1,381
2011
30%
1,332
2012
32%
1,028
2013
21%
42
Total
14,363
This table describes the number of observations and frequency of book-to-bill disclosures in our main sample by fiscal
year. N indicates the number of observations from each year. % Obs with book-to-bill indicates the percentage of
those observations that contain book-to-bill disclosures.
40
Table 2: transition matrix of book-to-bill disclosure frequencies
This table provides a transition matrix documenting the frequency with which managers’ change
the precision of BTB disclosure. We classify all firm-quarter observations in our sample into one
of four categories: (i) BTB ratio (when a numeric BTB ratio is disclosed), (ii) Qualitative
Description (a numeric ratio is not disclosed but a qualitative characterization of BTB is provided),
(iii) BTB mention (BTB is mentioned, but no numeric ratio, nor qualitative characterizations is
provided) and (iv) no BTB mention (all other observations). We report the number of observations
in the top of the cell and the percentage of observations at the bottom of the cell, with values for
period (t) in columns and for period (t-1) in rows.
BTB ratio (t)
BTB ratio (t-1)
Qualitative Description (t-1)
BTB mention (t-1)
No BTB mention (t-1)
Qualitative
BTB mention (t)
Description (t)
No BTB
mention (t)
2535
312
93
722
69.2%
8.5%
2.5%
19.7%
269
186
28
283
35.1%
24.3%
3.7%
36.9%
93
31
147
1141
6.6%
2.2%
10.4%
80.8%
868
302
255
7065
10.2%
3.6%
3.0%
83.2%
41
Table 3: Strategic rounding of book-to-bill ratios
This table tests differences in frequencies of the last digit in book-to-bill ratios. In panel A, we
use the entire sample. Panel B includes observations in which the last ratio the firm disclosed had
a non-zero value in the hundredths-place. Panel C includes observations in which the last ratio
the firm disclosed had a zero in the hundredths-place. Rows 1-4 test for differences in
frequencies at the hundredth place (i.e. X.X9 vs. X.X1). Row 5 tests the difference of
frequencies of all the hundredths place digits that could be rounded up (9,8,7,6) compared to all
the digits that cannot be rounded up (1,2,3,4). Row 6 tests the difference between the
frequencies of book-to-bill ratios of 0.9 vs. 1.1. The t-stat is calculated as the difference in
proportion minus 0.5, scaled by the standard error.
Panel A: Full Sample
Difference
(%higher)
Standard
Error
t-stat
(1)
9 vs. 1
50%
0.01
-0.17
(2)
8 vs. 2
49%
0.01
1.14
(3)
7 vs. 3
49%
0.01
0.65
(4)
6 vs. 4
43%
0.01
5.98
(5)
(9,8,7,6) vs
(1,2,3,4)
47%
0.01
3.84
(6)
1.1 vs. 0.9
70%
0.01
-13.62
Panel B: Non-zero value in hundredths place last time book-to-bill disclosed
Difference
(%higher)
Standard
Error
t-stat
(1)
9 vs. 1
49%
0.02
0.55
(2)
8 vs. 2
53%
0.02
-2.03
(3)
7 vs. 3
49%
0.02
0.65
(4)
6 vs. 4
44%
0.02
4.13
(5)
(9,8,7,6) vs
(1,2,3,4)
49%
0.01
1.75
42
Panel C: Zero value in hundredths place last time book-to-bill disclosed
Difference
(%higher)
Standard
Error
t-stat
(1)
9 vs. 1
53%
0.03
-1.19
(2)
8 vs. 2
40%
0.02
4.60
(3)
7 vs. 3
50%
0.02
0.14
(4)
6 vs. 4
40%
0.02
4.54
(5)
(9,8,7,6) vs
(1,2,3,4)
45%
0.01
4.42
43
Table 4: The effect of mandatory reporting on qualitative disclosures
This table tests whether managers are more positive in their qualitative descriptions of book-tobill ratios than their qualitative descriptions of earnings. Our regression model includes two
observations per firm quarter. One in which the dependent variable (Net Positive) is computed for
sentences including the phrase “book-to-bill” and one for sentences including an earnings phrase.13
We calculate Net Positive for BTB (earnings) as the net number of positive statements (calculated
as the number of positive words less the number of negative words) modifying a BTB phrase
(earnings phrase) divided by the total number of BTB phrases (earnings phrases). The variable of
interest Book to Bill Indicator (t) is set equal to one (zero) when net positive is calculated for
sentences including a Book to bill (earnings) phrase. Our sample includes all firm-quarters with
at least one book-to-bill sentence and one earnings sentence. Appendix 1 contains detailed variable
definitions. Standard errors are clustered by firm. *, **, and *** represent significance at 10%,
5%, and 1%, respectively (two-tailed). T-values are presented beneath the coefficient estimates in
parentheses.
Dependent Variable:
Net Positive
0.413***
Book to Bill Indicator (t)
(16.74)
0.027
Pre-Ann. Returns
(0.55)
-0.000
Unexp Earnings
(-0.03)
0.000
Unexp Revenue
(0.00)
-0.001
Book to Market
(-1.31)
-0.000
Market Cap
(-0.12)
0.000
Ep Ratio
(0.68)
13
Observations
5284
Adjusted R-squared
0.309
Year Fixed Effects
Yes
Firm Fixed Effects
Yes
An earnings statement is any sentence containing at least one of the terms: “earnings”, “EPS” or “net income”.
44
Table 5: The association between withholding disclosure and firm performance
This table models the determinants of providing BTB disclosures. We regress the book-to-bill ratio
indicator on the change in future revenue, the change in future earnings, three quarter lags of the
book-to-bill ratio indicators and control variables. Appendix 1 contains detailed variable
definitions. Standard errors are clustered by firm. *, **, and *** represent significance at 10%,
5%, and 1%, respectively (two-tailed). T-values are presented beneath the coefficient estimates in
parentheses.
Dependent Variable: Book-to-Bill Indicator(t)
(1)
(2)
(3)
(4)
Firm Fixed Firm Fixed
AR(3)
AR(3)
Effects
Effects
Model
Model
Model
Model
Δ Fut Revenue
0.042***
0.037***
(3.59)
(3.30)
Δ Fut Earnings
Book-to-Bill Indicator(t-1)
0.029**
0.029**
(2.54)
(2.57)
0.345***
0.345***
(27.91)
(27.75)
0.218***
0.218***
(15.66)
(15.68)
0.199***
0.198***
(17.89)
(17.56)
-0.029
-0.025
-0.037**
-0.033*
(-1.47)
(-1.26)
(-1.98)
(-1.76)
0.000
0.000*
0.000***
0.000***
(1.45)
(1.89)
(3.18)
(3.49)
-0.000
-0.000
-0.000
-0.000
(-1.15)
(-1.28)
(-1.21)
(-1.37)
0.000
0.000
0.000
0.000
(1.40)
(1.16)
(0.31)
(0.38)
0.000***
0.000***
0.003***
0.004***
(2.89)
(2.84)
(4.14)
(4.30)
-0.000
-0.000
-0.000**
-0.000*
(-1.19)
(-1.01)
(-2.00)
(-1.79)
Observations
13123
13032
14352
14250
Adjusted R-squared
Year-Quarter Fixed Effects
0.413
Yes
0.412
Yes
0.404
Yes
0.404
Yes
Firm Fixed Effects
No
No
Yes
Yes
Book-to-Bill Indicator(t-2)
Book-to-Bill Indicator(t-3)
Pre-Ann. Returns
Unexp Earnings
Unexp Revenue
Book to Market
Market Cap
Ep Ratio
45
Table 6: The association between qualitative disclosure and future performance
Panel A: Regression Analysis
This table tests the association between future firm performance and quantitative and qualitative
BTB disclosures. In Panel A we regress change in future revenue (Δ Fut Revenue) and change in
future earnings (Δ Fut Earnings) on the book-to-bill ratio, the book-to-bill indicator, qualitative
characterizations of the ratio (Positive Description, Negative Description), interactions between
the qualitative characterizations and the book-to-bill indicator and control variables. Appendix 1
contains detailed variable definitions. Standard errors are clustered by firm. *, **, and ***
represent significance at 10%, 5%, and 1%, respectively (two-tailed). T-values are presented
beneath the coefficient estimates in parentheses.
Dependent Variables:
Book-to-Bill Ratio
Book-to-Bill Indicator(t)
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Δ Fut
Revenue
Δ Fut
Earnings
Δ Fut
Revenue
Δ Fut
Earnings
Δ Fut
Revenue
Δ Fut
Earnings
Δ Fut
Revenue
Δ Fut
Earnings
0.018**
(2.06)
0.042***
(3.01)
-0.047**
(-2.55)
-0.030*
(-1.76)
-0.000
(-0.01)
0.039**
(2.02)
-0.002***
(-20.44)
0.000***
(5.38)
-0.002***
(-10.25)
-0.002***
(-4.34)
-0.003***
(-29.59)
14250
0.107
Yes
Yes
0.036***
(3.52)
-0.025*
(-1.84)
0.073***
(5.25)
-0.114***
(-5.63)
-0.033**
(-1.98)
0.018
(0.78)
0.092***
(4.87)
-0.000***
(-3.08)
-0.000***
(-4.66)
-0.002***
(-12.13)
0.000
(1.33)
-0.002***
(-14.14)
14352
0.145
Yes
Yes
0.015
(1.64)
0.003
(0.22)
0.042***
(3.01)
-0.047**
(-2.57)
-0.031*
(-1.82)
0.001
(0.02)
0.039**
(2.03)
-0.002***
(-20.45)
0.000***
(5.37)
-0.002***
(-10.24)
-0.002***
(-4.36)
-0.003***
(-29.53)
14250
0.107
Yes
Yes
0.039***
(3.72)
-0.018
(-1.39)
0.016*
(1.73)
0.002
(0.17)
0.094***
(4.93)
-0.000***
(-3.03)
-0.000***
(-4.60)
-0.002***
(-12.28)
0.000
(1.31)
-0.002***
(-14.18)
14352
0.139
Yes
Yes
0.039**
(2.05)
-0.002***
(-20.40)
0.000***
(5.41)
-0.002***
(-10.33)
-0.002***
(-4.34)
-0.003***
(-29.31)
14250
0.106
Yes
Yes
Positive Description
Negative Description
Positive Description*BTB
Indicator(t)
Negative Description*BTB
Indicator(t)
Pre-Ann. Returns
Unexp Earnings
Unexp Revenue
Book to Market
Market Cap
Ep Ratio
Observations
Adjusted R-squared
Year-Quarter Fixed Effects
Firm Fixed Effects
0.006
(0.87)
0.052***
(6.39)
-0.103***
(-8.35)
0.012
(1.56)
0.021**
(2.56)
-0.046***
(-3.92)
0.092***
(4.85)
-0.000***
(-3.07)
-0.000***
(-4.63)
-0.002***
(-12.21)
0.001
(1.38)
-0.002***
(-14.21)
14352
0.144
Yes
Yes
0.039**
(2.01)
-0.002***
(-20.40)
0.000***
(5.39)
-0.002***
(-10.26)
-0.002***
(-4.31)
-0.003***
(-29.57)
14250
0.107
Yes
Yes
46
0.012
(1.39)
0.073***
(5.25)
-0.113***
(-5.60)
-0.031*
(-1.83)
0.016
(0.70)
0.092***
(4.87)
-0.000***
(-3.06)
-0.000***
(-4.64)
-0.002***
(-12.20)
0.001
(1.37)
-0.002***
(-14.21)
14352
0.145
Yes
Yes
Panel B: The proportion of positive and negative BTB characterizations
This panel performs a two-proportion z-test to determine whether the difference between the
proportion of Positive Description (Negative Description) when the firm discloses a book-to-bill
ratio is significantly different from the proportion of Positive Description (Negative Description)
when the firm does not disclose a book-to-bill ratio. The sample is the firms that provide a
Positive or Negative Description of the book-to-bill ratio in a firm quarter. Appendix 1 contains
detailed variable definitions.
Positive
Description
Negative
Description
Row Sum
Disclose Ratio
1675
82.3%
360
17.7%
2035
100%
Do not Disclose Ratio
467
66.1%
240
33.9%
707
100%
Difference in
proportions test
Difference in proportions
(82.3% - 66.1%)
Standard Error14
Z-Score Test Statistic
16.2%
0.02
7.60
14
To calculate the standard error we first calculate p = (0.823 * 2035 + 0.661 * 707) / (2035 + 707). Then we calculate
the standard error as SE = sqrt(p*(1-p)*[(1/2035)+(1/707)].
47
Table 7: The association between book-to-bill and contemporaneous dislosures
This table tests how BTB disclosures substitute for and complement other disclosures. We regress
five measures of disclosure (# of backlog sentences, # of bookings sentences, # of non-earnings
sentences, EA plus PM length, Manager Forecast Precision) on the book-to-bill indicator and
control variables. Appendix 1 contains detailed variable definitions. Standard errors are clustered
by firm. *, **, and *** represent significance at 10%, 5%, and 1%, respectively (two-tailed). Tvalues are presented beneath the coefficient estimates in parentheses.
Dependent Variables:
(1)
(2)
(3)
# of backlog
sentences
5.911***
# of bookings
sentences
4.476***
(7.67)
-0.398
EA plus PM
length
-1.505
(4)
Manager
Forecast
Precision
-0.102***
(5)
# of nonearnings
sentences
1.636**
(5.99)
(-1.55)
(-3.88)
(2.32)
0.633
-3.228
-0.091**
-5.852***
(-0.29)
(0.54)
(-1.58)
(-2.04)
(-4.06)
-0.002
-0.004
0.011
-0.001***
-0.019**
(-0.24)
(-0.54)
(0.90)
(-3.06)
(-2.51)
0.003
0.013
-0.027*
0.001
0.003
(0.24)
(1.53)
(-1.76)
(1.44)
(0.28)
-0.008
-0.025
0.170***
-0.001
0.050**
(-0.29)
(-1.10)
(3.14)
(-0.75)
(2.38)
0.133*
-0.005
0.292**
0.003
0.250***
(1.86)
(-0.09)
(2.32)
(1.29)
(4.45)
0.012
-0.010
0.001
0.002***
-0.053***
(0.81)
(-0.80)
(0.03)
(3.59)
(-3.73)
Observations
14355
14355
7337
14355
12925
Adjusted R-squared
0.659
0.667
0.734
0.558
0.496
Year-Quarter Fixed Effects
Yes
Yes
Yes
Yes
Yes
Firm Fixed Effects
Yes
Yes
Yes
Yes
Yes
Book-to-Bill Indicator(t)
Pre-Ann. Returns
Unexp Earnings
Unexp Revenue
Book to Market
Market Cap
Ep Ratio
48
Table 8: The association between book-to-bill and returns
This table tests how BTB disclosures are impounded into stock prices. We regress Announcement
Returns or Post Announcement Returns (Ann. Return or Post Ann.Return) on Book-to-Bill Ratio,
Book-to-Bill Indicator, qualitative characterizations of BTB (Positive Description, Negative
Description) and control variables. Appendix 1 contains detailed variable definitions. Standard
errors are clustered by firm. *, **, and *** represent significance at 10%, 5%, and 1%, respectively
(two-tailed). T-values are presented beneath the coefficient estimates in parentheses.
Dependent Variables:
Book-to-Bill Ratio
Book-to-Bill Indicator(t)
(1)
(2)
(3)
(4)
Ann
Return
Post-Ann
Return
Ann
Return
Post-Ann
Return
0.007*
(1.70)
-0.004
(-0.83)
Positive Description
Negative Description
Pre-Ann. Returns
Unexp Earnings
Unexp Revenue
Book to Market
Market Cap
Ep Ratio
Observations
Adjusted R-squared
Year-Quarter Fixed Effects
Firm Fixed Effects
-0.032***
(-3.42)
0.000***
(10.38)
0.000***
(3.64)
-0.000***
(-3.04)
14355
0.034
Yes
Yes
49
-0.003
(-0.34)
0.007
(0.83)
0.000
(0.09)
0.008***
(2.67)
-0.010**
(-2.11)
-0.032***
(-3.45)
0.000***
(10.36)
0.013
0.000***
(0.69)
(3.64)
0.000** -0.000***
(2.42)
(-2.99)
0.000** -0.001***
(2.42)
(-6.53)
0.001*** -0.000***
(5.49)
(-6.74)
14355
14355
0.139
0.035
Yes
Yes
Yes
Yes
0.005
(0.80)
0.000
(0.04)
-0.001
(-0.14)
0.013
(0.69)
0.000**
(2.42)
0.000**
(2.41)
0.001***
(5.49)
-0.006***
(-12.42)
-0.000***
(-2.95)
14355
0.139
Yes
Yes