Do managers tacitly collude to withhold industry-wide bad news?
Jonathan L. Rogers
University of Colorado
Catherine Schrand
University of Pennsylvania
Sarah L. C. Zechman
University of Chicago
That managers would choose to withhold firm-specific bad news is not only intuitive, but supported
by theory, observed disclosure patterns, and survey responses. When signals are uncertain and may
be revised in future periods, firms may choose to withhold the news to avoid presumably costly
stock return volatility. This strategy, however, may not work when news contains a common
industry component. When adverse news is industry wide, traders can infer signal arrival from the
disclosure of any firm in the industry which increases capital market pressure to disclose. A
cooperative equilibrium of non-disclosure requires tacit collusion. We document industry
characteristics associated with industry-level financial reporting opacity. We also document intraindustry clustering of increases in the annual 10-K opacity, controlling for changes in fundamentals
that we suggest are driven by disclosure choice consistent with tacit collusion. These opacity
episodes are more likely in industries that tend to have large, correlated, negative shocks, industries
with more significant equity incentives, and greater litigation risk. The opacity episodes are less
likely in industries in which observable/public macro-economic data relevant to firm valuation is
available. The results have implications for understanding when economic forces are sufficient to
generate voluntary disclosure of industry-wide adverse news.
First draft: February 2013
This draft: April 2014
Preliminary and incomplete
The authors thank Paul Fischer, Mirko Heinle, and audiences at Clemson, Cornell, Duke, Iowa, MIT, Northwestern,
Ohio State University, Rice, and Singapore Management University for helpful comments. The authors thank Sehwa
Kim for research assistance on this project. The authors gratefully acknowledge the financial support of the University
of Chicago Booth School of Business (Jonathan Rogers and Sarah Zechman) and the Neubauer family (Sarah Zechman).
1. Introduction
This paper investigates determinants of financial reporting opacity related to industry-wide
information. Fundamental characteristics of the firm’s environment such as complexity, uncertainty, or
investment opportunities, affect its opacity.
However, voluntary disclosure choice also plays a
significant role in determining opacity. Firms can choose to alleviate inherent opacity by aggregating
and more transparently disseminating private information.
Alternatively, firms cans choose to
exacerbate inherent opacity by withholding or obfuscating information. Research on the determinants
of the strategic element of opacity frequently motivate the analysis with adverse selection models of
voluntary disclosure choice that assume firm-specific valuation signals (e.g., Dye, 1985; Jung and Kwon,
1988; Shin, 2003 and 2006). Broadly speaking, these models predict that firms with relatively favorable
news will choose to disclose it, essentially becoming more transparent. The assumption that news
arrival is uncertain, however, allows firms with relatively adverse news to maintain opacity by choosing
not to disclose.
Our analysis of opacity is distinct on two dimensions. First, we assume valuation signals
contain an industry-wide component. The assumption of an industry-wide component in valuation
signals is realistic because of correlated fundamentals due to similar production functions, demand and
supply conditions, investment opportunities, and exposure to external factors such as regulation.
Analyst specialization within industries is consistent with there being a common component to the
valuations (O’Brien, 1990; Dunn and Nathan, 2005). Second, we make the analysis a more realistic
representation of a manager’s disclosure decision by considering disclosure choice in a repeated game
where the signal is uncertain and future revisions are possible. In a single stage game with second
mover costs, coordinated opacity is not possible. Firms will move to the non-cooperative (“prisoners’
dilemma”) equilibrium in which all firms disclose. Assuming a repeated game allows us to consider
whether coordination in disclosure decisions could support a cooperative equilibrium of non-disclosure
when news is industry wide and to predict associated industry characteristics. We refer to cooperative
1
disclosure decisions for industry-wide valuation signals as “tacit collusion”, consistent with the literature
on price and quantity decisions in product markets. 1
An analysis of the determinants of opacity, assuming valuation signals contain an industry-wide
component, makes a distinct contribution to existing studies on the determinants of disclosure because
the firm-specific evidence cannot simply be aggregated to make inferences about industry-level
evidence. 2
The single stage adverse selection models have uncorrelated signals.
They do not
accommodate industry-wide signals, and their predictions do not translate to correlated signals.
Three features of industry-wide signals affect the translation. First, when signals contain an
industry-wide component, disclosure by even one firm in the industry eliminates uncertainty about
signal arrival for other firms in the industry, which is necessary to prevent full disclosure in adverse
selection models. Thus, capital market pressure to disclose is stronger when valuation signals are
industry wide. Second, it is possible that no firms in the industry receive a firm-specific valuation signal
that is higher than the expected valuation without disclosure. In this case, no firm will have an
individual incentive to disclose and to start the unraveling. Mean shifts in the posterior distribution are
not possible in traditional adverse selection models that assume uncorrelated signals. Third, the costs
of non-disclosure are greater when signals are industry wide. In traditional adverse selection models,
non-disclosing firms suffer stock price declines when the valuation adjusts to the conditional expected
value. With industry-wide signals, however, a non-disclosing firm will face an additional cost if a peer
firm chooses disclosure. Litigation costs are one example. Plaintiffs in a 10b-5 case can use the
disclosure by the first mover in an industry as evidence to support an allegation that the second mover
omitted a material fact. Lower financial reporting credibility is another example of a cost imposed by
the first firm to disclose on a second mover. The industry-wide nature of signals generates externality
1
As described in Ivaldi et al. (2003), footnote 2: ‘ “Tacit collusion” need not involve any “collusion” in the legal sense, and
in particular need involve no communication between the parties. It is referred to as tacit collusion only because the
outcome … may well resemble that of explicit collusion or even of an official cartel. A better term from a legal perspective
might be “tacit coordination”.’
2 Empirical and survey evidence about firm-specific news suggests that firms withhold adverse news (Kothari, Shu, and
Wysocki, 2009; Sletten, 2012; Tse and Tucker, 2010; Graham, Harvey and Rajgopal, 2005).
2
costs such that an individual firm’s costs of disclosure depend on the strategic disclosure decisions of
other firms in the industry. 3
Our analysis of reporting opacity in this more realistic setting is motivated by the debate about
an appropriate framework for mandated disclosure and predicts distinct determinants relative to those
predicted by traditional single stage adverse selection models with uncorrelated signals. Both the
Financial Accounting Standards Board (FASB) and the Securities Exchange Commission (SEC) are
reviewing the current structure of disclosure mandates. The FASB cites as its goal to establish “…an
overarching framework intended to make disclosures more effective and coordinated and less
redundant.” (FASB exposure draft, 2014). 4
The policymakers face two significant issues in developing a conceptual approach to disclosure
regulation. One issue is the extent to which disclosure mandates are necessary at all. Proponents of
mandates believe requirements are necessary because market forces, including capital market pressure
due to adverse selection and litigation risk, are not sufficiently strong to motivate firms to voluntarily
collect or report relevant information, in particular, adverse information. Opponents, however, have
long argued that these market forces encourage voluntary disclosure, even of adverse news, such that
disclosure mandates impose unnecessary costs on firms (Easterbrook and Fischel, 1984). A second
issue standard setters face is how to narrow the requirements in order to balance the benefits and costs,
which differ across reporting entities. One approach is tiered requirements based on firm attributes
such as size or methods of raising capital. Another approach is to set forth disclosure mandates for
specific types of transactions or events based on an assessment of the related benefits and costs.
Our empirical analysis first documents industry-level financial reporting opacity and intraindustry clustering in opacity for the Fama-French 49 (FF49) industries. We use FOG scores to
Dye (1985) discusses externality costs.
The SEC states that its current review of disclosure 10-K requirements can lead to “simplification” and “modernization,”
which are necessary because the current requirements are costly. The SEC was required to conduct a review of disclosure
requirements for emerging growth companies as part of the JOBS Act. However, the SEC states that they decided a
comprehensive review of the current disclosure structure could lead to suggested changes that would benefit all issuers.
3
4
3
measure opacity. FOG scores are a measure of the readability of a document, and readability of
financial documents has been associated with opacity (Bloomfield, 2002; Li, 2008). This analysis
provides descriptive evidence about firm fundamentals that show intra-industry correlation with
opacity. One notable finding is that firm size, which is a proposed category for tiered disclosure
requirements, is not systematically related to opacity across industries. Size is significantly positive in
only 35% of the industry models, and it is significantly negative in 23%. Two firm characteristics
exhibit a consistent association with opacity levels across industries – special items and the number of
non-missing items on Compustat as a measure of financial complexity both are consistently associated
with greater opacity. The consistent relations across industries between opacity and these two variables,
both of which represent the nature of specific transactions or complexity of specific firm activities,
together with the inconsistent relations between opacity and firm-level attributes such as size, informs
the standard-setting issue of how to narrow disclosure requirements.
The intra-industry analysis of opacity levels provides evidence on the inherent element of
opacity, but cannot be used to test for determinants related to disclosure choice. Correlated opacity
could reflect correlated fundamentals, and correlated fundamentals are an underlying assumption of our
predictions about tacit collusion. To distinguish the element of opacity that reflects disclosure choice
by firms in an industry, we develop a measure of “opacity episodes” that represent industry-years with a
significant increase in the intra-industry average level of 10-K FOG scores that is not explained by
changes in firms’ fundamentals.
By examining increases in FOG scores after controlling for
fundamentals we attempt to mitigate concerns that correlated fundamentals, rather than coordination in
disclosure choice, determine correlated opacity.
The number of opacity episodes that we identify is small. Across the FF49 industry groups
over the 14 year period from 1995 to 2008, we find 19 episodes in which the average increase in intraindustry disclosure opacity suggests coordinated opacity (17 when using only single segment firms). We
also identify 12 of the 19 episodes in which at least 60% of influential firms in the industry exhibit
4
positive unexplained FOG levels.
The small number of episodes is consistent with collusive
equilibriums being inherently unstable due to the reliance on conjectures about other firms’ strategic
disclosure decisions. We provide weak evidence that future-period cumulative adjusted returns for the
industries with opacity episodes are significantly more negative than for matched samples starting in the
second year after the episodes. Negative subsequent returns are consistent with our interpretation of
the opacity episodes as a period in which firms did not disclose adverse valuation signals.
Our primary analysis investigates industry characteristics associated with the identified opacity
episodes. We examine characteristics motivated by our discussion of the market forces that shape
disclosure such as capital market pressure due to adverse selection and litigation risk when valuation
signals contain an industry-wide component and are uncertain. Five results emerge. First, opacity
episodes are (weakly) more likely in industries in which firms are subject to significant industry-wide
negative shocks, measured by negative tail risk. The effect is magnified in more homogenous industries
as measured by the average relative weight of industry-wide news to idiosyncratic news in the industry.
This result suggests that industries with a greater propensity for negative shocks that are felt similarly by
peer firms are more likely to conjecture that non-disclosure is a sustainable equilibrium, making tacit
collusion and resulting opacity episodes more likely.
Second, opacity episodes are more likely for industries characterized by firms with high equity
incentives, which motivate firms with adverse signals to remain opaque in order to maintain an elevated
stock price. We measure equity incentives by the proportion of firms in the industry that trade on a
major exchange (i.e., in the CRSP database) relative to the proportion filing annual reports (i.e., in the
Compustat database). An alternative interpretation of the positive association between opacity episodes
and our proxy is that industries with a lower proportion of publicly traded firms (i.e., firms file annual
reports despite not having publicly traded equity) are subject to significant disclosure regulation,
perhaps from regulators or other users that demand financial statements. Firms in such industries will
5
conjecture that other firms will disclose making tacit collusion in opacity decisions less sustainable.
Supplemental analysis favors the first interpretation.
Third, opacity episodes are less likely in industries in which valuation signals are public, or likely
to become public quickly, though this finding is significant for only one definition of opacity episodes.
We identify such industries based on the explanatory power of a returns model that includes market
returns and seven observable public macro-economic signals (i.e., short-term interest rates, default
spreads, term spreads, foreign exchange rates, producer price index, and the Fama-French SMB and
HML factors). When news is public, or the duration until it becomes public is short, the benefits of
opacity decrease (and should be common knowledge), leading to less coordinated opacity.
Fourth, opacity episodes are positively associated with our proxy for industry-level litigation
risk. We interpret the positive association as an indication that our litigation risk proxy, which is based
on a model of suits brought against firms, captures uncertainty in the industry-level propensity for large
adverse shocks. Firms in more uncertain industries are more likely to believe that future valuation
signals may increase (i.e., the news will not be as bad as anticipated), and that this potential is common
knowledge, increasing the sustainability of correlated opacity as an equilibrium. Our model of the
determinants of the opacity episodes also includes a separate proxy for industry-level uncertainty
implied by option prices. However, we find no association between more volatile industries and the
opacity episodes, and our litigation risk proxy remains significant in its presence.
Finally, opacity episodes are weakly positively related to industry concentration. We include a
revenue-based herfindahl index in the model because industry concentration could be related to several
industry characteristics affecting opacity choice. Greater concentration could increase the probability
that firms conjecture other firms have received a similar negative signal. Greater concentration could
also be correlated with fewer influential firms in the industry, which provides greater opportunities to
form beliefs that the other influential firms will cooperate, perhaps because of explicit collusion, greater
interpersonal connections, or because a focal point is more likely available to facilitate cooperation.
6
Finally, greater concentration could imply more significant existing barriers to entry, which decreases
each firm’s incentives to disclose adverse signals relative to industries with low entry barriers.
Several analytical models have analyses related to the issue of disclosure of industry-wide news.
Dye and Sridhar (1995) analyze disclosure choice when the timing of the arrival of private signals is
correlated across firms in an industry, but the signal values are uncorrelated.
Jorgensen and
Kirschenheiter (2012) analyze disclosure of industry-wide news with an exogenous leader and follower
firm. The leader anticipates the disclosure decision of the follower, but there are no opportunities for
signal revisions. The closest analysis is by Acharya, DeMarzo, and Kremer (2011) who examine
disclosure when signal values have a market-wide component and predict that firms will delay
disclosure of adverse news. 5 Our analysis has two distinguishing features. First, the ultimate release of
the news in their model is exogenous, and second, they do not allow for signal revisions. Acharya et al.
(2011) discuss the possibility that the news is endogenously released via another firm’s disclosure,
however, they only conjecture that the results would hold, stating: “The construction of the equilibrium
presents a significant computational challenge.”
Section 2 provides a conceptual discussion of the determinants of opacity when valuation
signals contain an industry-wide component and managers make disclosure decisions in a repeated
game anticipating the potential for signal revision. Section 3 describes our measures of intra-industry
opacity and our method for identifying opacity episodes. Section 4 presents the results of the analysis
of the determinants of the opacity episodes and Section 5 concludes.
2. Discussion of determinants of industry clustering
Two factors determine reporting opacity: 1) fundamentals or inherent opacity, and 2)
managerial choice. The notion that managerial choice affects opacity is consistent with the “incomplete
Both Dye and Sridhar (1995) and Acharya et al. (2011) predict a clustering of bad news announcements because disclosure
by one firm will trigger disclosure by the multiple firms that withheld adverse news. Tse and Tucker (2010) provide
empirical evidence of clustering in bad news announcements.
5
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revelation hypothesis” of Bloomfield (2002). In a noisy rational expectations equilibrium with costly
and endogenous information acquisition, opacity hinders efficient price discovery. Managers will make
financial reporting decisions that increase the costs of extracting information that the manager does not
want aggregated into price.
Firm fundamentals that affect financial reporting opacity are interesting in their own right, and
we will analyze their association with reporting opacity at the industry level. The discussion in this
section relates to strategic disclosure choice and opacity. In Section 2.1 and 2.2, we discuss how the
assumptions of an industry-wide valuation component and potential for revised signals alter predictions
about how capital market pressure affects disclosure choice. In Section 2.3, we discuss how these two
assumptions alter predictions about how litigation risk affects disclosure choice. The purpose of the
discussion is to motivate the determinants of opacity that result from strategic interactions.
2.1 Industry-wide valuation signals
We first discuss capital market pressure when valuation signals contain a common industrywide component. Traditional adverse selection voluntary disclosure models with trader uncertainty
about signal arrival form the basis for the discussion (e.g., Dye, 1985; Jung and Kwon, 1988; Shin, 2003
and 2006). 6 In these models, the manager receives a private firm-specific valuation signal and chooses
whether to disclose it. Traders respond to disclosure and silence (i.e., non-disclosure), taking into
account the strategic behavior of managers. The stock price of a firm that discloses adjusts to the
disclosed amount.
The stock price of a non-disclosing firm will adjust to the expected value
conditional on non-disclosure. Adverse selection models predict a partial disclosure equilibrium in
which firms will not disclose if their valuation signal is below an endogenous threshold level that
equates the valuation given disclosure to the expected valuation given non-disclosure. The intuition is
that firms with adverse news are able to hide in the pool of firms that have no news because of the
assumption that traders are uncertain about signal arrival.
6
We refer to models with trader uncertainty about signal arrival simply as “adverse selection” models.
8
These adverse selection models assume that firms’ private signals are uncorrelated.
By
definition, industry-wide signals are positively correlated across firms in an industry; the valuation signal
contains a common industry component.
That valuations would have a common intra-industry
component is sensible. Firms within an industry are likely to have relatively homogeneous production
functions, to face similar supply and demand conditions, to have similar investment opportunities, and
to face similar exposure to external factors such as regulation.
Considering industry-wide rather than firm-specific valuation signals does not alter predictions
about a firm’s disclosure choice of a favorable signal. Assuming correlated signals does, however, alter
predictions about disclosure of adverse signals. With industry-wide signals, traders can infer signal
arrival for one firm in an industry based on the disclosures made by another firm in the same industry.
If even one firm in the industry receives a firm-specific valuation signal that is higher than its
unconditional expected valuation despite the adverse industry-wide component, that firm will disclose.
The disclosure will cause traders to infer that all firms in the industry received a signal, which in turn,
affects the conditional expected value for the non-disclosers. The uncertainty about signal arrival,
which generates the partial disclosure equilibrium in the threshold models, disappears and adverse
selection should lead each manager to disclose. The idea that traders would make such intra-industry
inferences is consistent with the evidence that investment analysts tend to specialize in one industry
(O’Brien, 1990) and that analysts who focus on one industry produce better forecasts than analysts
focusing on multiple industries (Dunn and Nathan, 2005).
The additional capital market pressure when signals contain an industry-wide component results
in more disclosure.
If firms’ private signals contain only the industry-wide component and no
idiosyncratic component, adverse selection leads to full unraveling and full disclosure. If firms’ private
signals contain an idiosyncratic component in addition to the industry-wide component, then the
disclosure threshold will be lower than the threshold in traditional adverse selection models that assume
uncorrelated signals.
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The discussion thus far suggests industry-wide signals will be associated with stronger capital
market pressure relative to uncorrelated signals. However, in the traditional models, at least one firm
will receive a private signal that is greater than the unconditional expected value and disclose it. When
the signals contain a common industry component, however, the private valuation signals received by
all firms in an industry could be less than their existing stock price valuations. No firm will individually
want to disclose, and if the distribution shift is common knowledge, unraveling will not start. 7
At this point, one could argue that firms’ conjectures about the disclosure choices of other
firms are irrelevant. Firms that receive adverse signals choose non-disclosure. If a peer discloses, the
firm responds with disclosure. The firm may incur an immediate stock price reaction prior to the
response, which would be incorporated into the determination of the endogenous disclosure threshold.
We propose, however, that being the second mover imposes additional costs on a firm, besides the
immediate stock price reduction, when firms’ valuation signals contain a common industry component.
One example of an externality imposed on the second mover is lost financial reporting credibility. Lost
reputation is greater when a firm can be compared to a peer firm. An individual manager that
withholds materially adverse news can be subject to SEC enforcement. Individuals identified in
enforcement actions lose their jobs, face restrictions on future employment, and incur pecuniary costs
including fines and valuation losses on shareholdings (Karpoff et al., 2008a). Individuals can also face
criminal charges and jail time (Karpoff et al., 2008a; Schrand and Zechman, 2012). Karpoff et al.
(2008b) document reputational penalties at the firm level and characterize the direct legal costs as
minimal, but the loss in firm value associated with the reputational effects of the misconduct as “huge.”
Litigation costs are another example of an externality imposed on the second mover. One
element of a 10b-5 case is to show that the defendant omitted a material fact. The disclosure of
7
This observation is not unlike a point made by Dye and Sridhar (1995) in the “industry-wide common knowledge” case.
They analyze disclosure choice when the timing of the arrival of private signals is correlated across firms, however, the signal
values are uncorrelated. Full non-disclosure is a possible equilibrium, but only if firms “know” that other firms will not
disclose (p. 166).
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industry-wide news by a peer firm in the industry can provide useful evidence to support this claim. If
one firm in the industry discloses, plaintiffs are more likely able to establish that the non-disclosing firm
knew the information (or was reckless in not knowing it), which is otherwise difficult to prove. It may
also be easier for a plaintiff to establish materiality given that the disclosing firm assessed the
information as material. While litigation risk affects even a disclosing firm, as will be discussed later, we
propose that an industry-wide component in valuation signals creates incremental litigation costs for a
non-disclosing firm because the revelation of the adverse news by a peer firm could justify a stronger
case for the plaintiff.
In summary, when valuations contain an industry-wide component greater capital market
pressure arises because trader uncertainty about signal arrival disappears when another firm in the
industry discloses. However, this capital market pressure may not induce disclosure if the industry-wide
component is sufficiently adverse such that no firms have individual incentives to disclose their
valuations.
The unraveling that occurs in adverse selection models would not start.
The costs
associated with non-disclosure, including litigation and reputation costs, could be greater when news is
industry wide. The externality costs of being the second mover in the disclosure game are an additional
force that can lead to greater disclosure.
2.2 A repeated game
We next consider how the anticipation of opportunities to disclose revised signals in subsequent
periods affects managers’ incentives to disclose valuation signals. Dye and Sridhar (1995) and Acharya,
DeMarzo, and Kremer (2011), discussed previously, have multiple periods, but the future periods only
represent additional opportunities to disclose the initial signal; revised signals do not arrive. We
consider the implications of disclosure choice in a repeated game because we are interested in
identifying industry conditions and economic forces that shape disclosure in the realistic setting in
which firms make disclosure decisions. Incorporating the potential for signal uncertainty in evaluating
disclosure decisions accords with survey evidence in Graham et al. (2005) that managers hide adverse
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news “…in hopes that the firm’s status will improve before the next required information release,
perhaps saving the company the need to ever release the bad information...” (p. 65).
Because valuation signals are uncertain, the manager can assess positive probability to an
adverse signal being lessened or never materializing. Anticipation of subsequent revisions introduces
an important tension in the manager’s disclosure decision. If the manager and industry peers do not
disclose the manager avoids costly interim-period stock price volatility. However, if the manager does
not disclose but another firm does, the manager faces externality costs associated with being the second
mover. In a single stage game with second mover costs, coordinated opacity is not possible. Firms will
move to the prisoners’ dilemma equilibrium in which all firms disclose.
In a repeated game, it is possible for firms to coordinate and collectively choose non-disclosure
or opacity despite the potential costs of being the second mover. Coordinated opacity requires each
individual manager assign positive probability to reversal, conjecture that other managers have similar
beliefs, believe they share these beliefs, and so on. As such, “softer” issues affecting beliefs about
disclosure by other firms will determine whether the cooperative equilibrium is sustainable. Focal
points make a cooperative equilibrium more sustainable (Pindyck and Rubinfeld, 1989). 8 Frequent
interactions between the players also facilitate cooperation (Ivaldi et al., 2003). Perhaps the softest of
the variables is trust. Cooperation is more likely when firms can build trust over time that other firms
will make the cooperative choice. The observed coordination in firms’ disclosure decisions is referred
to as “collusion” in the product market literature, but the use of this term does not imply that firms
directly communicate their strategies (i.e., “explicitly” collude). Tacit collusion only implies that the
manager’s disclosure choice depends on her beliefs about the behavior and beliefs of other firms.
Our predictions about when correlated opacity is sustainable are similar to predictions of tacit
collusion in product markets that use a repeated game setting. Unlike product market games, however,
In fact, Pindyk and Rubinfeld (1989) give the prescient example that LIBOR could serve as a focal point, allowing banks to
cooperate.
8
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firms’ disclosure decisions do not have strategic interactions that affect the level of payoffs to
cooperation or the level of externality costs. 9 The focus of our analysis is on the disclosure of the
adverse signal, not on tacit collusion in operating, investing, or financing decisions as a reaction to the
signal. 10 This idea is in the background in our analysis, though, because of the assumption that firms
anticipate receiving new signals in subsequent periods and that the beliefs are common knowledge.
The reliance on conjectures makes coordinated equilibrium strategies inherently unstable, as evidenced
by the numerous empirical studies on tacit collusion or cartel formation in product markets and the
explanations for why cartels dissolve. 11
2.3 Litigation risk
Adverse selection models feature capital market pressure as a market force that affects
disclosure choice; litigation risk is another. A common assertion is that firms disclose adverse news
early to mitigate litigation risk (e.g., Skinner, 1994). The empirical evidence is mixed. Skinner (1994)
finds evidence consistent with this argument, but Francis, Philbrick, and Schipper (1994) do not find
evidence that preemptive disclosure mitigates litigation risk. Skinner (1997) then examines a broader
sample and finds support consistent with Francis et al. (1994) but suggests that, while issuing forecasts
may not deter litigation, it may reduce the costs.
This line of reasoning suggests that litigation risk is a market force that reduces the need for
mandated disclosure of firm-specific news. The implications of litigation risk are unclear, however, in a
repeated game setting and when signals contain an industry-wide component. Litigation risk could be
an even stronger market force than predicted by firm-specific models when adverse signals contain an
Another difference between our assumed payoff structure and those in models of price or quantity decisions is that tacit
collusion in product market decisions typically describes the cooperative equilibrium that arises when firms can credibly
retaliate in a future period against firms that deviate (Bertomeu and Liang, 2008). The threat of retaliation does not exist in
the disclosure setting.
10 Rajan (1994) is an example of a model of firms’ choices about disclosures of industry-wide news in the context of a
specific strategic decision. The disclosure choice is a bank’s decision about recording credit reserves, which serves as a
signal to other banks that make lending decisions about the overall level of credit quality in the industry. The analysis
focuses on the implications of the recorded reserves for strategic lending and other decisions, which affect payoffs and
business strategy, and the overall availability of credit.
11 See Levenstein and Suslow (2006) for a review.
9
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industry wide component. If firms in an industry conjecture that at least one firm in the industry will
disclose due to litigation risk, the potential for externality costs associated with being a second mover
will favor greater disclosure. Litigation risk could mitigate or exacerbate the incentives for firms to
preemptively disclose adverse news in a repeated game relative to the predictions from a single stage
game. On one hand, as the probability that the signal will materialize or be discovered declines,
expected litigation costs also decline, favoring non-disclosure.
But Skinner (1994) suggests that
litigation costs are increasing in the duration of withholding because a longer withholding period means
a longer class period.
3. Measuring intra-industry opacity and identifying opacity episodes
The variable of interest is reporting opacity. We use readability as an indication of opacity, and
our proxy for readability is the Gunning FOG index for firms’ 10-Ks. The FOG index is computed as
a function of the average number of words per sentence and the percent of words with three or more
syllables. The computation is intended to result in a score that represents the number of years of
education required to read the document. We obtain 10-K FOG scores as used in Li (2008) from Feng
Li’s website (http://webuser.bus.umich.edu/feng/). His sample is derived from the intersection of
firms available in the CRSP and Compustat databases and with electronic filings on EDGAR. Li’s
FOG score is calculated using the text (excluding heading items, paragraphs less than one line, and
tables) for all 10-K filings with more than 3,000 words or 100 lines of remaining text (Li, 2008). The
data provided on the website has been updated to include all observations meeting these requirements
that were filed with the SEC through December 2009 (or with fiscal year ends through mid-2009).
Empirical evidence provides some support for using the FOG index as a measure of opacity.
Callen, Khan, and Lu (2013) show that a higher FOG score (lower readability) is associated with greater
price reaction delay to a firm’s 10-K, where price reaction delay is measured by how much of the firm’s
return can be explained by lagged market returns. Lehavy, Li, and Merkley (2011) show that higher
14
FOG scores (lower readability) are associated with higher analyst forecast dispersion, lower forecast
accuracy, and analyst uncertainty. They interpret their evidence as consistent with FOG scores being
associated with information that is more difficult to process.
We proxy for opacity using poor readability rather than using non-disclosure for two reasons.
First, extant studies show that termination of previously disclosed information is costly (e.g., Chen,
Matsumoto, and Rajgopal, 2011), and second, Reg. S-X requires firms to discuss various topics, but the
content is discretionary. In addition, a readability index like the FOG score is objective and can be
measured on a large sample, as discussed in Lehavy et al. (2011).
The first element of our analysis examines industry fundamentals associated with opacity. For
this purpose, we estimate intra-industry models of FOG score levels. Section 3.1 describes the model
and results. In Section 3.2, we describe the model we use to identify unexplained changes in industrylevel reporting opacity. The procedure creates a set of “opacity episodes.” In Section 4, we estimate
the industry characteristics that are determinants of the opacity episodes using a logit model. The
proposed determinants are motivated by the discussion in the previous section.
3.1 Modeling levels of intra-industry opacity
We estimate the determinants of intra-industry opacity using the following model:
𝐹𝑂𝐺𝑗𝑡 = 𝛼0 + ∑9𝑐=1 𝛿𝑐 𝐶𝑂𝑁𝑇𝑅𝑂𝐿𝑗𝑡𝑐 + 𝜖𝑗𝑡
(1)
where FOG is a firm’s 10-K FOG score. The unit of observation is firm (j) in year (t). We estimate the
model annually for each of the FF49 industries from 1994 to 2008 using two samples. The FULL
sample includes all firm-year observations with available data. The 1SEG sample excludes firms with
multiple business segments, where segments are defined at the FF49 level. 12 The vector CONTROL
includes nine variables, identified by Li (2008), that represent firm fundamentals: the log of market
value of equity, the market to book ratio, special items scaled by total assets, return volatility, the
We use the Fama French 49 industry level to identify single segment firms in order to mitigate the effects of SFAS 131,
which substantially reduced the number of single segment firms beginning in 1998.
12
15
number of non-missing data items in Compustat as a measure of complexity, firm age, an indicator for
Delaware incorporation, the log of the number of geographic segments plus one, and the log of the
number of business segments plus one. (See variable definitions in Appendix A.)
Table 1 reports coefficient estimates by industry for the FULL sample (Panel A) and for the
1SEG sample (Panel B). At the top of each panel, we report the number of coefficient estimates that
are significantly positive, significantly negative, and insignificant for each control variable. We present
results for 48 industry-level regressions, excluding “Tobacco Products” because of a small sample
size. 13 The first notable finding is that firm size, which is a proposed category for tiered disclosure
requirements by the FASB, is not systematically related to opacity across industries. Li (2008) indicates
a positive association on average in his pooled sample across industries, but size is significantly positive
in 35% of the industry models and significantly negative in 23%.
The only determinants that have relatively consistent explanatory power across industries are
special items scaled by total assets and the number of non-missing data items in Compustat. Firms
with larger special items, which are generally negative, have consistently less readable financial reports
(higher FOG scores) across industries, consistent with the findings in Li (2008). Firms with more nonmissing items, which are presumably more complex, also have consistently higher FOG scores or lower
readability across industries. This result is opposite to the negative association in the pooled sample in
Li (2008), but it is consistent with his prediction that greater financial complexity is associated with
greater opacity. Special items and non-missing items are distinct from the other determinants we
explore in this specification, none of which show consistent patterns across industries, in that they
represent specific types of transactions or activities rather than attributes of the firms. The intraindustry analysis therefore suggests that tiered disclosure requirements based on firm attributes may not
identify the disclosure conditions that are most at risk of opacity. Instead, an approach focusing on
The Tobacco Products industry (Fama-French industry 5) has 54 industry-year observations in the FULL sample panel
(an average of less than four per year in the 15 year sample period) and only 14 observations in the 1SEG sample panel.
13
16
characteristics of events or transaction would be more likely to alleviate sources of opacity that are
consistent across industries.
Table 2 reports measures of intra-industry dispersion in FOG for each of FF49 industries. We
measure dispersion as the intra-industry coefficient of variation (standard deviation/mean) and as the
quartile coefficient of dispersion (Q3 - Q1/median). We measure dispersion separately for the FULL
sample and the single segment firms (1SEG) over the period 1994 through 2008. Lower dispersion
implies greater clustering of readability within the industry. We evaluate whether the industry clustering
is unusually high or low by testing the equality of the variance in each industry against the variance for
the pooled sample of firms in all industries. The coefficient of variation for the pooled sample
reference group in the FULL sample is 0.0742 (Panel A) and 0.0734 in the 1SEG sample (Panel B).
The last column of Table 2 indicates whether the industry exhibits more significant clustering/less
dispersion (“c” or “cc”) or more significant dispersion (“x” or “xx”) than the reference group. 14
The industries with the most significant clustering are Consumer Goods (9), Healthcare (11),
Precious Metals (27), Computer Software (36), and Insurance (46). Additional industries that exhibit
weaker evidence of clustering are Apparel (10), Pharmaceutical Products (13), Chemicals (14),
Communication (32), and Business Services (39).
3.2 Identifying opacity episodes
The object of interest in the primary analysis is coordinated opacity that results from voluntary
disclosure choice rather than correlated fundamentals. Li (2008) concludes that the FOG measure, in
levels, may capture intentional opacity. He documents that higher FOG score levels are associated with
lower earnings and lower earnings persistence for profitable firms and with earnings decreases. Based
on this evidence, he concludes that managers may be strategically disclosing to “hide adverse news.”
14 We compute three statistics to test the equality of the variances: Bonferroni, Scheffe, and Sidak. The Bonferroni and
Sidak statistics provide identical results. The Scheffe statistic rejects equality of variances for fewer industries. An “x” or
“c” means that equality of variances is rejected by the Bonferroni and Sidak statistics. An “xx” or “cc” means that the
equality of variances is also rejected by the Scheffe statistic.
17
While his evidence is consistent with FOG scores representing opacity choice, fundamentals such as
complexity or realized poor performance that are omitted from his FOG model could also drive these
results (Bloomfield, 2008).
In an effort to distinguish the correlated opacity that results because of strategic disclosure
choice given an industry-wide valuation component and a repeated game, we identify a set of industryyear observations that exhibit significant unexplained increases in intra-industry FOG scores. We
modify eqn. (1) to measure industry changes in FOG scores, controlling for changes in fundamentals.
We estimate eqn. (2) for rolling two-year windows from 1994 through 2008
𝐹𝑂𝐺𝑗𝑡 = 𝛼0 + 𝛼1 𝑌𝐸𝐴𝑅2 + ∑9𝑐=1 𝛿𝑐 𝐶𝑂𝑁𝑇𝑅𝑂𝐿𝑗𝑡𝑐 + 𝜀𝑗𝑡
(2)
where YEAR2 is an indicator for the later of the two years. This model allows us to identify industryyears with unexplained changes in FOG. An industry-year is considered an “opacity episode” in the
latter of the two years if the coefficient estimate on the YEAR2 indicator ( α 1 ) is positive and
significant (p-value < 0.10). For use in later falsification tests, we also identify “transparency episodes.”
An industry-year is considered a transparency episode (TRANSP) in year 2 if α 1 is negative and
significant (p-value < 0.10). This analysis generates 19 industry-year observations of opacity episodes
for the years 1995 through 2008 and 13 transparency episodes.
We identify a second set of opacity and transparency episodes by estimating eqn. (2) using only
single segment firms based on the FF49 industries (“1SEG” sample). This method identifies 17 opacity
episodes and 12 transparency episodes. We expect the parameter estimates when estimating eqn. (2)
with the 1SEG sample to provide better controls for the fundamental firm characteristics that affect
FOG, thus providing a cleaner measure of the change in abnormal FOG.
Table 3 reports the industry distribution of opacity and transparency episodes estimated using
both the FULL and 1SEG samples. Tables 3 and 4 report episodes by industry and year, respectively.
Technology-related industries (35, 36, and 37; hardware, software, and chips), are over-represented in
18
the opacity episodes, with all three industries showing industry-wide decreases in readability in 2002 and
2003. There is less obvious industry clustering in the transparency episodes. The opacity episodes
generally exhibit time clustering in 2002 and 2003. We do not expect that required disclosures related
to the Sarbanes-Oxley Act of 2002 (SOX) are the source of this clustering given that the specific
provisions were not effective until later and the general provisions should have affected all industries.
However, as a precaution, we estimate the logit model of the determinants of the opacity episodes
including non-opacity observations only for years in which there is at least one opacity episode.
We also identify a third set of opacity episodes that is a subset of the FULL sample episodes.
In addition to the on-average increase in industry-wide unexplained FOG, we require the majority of
influential firms in the industry exhibit significant unexplained increases in FOG. To identify influential
firms in each industry-year, we find the lowest number of firms such that the sales concentration ratio
is greater than or equal to 50%. For example, if the concentration ratio for the largest four (six) firms is
40% (55%), then the number of influential firms in the industry is six. We apply the coefficient
estimates from eqn. (1) for industry i in year y-1 to the control variables for the influential firms in
industry i in year y to estimate their predicted FOG. A positive abnormal FOG, equal to actual FOG
less predicted FOG, indicates relatively high disclosure opacity for that year. This procedure identifies
12 of the original 17 opacity episodes that also have at least 60% of influential firms with abnormally
high FOG levels. We expect these episodes to provide a cleaner measure of strategic opacity.
Our indirect approach to identifying periods of opacity is preferred to other more direct
approaches. We cannot rely on observed ex post adverse outcomes and work backwards to examine
whether the adverse signal was withheld. Such a sample selection method would identify only those
episodes in which the adverse signals eventually materialized. But the discussion in Section 2 suggests
that correlated opacity is more likely when there is greater uncertainty about the eventual realization of
the signal. Thus, such sampling would be biased against identifying periods in which firms chose
19
opacity. We also cannot examine reporting decisions using concurrent performance – if the firm is
recognizing the concurrent performance it is effectively disclosing the signal.
3.3 Returns tests
We use a traditional portfolio method to investigate the relation between opacity episodes and
subsequent returns. We compare the returns of firms in industry i following an opacity episode to
those of a matched sample described below. We expect to observe poor returns, on average, following
opacity episodes if our episodes capture opacity that obfuscates adverse industry-wide valuation signals.
This analysis is inherently weak because opacity episodes are more likely when firms believe the adverse
signal will not materialize (or will be better than originally anticipated). Poor returns on average are
consistent with strategic opacity, although not finding poor subsequent returns does not preclude
strategic opacity; the initial adverse valuation signal may not have materialized. Also, we do not make
predictions about the timing of the subsequent poor returns, although we do not expect them to be
immediate. Strategic opacity is positively related to the duration that the stock price remains artificially
elevated. If firms had rationally anticipated that the valuation signal would be made public quickly, we
should not observe correlated opacity.
We measure subsequent returns as the cumulative abnormal size-decile adjusted returns (CARs)
in months 1, 3, 6, 12, 24 and 36 after month four following a year end. We compute CARs for an
opacity sample and a matched sample. The opacity sample consists of firm year observations in
industry i and year y if the i-y industry-year combination is an opacity episode based on the 1SEG
sample. The matched sample consists of all other firm-year observations, excluding firms in industry i
in years y+1 through y+3. For example, if an industry has an opacity episode in 2002, firms in the
industry are excluded from the matched sample in 2003-2005. This exclusion ensures that returns for
the opacity sample firms do not confound the y+1 and y+2 returns for the matched sample. We
examine return performance for all firms in the industry, including firms that were not in the
20
determination of the opacity episodes due to data availability, and we do not require firms to have data
for all three subsequent years.
Table 5 reports the average firm-level CARs for the opacity sample and matched sample
portfolios for the 15 industry-year opacity episodes (OPACITY1SEG = 1). Panel A presents average
CARs for portfolios that include only single segment firms and exclude new entrants to the industry,
defined as firms with returns available as of month four following the opacity episode year (y) but with
missing returns as of December year y. 15 Panel B presents average CARs for single and multi-segment
firms, which significantly increases the sample size in our tests, but at the cost of less specific returns
related to the industry segment subject to the adverse valuation signal.
In the opacity sample portfolios in Panels A and B, there are 1,750 (2,246) observations for
single segment (single and multi-segment) firms with data available to compute CARs in the first month
following the opacity episodes aggregated across the 15 opacity episodes. This number declines to
1,295 (1,708) by month +36. In both panels, the CARs of firms in the opacity sample turn negative
(but not significant) by the end of the second year following the episode. The CARs for the opacity
sample are significantly greater than for the matched sample at each date up to one year following the
episode (Ret12mon). By Ret24mon, however, the opacity sample CARs are significantly lower than
those of the matched sample.
Panels C and D report CARs for cross-sections based on the propensity of the industry to
experience adverse valuation signals. We identify such industries based on expected return skew
implied by option prices. The procedure designates 17 high negative tail risk (positive skew) industries,
or approximately 35% of the FF49 industries. The industries are designated with an asterisk in
Appendix B. Bifurcating the opacity and matched samples based on the likelihood of an industry-wide
adverse signal is meant to reduce noise in the matched sample by eliminating industries that are unlikely
We also create a set of firms that includes single segment new entrants to the industry. All results (untabulated) are
qualitatively the same, suggesting that new entrants and existing firms were similarly affected by the industry-wide valuation
component.
15
21
to have had adverse signals. We expect the differences in average CARs to be more pronounced when
comparing only high negative tail risk industries.
Panel C reports CARs for single segment firms in high negative tail risk industries (comparable
to Panel A). Except in month +1, the differences between the opacity and matched sample portfolios
are insignificant in the first two years, in contrast to the higher CARs for the opacity sample reported in
Panel A. The CARs turn significantly negative for the opacity sample, but not for the matched sample,
in Ret24mon, and the difference in CARs between the two samples is significantly negative in
Ret36mon. Panel D shows that the low tail risk industries drive the positive differences in CARs
between the opacity and matched sample portfolios in months 1, 3, 6, and 12 in Panel A. In fact, in the
low tail risk industries, the opacity sample firms have significantly higher CARs than the matched
sample in Ret36mon.
In untabulated analysis, we adjust CARs for performance-related delistings. These missing
observations will create a bias in our sample that would work against finding subsequent poor returns
in the opacity sample if the missing observations are firms with the most egregious adverse news. We
replace observations missing due to performance-related delistings with the 5th percentile CAR for the
same FF49 industry in year y. The replacement of missing values with the 5th percentile is made for
both the opacity and matched sample portfolios. The delisting codes (from CRSP) we use to identify
performance-related delistings are 400 to 500 and 520 to 584 (Shumway, 1997). We assume delistings
for other reasons (e.g., changes in exchanges) do not create a bias in our portfolio returns. In Panels A
and B, the CARs are lower, by construction, for both the opacity and matched sample portfolios, but
the differences in CARs are similar in magnitude and significance.
Overall, we find some evidence of poor subsequent returns for firms following opacity
episodes. The analysis shows the episodes can empirically distinguish industries that realize poor
returns two to three years following the episode, which provides some validation that the ex ante
identified episodes are associated with yet unrealized adverse signals. Our analysis of average realized
22
returns understates the amount of adverse news if the signal was either partially mitigated or never
realized. Firms may have chosen opacity anticipating that the adverse signal would not materialize.
4. Determinants of opacity episodes
We estimate the determinants of opacity episodes identified in Section 3 using a logit model. 16
The discussion in Section 2 motivates the industry characteristics that we include in the model as
explanatory variables, all of which are measured at the industry or industry-year level.
OPACITYiy=α0 + δ1TAILRISKi + δ2IND_WIDEiy + δ3TAILRISKi*IND_WIDEiy +
δ4VOLATILITYiy + δ5PUBLICi + δ6%CRSPiy + δ7LIT_RISKiy + δ8HERFiy +εiy
(3)
We estimate the model separately for opacity episodes identified using the FULL sample
(OPACITY=1 and OPACITYINFL=1) and the 1SEG sample (OPACITY1SEG=1).
The dependent
variables equal zero for remaining industry-year observations and are missing if data were not available
to estimate the FOG model. An industry-year is also set to missing for industry i in years y+1 through
y+3 if the industry was defined as an opacity episode in year y. This exclusion reduces noise in the nonopacity observations, since opacity in year y may continue in subsequent years. The logit model
includes only years with at least one opacity episode. These restrictions yield 17 opacity episodes (out
of the original 19) for the FULL sample, 12 opacity episodes with influential firms, and 14 opacity
episodes (out of the original 17) for the 1SEG sample. Two OPACITY1SEG episodes are lost because of
repeat years and the 1995 episode is lost because the litigation risk proxy is available starting in 1996.
4.1 Explanatory variables and predictions
We measure all explanatory variables at the industry or industry-year level consistent with our
goal of characterizing industries likely to have opacity episodes. Whether opacity is a sustainable
equilibrium depends not only on each firm’s own incentives and costs, but also on the firm’s
16
We also estimate the model using a probit specification with consistent results.
23
conjectures about other firms’ disclosure decisions. If firms conjecture that it is in the best interest of
all firms in the industry to maintain opacity, then opacity is a sustainable equilibrium. If a firm
anticipates that a peer firm will deviate and disclose the adverse signal, no firm will be willing to
withhold the industry-wide news, fearing that other firms will disclose it first and impose externality
costs on the non-discloser. Because the sustainability of an opacity equilibrium depends on conjectures
about the costs and benefits for all firms in the industry, we measure the explanatory variables at the
industry or industry-year level. Appendix A provides details of the variable construction. Appendix B
reports the variables by industry.
TAILRISK: The first explanatory variable is an indicator equal to one for industries in which
firms are more likely to have adverse or complex news. We identify industries with high negative tail
risk based on expected return skew implied by option prices as described previously. We use an
industry indicator rather than an industry-year indicator because we use market data to construct the
variable. While industry tail risk may be time variant, creating an annual measure requires assumptions
about when the market realizes the news that is reported opaquely. We expect firms in high tail risk
industries to have a greater propensity to receive an adverse industry-wide signal, which increases an
individual firm’s incentives to maintain opacity. If this industry characteristic is common knowledge,
capital market pressure may not induce disclosure, making opacity episodes more likely.
IND_WIDE: The second explanatory variable proxies for the degree to which valuation signals
in an industry have a common component.
When the industry-wide component is likely to be
significant relative to the idiosyncratic component, individual firms can more easily infer other firms’
incentives. Even if this industry characteristic is common knowledge, however, the implication for the
sustainability of coordinated opacity is ambiguous. On one hand, it is more likely that managers
receiving an adverse signal will conjecture that other firms in the industry have received an adverse
signal, and conjecture that the industry-wide component is common knowledge. This line of reasoning
suggests that homogeneity makes coordinated opacity more likely. However, homogeneity also increases
24
the probability that traders will infer industry-wide signal arrival from the disclosure of any individual
firm. In less homogenous industries, traders infer less from disclosure by one firm in the industry
about signal arrival for another firm, thus uncertainty about news arrival remains. 17
Our proxy for the level of industry homogeneity (or degree of industry-wide news) is the
negative value of industry-year heterogeneity. We compute heterogeneity as the annual average of the
standard deviation of residuals from monthly within-industry standard market models. This industryyear measure (IND_WIDE) represents the average industry-wide component of returns relative to the
idiosyncratic component. The mean (median) reported in Table 6 Panel A is -0.16 (-0.16). Equation
(3) also includes the interaction of IND_WIDE with the industry-level indicator for high tail risk as it is
likely that the intersection of these characteristics has a stronger effect on coordinated opacity.
VOLATILITY: The third explanatory variable, industry return volatility, is included in the
model for two reasons. First, we expect volatility to be correlated with fundamental uncertainty and
thus with valuations that are inherently more difficult to describe. Second, we expect industry-wide
return volatility to affect disclosure incentives. The more uncertain the valuation signals, the more
likely it is that future signals will at least partially reverse. An adverse signal may never be revealed if the
source of the shock is not realized (e.g., a decline in average customer credit quality reverses prior to
significant credit losses.) Uncertainty increases the incentives for each firm to withhold adverse news.
If this industry characteristic is common knowledge, capital market pressure may not induce disclosure,
making opacity episodes more likely in high-volatility industries. We measure industry return volatility
as the average implied volatility of at-the-money options for firms in an industry following Van Buskirk
(2011). We use an industry indicator rather than industry-year indicator because we use market data to
construct the variable.
17
This discussion mirrors a case considered in Dye and Sridhar (1995). In their main analysis, the timing of the arrival of
private signals is correlated, but the signal values are uncorrelated, and firms do not learn whether other firms have received
a signal. In their comparative static analysis related to the number of firms (n) in the industry (Theorem 2a), two forces are
at play. As n increases, traders infer less from the disclosure by any one firm about the probability that a non-discloser has
received a private signal, but adverse selection motivates more firms to disclose. The second effect always dominates and
the threshold is decreasing in n.
25
PUBLIC: The fourth explanatory variable is a proxy for the expected duration until the
valuation signal is revealed. The longer the expected duration of opacity, the greater is the expected
value of the artificially elevated stock price. The expected duration depends on two factors. First, the
duration depends on the uncertainty of the news. The more uncertain the valuation signal, the less
likely the signal will be revealed. The VOLATILITY variable, described previously, represents this
input to duration. Second, the duration depends on whether, and if so how quickly, the withheld news
is likely to be made public by external parties (e.g., an analyst or the media) or by a public
announcement of macro-economic data (e.g., GDP figures that are relevant to firm valuation).
To compute PUBLIC, we estimate a factor model for each of the FF49 industries that includes
the equal-weighted market index and the equal-weighted industry index plus seven macro-economic
risk factors: short-term interest rates, default spreads, term spreads, a foreign exchange factor, a
producer price index, and the Fama-French SMB and HML factors. The factors represent observable
public signals. The power of these observable factors to explain returns for firms in the industry
provides a proxy for the degree to which news about the industry is likely to be public. The variable
PUBLIC is the adjusted-R2 from the factor model. Table 6 reports that the average (median) adjustedR2 used to measure the amount of information in industry returns conveyed through observable public
sources is 12.2% (11.0%). Appendix B (final column) reports the PUBLIC variable by FF49 industry.
Seven industries stand out as having substantially higher adjusted R2s: coal, precious metals, tobacco
products, defense, shipbuilding/railroad equipment, petroleum and natural gas, and electronic
equipment. The seven industries with the smallest adjusted R2s are: food products, entertainment,
consumer goods, healthcare, construction materials, personal services, and wholesale. We expect
opacity episodes are decreasing in PUBLIC.
%CRSP: The fifth explanatory variable proxies for industry-level incentives for firms to
maintain an artificially elevated stock price.
In industries in which more managers have equity
incentives, any individual manager is more likely to have incentives to remain opaque. If this industry
26
characteristic is common knowledge, capital market pressure may not induce disclosure, making opacity
episodes more likely in high-incentive industries. Our industry-year proxy for equity incentives is the
number of firms in the industry with data on CRSP divided by the number of firms with data on
Compustat (%CRSP). For CRSP, we count the firms in each industry-year with greater than 200 daily
return observations. For Compustat, we count the firms in each industry-year in the fundamentals
annual file with an SIC code, sales, and total assets. Firms in industries with a higher percent of firms
on CRSP are less likely to disclose adverse signals and more likely to conjecture that other firms in the
industry have similar incentives.
Appendix B presents the average annual %CRSP measures by
industry. Industries with low percentages are: Shipbuilding/Railroad Equipment, Rubber and Plastic
Products, Utilities, and Apparel, ignoring the “Almost Nothing” industry.
Industries with high
percentages are: Banking, Precious Metals, and Trading. The average (median) reported in Table 6 is
122.4% (110.3%) and the inter-quartile range is 88.9% to 125.8% (not reported).
LIT_RISK: The litigation risk variable is motivated by several factors. Litigation risk encourages
individual managers to disclose adverse signals. If litigation risk pressure is common knowledge,
opacity episodes are less likely ceteris paribus. Litigation risk, however, is likely to be a function of the
uncertainty of valuation signals. Thus, in a repeated game setting the implications of litigation risk are
ambiguous. Expected litigation costs decline as the probability that the shock will materialize or be
discovered declines, increasing the sustainability of tacit collusion. If, however, litigation costs are
increasing in the duration that the signal is withheld, and this is common knowledge, opacity episodes
are less likely. Our proxy for litigation risk (LIT_RISK) uses Model 3 from Table 7 of Kim and Skinner
(2012), which is intended to measure the ex ante probability of litigation. See Appendix A for a
description of their model and our sample. We estimate LIT_RISK at the firm level for fiscal years
between 1996 and 2008 and create an industry-year average.
HERF: The model includes a revenue-based herfindahl index as a measure of industry-year
concentration. We include concentration in the model as a summary measure of various industry
27
characteristics that could be associated with incentives or opportunities for coordinated opacity. 18
Greater industry concentration indicates that there are a smaller number of larger and perhaps more
influential firms in an industry, which may enable easier coordination due to explicit collusion
opportunities or because it is more likely that a focal point is available. 19 Greater concentration may
also be associated with firms’ conjectures that other firms have received a similar signal and believe
each firm has similar conjectures, and that the signal affects all other firms in the same direction. In
addition, greater industry concentration can be associated with high entry barriers, in which case firms
collectively have incentives to remain opaque.
Table 6 Panel B reports the correlation matrices for the explanatory variables separately for the
observations that will be included in the FULL sample logit analysis and in the 1SEG sample logit
analysis. The correlations with industry size are also reported although industry size is not included in
the model. 20 Our measures of uncertainty (VOLATILITY) and homogeneity (IND_WIDE) are highly
correlated in both samples. We run the analyses without the VOLATILITY measure and results
remain qualitatively the same using all three measures of opacity episodes (untabulated). In addition,
our control variable for litigation risk (LIT_RISK) is highly correlated with a number of the other
independent variables. We consider the implications of these correlations in the analysis and discuss
the results in the next section.
4.2 Results
Table 7 reports the marginal effects from estimating the logit model of determinants. Panel A
presents results when we define opacity episodes based on significant increases in average industry
We also compute an asset based herfindahl index and the concentration of the top four, six, or eight firms in each
industry and year based on market shares of total sales and assets. See Appendix A for details. We report the herfindahl
index as it gives some weight to “medium-sized” firms, however, results are consistent with concentration ratios.
19 Ivaldi et al. (2003) note this explanation for a relation between industry concentration and collusion even in product
markets but claim that there is little evidence. Instead, concentration ratios are commonly predicted to be associated with
tacit collusion in product markets because concentration determines the profitability of a collusive price (or quantity)
strategy relative to the non-collusive strategy, and the short-term profits from deviating, both of which affect firms’
equilibrium beliefs about whether the collusive product-market strategy is sustainable.
20 We estimate industry size based on the number of firms listed in Compustat for the respective industry-year.
18
28
FOG using the FULL sample (OPACITY). Panel B column (1) presents results when we define
opacity episodes as the subset of opacity episodes in which the influential firms have significant positive
unexplained FOG (OPACITYINFL). Panel B column (2) presents results when we identify opacity
episodes using the 1SEG sample that excludes firms with multiple business segments (OPACITY1SEG).
In Panel A, there are 17 industry-year opacity episodes and 120 non-opacity industry-year observations.
In Panel B, there are 12 (14) industry-year opacity episodes and 125 (112) non-opacity industry-year
observations using the OPACITYINFL (OPACITY1SEG) measures.
The first result is that opacity episodes are (weakly) positively related to the industry’s
propensity for significant industry-wide negative shocks (TAILRISK) using both OPACITY and
OPACITYINFL. This relation is magnified when the firms in the industry are more homogenous as
measured by the interaction between TAILRISK and IND_WIDE. Opacity episodes are significantly
negatively related to the main effect of intra-industry homogeneity (IND_WIDE) in Panel A, though
the predicted association was ambiguous and we find no results in either specification in Panel B. On
one hand, valuation signals in homogeneous industries are more likely to have a common component
that affects all firms similarly and have the effect be common knowledge, which makes withholding
more sustainable. On the other hand, intra-industry homogeneity increases the likelihood that investors
will infer industry-wide news from the disclosure by one firm, increasing the costs of withholding and
making coordinated opacity less sustainable.
The second result is that opacity episodes are more likely in industries with greater equity
incentives (%CRSP).
We observe this result using both OPACITY and OPACITYINFL.
One
explanation for the positive association is a numerator effect – industries characterized by greater equity
incentives, and thus greater benefits of maintaining an artificially inflated stock price, exhibit
coordinated opacity. A second explanation, however, is that the result is driven by the denominator
effect – industries in which firms file annual reports despite not having publicly traded equity. If such
industries are subject to significant mandated disclosure requirements, and this industry characteristic is
29
common knowledge, firms in these industries are less likely to view opacity as a sustainable equilibrium.
Therefore, opacity will (endogenously) not be sustainable. Additional analysis in Section 4.3 attempts
to disentangle these explanations.
The third result is that opacity episodes are less likely when industry information is more
publicly available, though this finding is only significant when using the OPACITYINFL proxy for
opacity episodes. Greater availability of public information shortens the expected duration of the
elevated stock price, thus decreasing the likelihood that correlated opacity is sustainable. In short, more
observable public information deters firms that receive adverse valuation signals from coordinating
their disclosure decisions and collectively remaining opaque.
The propensity for an opacity episode is increasing in litigation risk.
This result is not
consistent with the assertion that litigation risk is a market force that encourages disclosure. Our proxy
for litigation costs (LIT_RISK) is based on a model of suits that were brought against firms in an
industry. Although the litigation risk model controls for fundamentals including return skew and return
volatility, the positive sign is consistent with the proxy for litigation risk reflecting greater uncertainty
about valuation signals. Higher uncertainty will lead firms to believe that future valuation signals may
increase and that this outcome and these beliefs are common knowledge, which increases the
sustainability of opacity as an equilibrium.
As noted previously, LIT_RISK is highly correlated with several explanatory variables. We
estimate eqn. (3) excluding LIT_RISK (untabulated). The results for these other variables remain
qualitatively similar with the following exceptions. Using the FULL sample (OPACITY), the results
related to TAILRISK, IND_WIDE, and the interaction term are more significant and the results related
to %CRSP weaken slightly, although they are still significant at conventional levels.
Opacity episodes are positively related to industry concentration (HERF) for the episodes
identified by the single segment firm sample in Panel B. There are several explanations for a positive
association. Higher concentration could reflect a smaller number of influential firms such that firms are
30
more likely to conjecture that other firms will share similar adverse signals and that these conjectures
are common knowledge. Higher concentration could also be correlated with softer factors that can
sustain cooperation, such as the existence of a focal point. Finally, higher concentration could imply
greater barriers to entry, which decrease firms’ incentives to disclose.
Opacity episodes are not related to industry uncertainty as measured by return volatility
(VOLATILITY). We expected that increased uncertainty could increase managers’ collective beliefs
that an adverse valuation signal would reverse and thus increase the duration of the artificially inflated
stock price.
4.3 Additional analyses
Our first additional analysis is meant to evaluate whether changes in correlated fundamentals
omitted from the FOG model identifying opacity episodes explain our findings.
We conduct a
falsification test using the transparency episodes (TRANSP) described in Section 3, which represent
periods of industry-wide significant decreases in FOG (i.e., increases in transparency).
If omitted
correlated fundamentals explain our findings, we expect the explanatory variables in the logit model to
explain transparency episodes as well as opacity episodes. The industry-level explanatory variables that
we investigate are predicted to be associated with increases in correlated opacity that reflect disclosure
choice, but they should not predict increases in correlated transparency unless they measure correlated
fundamentals.
There are 13 significant industry-year decreases or transparency episodes and 167 nontransparency observations. Table 8 column (2) reports the marginal effects from estimating the logit
model of determinants. The results for the opacity episodes are reported in column (1) for convenient
comparison.
None of the variables that have explanatory power for the opacity episodes have
explanatory power for the transparency episodes.
Our second additional analysis is meant to distinguish two explanations for the positive
association between %CRSP and opacity episodes reported in Table 7.
31
Our motivation for
investigating %CRSP as a determinant of opacity episodes is that firms in industries with high equity
incentives are more likely to tacitly collude. While the positive association between opacity and %CRSP
is consistent with this explanation, a second explanation is that firms in industries with low %CRSP are
less likely to collude because of existing mandatory disclosure regulation. If firms believe that peer firms
will make the valuation signals public due to mandatory disclosure, then each individual firm has
incentives to disclose and coordinated opacity is not sustainable.
In an attempt to distinguish these two explanations, we analyze whether the positive relation
between %CRSP and opacity episodes comes from the low end or the high end of the distribution of
%CRSP (or both). Table 9 reports the results for opacity episodes defined using the FULL sample
(Panel A) and for the subset of opacity episodes for which influential firms have abnormal FOG (Panel
B). We do not present the results for the single segment sample using OPACITY1SEG due to the small
sample and lack of results for the %CRSP variable in this specification in Table 7.
We use two model specifications. In the first test specification, we expand the logit model to
include an interaction term that distinguishes the low end of the %CRSP distribution. We identify high
and low equity incentives using an indicator variable (LOW) equal to 1 for observations that are less
than or equal to the median industry-year level of %CRSP. The results in Column (1) show the
marginal effect of %CRSP declines but remains positive and significant. The marginal effect of the
interaction term (%CRSP*LOW) is negative but not significant. It is worth noting that the marginal
probabilities for the other explanatory variables in the analysis in Column (1) of both panels are similar
to those in Table 7 in terms of sign, magnitude, and significance.
In the second specification, we estimate the logit model separately for industry years with low
(LOW = 1) and high (LOW = 0) equity incentives. 21
We use this specification because the
interpretation of interaction terms in logit models can be unreliable (Ai and Norton, 2003). In both
panels, the marginal effect of %CRSP is significantly positive for industry-years with high equity
21
The results are the same if we define low (high) equity incentives as %CRSP<1 (%CRSP≥1).
32
incentives (Column 3) and insignificant for industry years with low equity incentives (Column 2). These
results suggest that the explanatory power for the opacity episodes derives from the high end of the
%CRSP distribution, which represents managers who are more likely to benefit from keeping the
adverse signal hidden from equity markets, and who believe other firms in the industry share those
benefits and beliefs.
5. Conclusion
This study empirically examines the determinants of financial reporting opacity. The analysis is
distinct from existing studies in that we assume firms’ valuation signals contain an industry-wide
component and we assume managers make disclosure decisions anticipating future opportunities to
disclose and the potential for signal revision. These two assumptions provide a realistic depiction of
the setting in which managers operate and inform the debate on disclosure mandates. In particular, the
assumption of an industry-wide component in valuation signals would suggest that capital market
pressure to disclose adverse signals is strong enough such that disclosure mandates are unnecessary.
However, in a repeated game setting that allows opportunities for tacit collusion, opacity can be an
optimal disclosure choice. Opacity, therefore, is determined by strategic interactions in disclosure
choice among firms in an industry. Because tacit collusion depends on the relative magnitudes of the
costs and benefits of disclosure for each individual firm and the firm’s conjectures about these costs
and benefits for all firms in the industry, whether we expect to see withholding of industry-wide bad
news in practice is an empirical question.
We do observe periods of correlated opacity that are not explained by changes in correlated
fundamentals, which suggests that market forces are not entirely sufficient to induce full disclosure.
The number of “opacity episodes,” however, is small.
Of 500 (444) possible industry-year
combinations for the full (single segment) sample of firms, we find between 12 and 17 opacity episodes
(depending on the sample and method of identification) in which an increase in intra-industry
33
disclosure opacity suggests tacit collusion. Two years after the identified opacity episodes, we see
evidence of significantly lower adjusted returns for the samples of opaque firms. These results suggest
that the opacity represents adverse news.
Our analysis of the determinants of the identified opacity episodes suggests the following. First,
the opacity episodes are more likely in industries that face a greater likelihood of a significant negative
shock, especially if the firms in these industries are more homogenous. Second, withholding is more
likely in industries with a greater proportion of firms having equity incentives, in which the benefits of
maintaining an elevated stock price are more likely to exceed the costs of withholding, and this would
be common knowledge. Third, there is weak evidence that withholding is less likely in industries in
which the news is likely to be public or become public quickly. Finally, withholding is more likely in
industries with greater litigation risk.
34
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36
Table 1. Determinants of FOG scores by industry
OLS estimates from pooled regressions of eqn. (1) by Fama-French 49 industry over the years 1994 through 2008. Firm-year FOG scores are regressed on: Log of
market value of equity (MVE), Market to book ratio (MTB), = Number of years a firm has been listed on CRSP (Firm age), Special items scaled by total assets (Special
items), Standard deviation of monthly stock returns for the 12 months ending in the third month of the current year (Return volatility), Number of non-missing items in
Compustat as a measure of financial complexity (Non-missing items), an indicator = 1 if the firm was incorporated in Delaware (Delaware), log of the number of
geographic segments plus one (GEO segments), and log of the number of business segments plus one (BUS segments). Panel A presents results for the FULL sample.
Panel B presents results for the 1SEG sample. *, **, and *** equal 10%, 5%, and 1% significance at the 2-tailed level.
Panel A: FULL sample
Number of estimates:
Intercept
Positive, (p-value<0.10)
Negative, (p-value<0.10)
Not significant
By industry:
Agriculture
Food Products
Candy & Soda
Beer & Liquor
Recreation
Entertainment
Printing/Publishing
Consumer Goods
Apparel
Healthcare
Medical Equipment
Pharma. Products
Chemicals
Rubber/Plastic Prods
Textiles
Construction Mats
Construction
Steel Works Etc.
Fabricated Products
Machinery
Electrical Equipment
Automobiles/Trucks
13.092 ***
16.444 ***
15.707 ***
16.455 ***
18.169 ***
19.448 ***
19.040 ***
17.312 ***
18.987 ***
19.449 ***
18.079 ***
18.487 ***
17.533 ***
17.859 ***
20.482 ***
17.491 ***
19.123 ***
17.235 ***
18.931 ***
17.670 ***
17.013 ***
17.086 ***
MVE
MTB
Firm age
Special
items
17
11
20
6
11
31
12
12
24
1
13
34
0.170 *
-0.048
0.478 ***
-0.134 *
0.125 ***
0.177 ***
-0.265 ***
0.018
-0.068 *
0.045
-0.069 ***
-0.048 ***
0.013
-0.022
0.287 **
0.089 **
-0.081 **
-0.074 **
-0.001
-0.059 ***
0.055
-0.084 *
-0.062
-0.058
-0.297 **
0.246 ***
0.050
-0.117 **
-0.202 **
0.008
0.054
-0.051 *
0.032 **
0.001
-0.051
0.048
-0.462 **
0.041
0.083
-0.009
0.314
0.096 ***
-0.030
-0.024
0.005
0.001
0.047
0.005
0.013 **
0.015 **
0.008
0.002
0.007 *
-0.002
-0.014 ***
-0.002
-0.006 **
-0.002
0.016
0.004
0.008 *
0.009 ***
0.013 *
0.005 **
-0.011 **
0.001
37
-4.661 **
-1.516
-4.487 *
-3.012
-0.317
-1.466 **
0.315
-2.125 ***
-0.017
-0.556
-0.778 *
0.454 *
-1.119 *
-1.088
0.848
-0.125
-2.200 *
0.155
-1.551
-1.049 *
-0.252
-0.323
Return
volatility
Nonmissing
items
Delaware
GEO
segments
BUS
segments
12
6
30
32
1
15
20
5
23
4
18
26
9
8
31
2.408 *
0.167
6.048 *
-1.212
1.407 *
0.984
-1.244
1.197
-0.844
0.419
1.018 **
-0.733 ***
1.565 *
0.304
5.341 ***
1.421
-1.819 **
-0.561
0.51
0.784 *
0.739
0.944
0.011 **
0.009 ***
-0.009
0.009 **
0.003
0.000
0.007 ***
0.006 ***
0.001
0.003
0.008 ***
0.007 ***
0.008 ***
0.009 ***
-0.010 *
0.005 ***
0.004 *
0.009 ***
0.003
0.005 ***
0.010 ***
0.008 ***
-0.042
-0.021
2.100 ***
-0.409
-0.371 **
0.476 ***
0.431 **
-0.174 *
0.079
0.081
0.008
0.207 ***
0.209 **
0.421 ***
-0.236
0.262 **
0.216 *
0.081
0.135
0.176 ***
0.314 ***
-0.135
0.52
0.117
0.414
0.076
-0.275
-0.486 **
-0.144
-0.272 *
0.129
0.304
0.02
-0.178 **
-0.456 ***
-0.170
-0.677
-0.264 *
-0.337 **
0.071
-0.981 ***
0.112
-0.182
0.309 *
1.637 ***
0.439 **
1.126 ***
-0.305
-0.670 **
0.349
0.368 *
-0.109
-0.521 **
-0.036
0.022
0.349 *
-0.636 **
0.318
-0.109
0.332
-0.062
-0.158
-0.197
0.009
0.219
Aircraft
Ship/Railroad Equip.
Defense
Precious Metals
Mining
Coal
Petroleum/Nat Gas
Utilities
Communication
Personal Services
Business Services
Computers
Computer Software
Electronic Equip.
Meas/Control Equip.
Business Supplies
Shipping Containers
Transportation
Wholesale
Retail
Restaurants, Hotels
Banking
Insurance
Real Estate
Trading
Almost Nothing
15.940 ***
16.571 ***
16.990 ***
17.831 ***
18.396 ***
13.377 ***
18.574 ***
17.984 ***
18.859 ***
18.367 ***
19.227 ***
18.378 ***
18.478 ***
18.714 ***
17.605 ***
18.239 ***
19.106 ***
18.712 ***
18.691 ***
17.328 ***
18.748 ***
18.730 ***
19.334 ***
17.229 ***
18.550 ***
19.209 ***
0.274 ***
-0.148
0.302 ***
-0.015
0.029
-0.022
0.013
0.179 ***
0.040 *
0.033
-0.002
0.077 ***
-0.023 *
0.037 **
-0.027
0.055
-0.110
0.035
0.142 ***
0.062 **
0.059
0.115 ***
-0.042 **
0.102 **
0.077 ***
0.057
0.364 *
-0.116
-0.033
0.063
0.137
0.665 *
0.038
-0.564 ***
-0.070 **
-0.073
-0.012
-0.023
0.006
0.015
0.074 ***
-0.184 *
-0.020
0.007
-0.085 **
-0.062 *
-0.025
-0.186 ***
0.078
-0.004
0.015
0.026
-0.008
0.006
0.002
-0.017 ***
-0.005
0.033 *
0.007 **
-0.002
-0.004
0.005
-0.003
-0.014 ***
-0.007 ***
-0.009 ***
-0.001
0.007 *
0.025 ***
0.005 *
-0.006 **
-0.014 ***
0.006
-0.017 **
-0.001
-0.004
-0.011 ***
-0.010 *
38
0.537
0.224
0.322
0.508
1.264
-5.387 *
0.858
-3.389 *
0.389
-1.522
-0.259
0.102
0.253
-0.084
-0.431
-0.682
-4.129 *
-2.857 ***
-1.606 ***
-0.501
-0.145
0.129
-1.060
-0.970
0.009
-0.343
0.272
-0.571
-1.413
0.242
-0.838
-9.711
-0.484
2.351 **
0.494
-0.198
0.142
-0.324
-0.980 ***
-0.498 *
0.058
-0.073
1.938
-0.973
1.494 ***
0.079
0.348
1.376 *
-1.982 ***
3.073 **
-0.833 *
1.477 *
0.009 ***
0.013 ***
0.005
0.006 *
0.006
0.001
0.004 ***
0.005 ***
0.005 ***
0.005 **
0.004 ***
0.004 ***
0.007 ***
0.005 ***
0.009 ***
0.004 *
-0.001
0.001
0.001
0.008 ***
0.002
0.007 ***
0.006 ***
0.004
0.004 ***
0.004
0.646 ***
0.374
-1.299 ***
-0.007
-0.678 **
1.139
-0.049
-0.058
-0.089
0.311 **
0.174 ***
0.047
0.152 ***
-0.083
0.196 ***
0.518 ***
0.098
0.305 ***
0.12
0.242 ***
-0.166
0.099
0.116 **
0.04
0.322 ***
-0.342 **
-0.552 *
0.457
0.684
-0.349
-0.957 ***
2.022 ***
-0.261 ***
-0.087
0.100
-0.362 *
-0.289 ***
-0.269 **
0.174 ***
-0.157 ***
-0.473 ***
-0.572 ***
-0.221
-0.357 ***
0.086
-0.267 *
-0.008
-0.258
-0.049
0.865 **
0.193
-0.144
-0.403
-0.465
-0.974
-0.076
0.950 *
2.386 ***
0.190
0.007
-0.425 **
0.441 *
-0.048
0.570 ***
-0.279 **
-0.071
0.063
0.234
-0.054
0.099
-0.368 ***
0.214
0.002
-0.579 **
-0.368 ***
-0.167
-0.057
-0.168
Panel B: 1SEG sample
Number of estimates:
Intercept
Positive, (p-value<0.10)
Negative, (p-value<0.10)
Not significant
By industry:
Agriculture
Food Products
Candy & Soda
Beer & Liquor
Recreation
Entertainment
Printing/Publishing
Consumer Goods
Apparel
Healthcare
Medical Equipment
Pharma. Products
Chemicals
Rubber/Plastic Prods
Textiles
Construction Mats
Construction
Steel Works Etc.
Fabricated Products
Machinery
Electrical Equipment
Automobiles/Trucks
Aircraft
Ship/Railroad Equip.
Defense
Precious Metals
Mining
Coal
Petroleum/Nat Gas
Utilities
19.262 ***
16.361 ***
15.707 ***
18.414 ***
18.030 ***
18.898 ***
19.452 ***
18.010 ***
18.909 ***
19.155 ***
18.144 ***
18.572 ***
17.518 ***
18.303 ***
19.686 ***
16.738 ***
19.191 ***
17.813 ***
19.245 ***
17.219 ***
17.642 ***
17.585 ***
14.485 ***
18.554 ***
25.183 **
17.523 ***
19.556 ***
20.665 ***
18.752 ***
17.375 ***
Return
volatility
Nonmissing
items
Delaware
GEO
segments
MVE
MTB
Firm age
Special
items
12
10
26
7
6
35
9
10
29
2
8
38
8
8
32
27
0
21
19
4
24
2
13
33
0.068
-0.060
0.478 ***
-0.399 ***
0.082
0.245 ***
-0.271 ***
0.017
-0.105 **
0.069 **
-0.102 ***
-0.050 ***
0.015
0.019
0.325 **
0.021
-0.149 ***
-0.085
-0.088
-0.120 ***
-0.020
-0.073
0.184
-0.200
-1.333
-0.034
-0.212 *
-0.093
0.005
0.229 ***
0.175
-0.037
-0.297 **
0.195 *
0.022
-0.151 ***
-0.079
0.003
0.108 *
-0.053 *
0.033 **
-0.001
-0.016
0.053
-0.335
0.406 ***
0.115
-0.012
0.447
0.123 ***
-0.024
0.021
0.113
-0.177
-0.018
0.071
0.199
0.444
0.044
-0.166
-0.014
-0.001
0.047
0.030 **
0.034 ***
0.010
0.031 **
0.004
0.006
-0.005
-0.020 ***
-0.003
-0.002
-0.006
0.015
0.011
0.005
0.008 *
-0.007
0.002
0.003
0.016 ***
-0.017
0.017
0.069
-0.021 ***
0.013
0.033
0.010 ***
-0.004
-2.643
-1.711
-4.487 *
-0.875
0.151
-2.100 ***
0.494
-1.920 **
0.572
-0.692
-0.546
0.563 **
-1.356 *
-0.225
0.984
-1.582
-2.698 *
0.828
-1.407
-0.516
0.090
-0.572
3.024
-1.196
1.373
0.726
-1.373
-0.805
0.924
-4.206
1.497
0.398
6.048 *
-1.868
1.172
2.326 ***
-1.474
1.143
-1.367
0.771
0.704
-0.772 ***
2.067 **
-0.592
3.620
0.627
-2.060 **
0.522
-1.411
0.707
1.297
1.710
1.365
-5.511 **
-8.907
0.551
-2.846 *
-6.960
-0.702
5.666 ***
-0.002
0.011 ***
-0.009
0.009 *
0.003
-0.001
0.002
0.004 **
0.002
0.002
0.008 ***
0.007 ***
0.008 ***
0.008 ***
-0.010
0.005 *
0.006 **
0.008 ***
0.006
0.007 ***
0.007 ***
0.004
0.012 **
0.013 *
0.015
0.007 *
0.007
-0.014
0.003 *
0.004
-0.569 *
0.061
2.100 ***
-0.251
-0.286 *
0.380 ***
0.856 ***
-0.170
0.311 **
0.191
0.063
0.181 ***
0.141
0.390 **
-0.055
0.400 **
0.459 ***
0.029
-0.314
0.225 ***
0.292 *
-0.098
1.053 ***
0.026
0.918
0.049
-0.295
-0.058
-0.077
-0.673
0.222
0.414
0.007
-0.312
-0.391
0.138
-0.172
0.005
0.280
0.002
-0.187 **
-0.392 **
-0.859 ***
-0.149
0.208
-0.587 ***
-0.298
-0.872 *
0.235 *
-0.152
0.288
0.465
-0.877
-1.878
-0.202
-0.803 *
1.884
-0.202 *
-0.018
39
Communication
Personal Services
Business Services
Computers
Computer Software
Electronic Equip.
Meas/Control Equip.
Business Supplies
Shipping Containers
Transportation
Wholesale
Retail
Restaurants, Hotels
Banking
Insurance
Real Estate
Trading
Almost Nothing
19.470 ***
18.585 ***
19.274 ***
19.023 ***
18.410 ***
18.741 ***
17.672 ***
19.351 ***
19.884 ***
18.529 ***
18.549 ***
17.556 ***
18.879 ***
18.097 ***
19.153 ***
18.373 ***
18.724 ***
18.737 ***
-0.021
0.072
-0.014
0.065 ***
-0.027 **
0.032 *
-0.028
0.135 **
-0.169
0.047
0.123 ***
0.043
0.045
0.131 ***
-0.039 **
0.094
0.068 ***
0.112 **
-0.065 *
-0.089
-0.013
-0.024
0.006
0.013
0.072 ***
-0.164
-0.043
-0.034
-0.120 **
-0.028
-0.002
-0.169 ***
0.045
0.038
0.017 *
0.037
-0.001
0.001
-0.007 **
-0.020 ***
-0.007 **
-0.009 ***
0.003
0.015 **
0.043 ***
0.012 ***
-0.006
-0.016 ***
0.007
-0.023 **
0.002
-0.015 *
-0.011 ***
0.017
40
0.848 **
-0.983
-0.081
0.002
0.254
0.095
-0.650
-1.175
-4.266 *
-2.262 **
-1.745 **
-0.360
-0.154
-0.117
-0.547
-2.321
-0.061
0.201
0.185
-0.582
0.095
-0.402
-0.889 ***
-0.678 **
0.468
1.471
2.416
0.126
1.841 ***
-0.204
-0.804
1.748 *
-1.837 ***
4.735 ***
-0.917 **
1.897 *
0.003 **
0.008 ***
0.003 ***
0.003 **
0.007 ***
0.005 ***
0.009 ***
-0.001
-0.004
0.001
0.001
0.008 ***
0.002
0.006 **
0.005 ***
-0.002
0.003 ***
0.002
-0.097
0.056
0.188 ***
0.080
0.170 ***
-0.130 **
0.213 **
0.720 ***
0.567
0.195 **
0.116
0.213 **
-0.259 *
0.119
0.247 ***
-0.161
0.348 ***
-0.263
0.016
-0.863 ***
-0.187 **
-0.304 ***
0.136 **
-0.217 ***
-0.588 ***
-1.290 ***
-0.412
-0.186
0.130
-0.195
0.024
-0.037
0.065
0.752
0.156
-0.094
Table 2: Measures of FOG score dispersion by industry
Panel A (Panel B) presents measures of dispersion for each FF49 industry for the FULL sample (the 1SEG sample that excludes firms with
multiple business segments). CV is the coefficient of variation, which is the sample standard deviation/sample mean. QD is the quartile
coefficient of dispersion, which is quartile 3 – quartile 1 divided by the median. The last column indicates the significance of the clustering.
“c” indicates that equality of variances is rejected for the industry vs. the pooled sample reference group (n = 46,242 in the FULL sample
and 34,172 in the 1SEG sample) and the industry exhibits greater clustering. “x” indicates that equality of variances is rejected and the
industry FOG scores are more disperse. “cc” and “xx” are the same but based on a more conservative test statistic.
Panel A: FULL sample
Pooled sample reference group
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
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
Agriculture
Food Products
Candy & Soda
Beer & Liquor
Tobacco Products
Recreation
Entertainment
Printing and Publishing
Consumer Goods
Apparel
Healthcare
Medical Equipment
Pharmaceutical Products
Chemicals
Rubber and Plastic Products
Textiles
Construction Materials
Construction
Steel Works Etc.
Fabricated Products
Machinery
Electrical Equipment
Automobiles and Trucks
Aircraft
Shipbuilding, Railroad Equip.
Defense
Precious Metals
Non-Metallic/Ind. Metal Mining
Coal
Petroleum and Natural Gas
Utilities
Communication
Personal Services
Business Services
Computers
Computer Software
Electronic Equipment
Measuring & Control Equip.
Business Supplies
Shipping Containers
Transportation
Wholesale
Retail
Restaurants, Hotels, Motels
Banking
Insurance
Real Estate
Trading
Almost Nothing
n
46,242
CV
0.0742
QD
0.0901
SIG
145
575
54
122
54
415
706
367
889
592
950
1,569
3,024
879
444
146
903
625
684
195
1,641
662
679
201
91
91
151
149
53
1,710
1,359
1,353
538
2,571
1,294
4,047
2,705
1,060
556
149
1,215
1,810
1,725
542
577
2,090
364
2,995
407
0.0801
0.0726
0.0852
0.0632
0.0870
0.0741
0.0769
0.0778
0.0743
0.0729
0.0729
0.0681
0.0659
0.0705
0.0769
0.0898
0.0853
0.0690
0.0755
0.0790
0.0710
0.0728
0.0768
0.0798
0.0655
0.0835
0.0603
0.0755
0.0861
0.0772
0.0757
0.0729
0.0710
0.0702
0.0685
0.0648
0.0665
0.0624
0.0767
0.0665
0.0783
0.0778
0.0795
0.0747
0.0780
0.0657
0.0756
0.0783
0.0720
0.1049
0.0900
0.1307
0.0733
0.0924
0.1033
0.0961
0.1070
0.0926
0.0829
0.0875
0.0778
0.0767
0.0870
0.0946
0.1048
0.1077
0.0785
0.0890
0.0951
0.0836
0.0882
0.0914
0.0977
0.0658
0.0997
0.0686
0.0834
0.1202
0.0901
0.0888
0.0832
0.0850
0.0873
0.0769
0.0781
0.0779
0.0741
0.0904
0.0659
0.0961
0.0926
0.0952
0.0889
0.0963
0.0804
0.0907
0.0986
0.0968
x
cc
41
c
x
x
xx
cc
cc
cc
c
x
xx
c
xx
x
cc
x
c
cc
cc
x
c
xx
xx
x
cc
xx
c
Table 2, continued.
Panel B: 1SEG sample
Pooled sample reference group
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
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
Agriculture
Food Products
Candy & Soda
Beer & Liquor
Tobacco Products
Recreation
Entertainment
Printing and Publishing
Consumer Goods
Apparel
Healthcare
Medical Equipment
Pharmaceutical Products
Chemicals
Rubber and Plastic Products
Textiles
Construction Materials
Construction
Steel Works Etc.
Fabricated Products
Machinery
Electrical Equipment
Automobiles and Trucks
Aircraft
Shipbuilding, Railroad Equip.
Defense
Precious Metals
Non-Metallic/Ind. Metal Mining
Coal
Petroleum and Natural Gas
Utilities
Communication
Personal Services
Business Services
Computers
Computer Software
Electronic Equipment
Measuring & Control Equip.
Business Supplies
Shipping Containers
Transportation
Wholesale
Retail
Restaurants, Hotels, Motels
Banking
Insurance
Real Estate
Trading
Almost Nothing
n
34,172
CV
0.0734
QD
0.0892
SIG
77
403
54
66
14
301
564
174
625
444
743
1,363
2,790
557
259
95
398
354
398
108
1,043
349
367
75
50
21
136
84
23
1,260
494
940
361
1,882
1,062
3,570
2,256
809
277
59
967
1,157
1,394
400
407
1,554
241
2,775
275
0.0669
0.0754
0.0852
0.0639
0.1152
0.0751
0.0771
0.0705
0.0727
0.0720
0.0725
0.0681
0.0658
0.0689
0.0695
0.0894
0.0831
0.0730
0.0756
0.0778
0.0714
0.0698
0.0752
0.0852
0.0724
0.0807
0.0621
0.0703
0.0592
0.0773
0.0719
0.0725
0.0705
0.0698
0.0654
0.0637
0.0658
0.0617
0.0772
0.0735
0.0800
0.0775
0.0791
0.0733
0.0828
0.0631
0.0796
0.0779
0.0688
0.1006
0.0928
0.1307
0.0796
0.1515
0.1025
0.0981
0.0866
0.0906
0.0830
0.0908
0.0780
0.0763
0.0892
0.0781
0.0937
0.1039
0.0793
0.0891
0.0930
0.0847
0.0924
0.0821
0.1225
0.0596
0.0559
0.0714
0.0788
0.0832
0.0929
0.0858
0.0842
0.0780
0.0865
0.0751
0.0770
0.0751
0.0724
0.0890
0.0935
0.1010
0.0875
0.0933
0.0852
0.1022
0.0781
0.0955
0.0992
0.0944
c
xx
42
x
x
x
cc
c
cc
c
c
x
x
x
cc
x
cc
x
c
c
cc
x
x
xx
xx
x
cc
x
c
Table 3: Summary of opacity and transparency episodes by Fama-French 49 industry
The opacity (transparency) episodes represent a significant industry-level increase (decrease) in FOG identified in two-year
rolling regressions. Columns (1) and (3) show the episodes identified using the full sample (FULL); Columns (2) and (4)
show the episodes identified using the sample that excludes firms with multiple business segments (1SEG).
Opacity Episodes
Transparency Episodes
FF 49 Industry
FULL (1)
1SEG (2)
FULL (3)
1SEG (4)
1 Agric
Agriculture
2 Food
Food Products
1
2
3 Soda
Candy & Soda
4 Beer
Beer & Liquor
5 Smoke Tobacco Products
6 Toys
Recreation
7 Fun
Entertainment
1
8 Books Printing and Publishing
1
1
1
9 Hshld Consumer Goods
1
1
10 Clths
Apparel
1
11 Hlth
Healthcare
1
12 Medeq Medical Equipment
1
1
13 Drugs Pharmaceutical Products
1
1
1
1
14 Chems Chemicals
1
1
15 Rubbr Rubber and Plastic Products
1
1
16 Txtls
Textiles
17 Bldmt Construction Materials
1
18 Constr Construction
19 Steel
Steel Works Etc.
2
20 Fabpr Fabricated Products
1
21 Mach
Machinery
1
22 Elceq
Electrical Equipment
1
1
2
1
23 Autos Automobiles and Trucks
24 Aero
Aircraft
1
25 Ships
Shipbuilding, Railroad Equip.
26 Guns
Defense
27 Gold
Precious Metals
1
28 Mines Non-Metallic/Ind. Metal Mining
29 Coal
Coal
30 Oil
Petroleum and Natural Gas
31 Util
Utilities
32 Telcm Communication
1
1
1
1
33 Persv
Personal Services
34 Bussv Business Services
1
1
35 Hardw Computers
1
1
36 Softw
Computer Software
2
2
1
1
37 Chips
Electronic Equipment
2
2
38 Labeq Measuring & Control Equip.
1
39 Paper
Business Supplies
40 Boxes Shipping Containers
41 Trans
Transportation
42 Whlsl
Wholesale
1
1
1
43 Rtail
Retail
44 Meals
Restaurants, Hotels, Motels
45 Banks Banking
46 Insur
Insurance
1
47 Rlest
Real Estate
1
1
48 Fin
Trading
1
1
49 Other Almost Nothing
1
Total
19
17
13
12
43
Table 4: Summary of opacity and transparency episodes by year
The opacity (transparency) episodes represent a significant industry-level increase (decrease) in FOG identified in two-year
rolling regressions. Columns (1) and (3) show the episodes identified using the full sample (FULL); Columns (2) and (4)
show the episodes identified using the sample that excludes firms with multiple business segments (1SEG).
Year
1995
1996
1997
1998
Opacity Episodes
FULL (1)
1SEG (2)
Fama-French 49 industry Fama-French 49 industry
42 (whlsl)
22 (elceq)
7 (fun)
15 (rubbr)
22 (elceq)
9 (hshld)
12 (medeq)
13 (drugs)
22 (elceq)
36 (softw)
8 (books)
47 (rlest)
2000
32 (telcm)
2001
2003
2004
24 (aero)
32 (telcm)
34 (bussv)
35 (hardw)
36 (softw)
37 (chips)
46 (insur)
8 (books)
11 (hlth)
13 (drugs)
14 (chems)
20 (fabpr)
36 (softw)
37 (chips)
38 (labeq)
42 (whlsl)
48 (fin)
9 (hshld)
32 (telcm)
34 (bussv)
35 (hardw)
36 (softw)
37 (chips)
49 (other)
15 (rubbr)
2 (food)
12 (medeq)
13 (drugs)
22 (elceq)
36 (softw)
8 (books)
19 (steel)
47 (rlest)
19 (steel)
32 (telcm)
13 (drugs)
14 (chems)
21 (mach)
36 (softw)
37 (chips)
48 (fin)
22 (elceq)
2 (food)
10 (clths)
2005
2006
2007
2008
Total
27 (gold)
42 (whlsl)
1999
2002
Transparency Episodes
FULL (3)
1SEG (4)
Fama-French 49 industry Fama-French 49 industry
2 (food)
17 (bldmt)
19
17
13
44
12
Table 5: Returns following the opacity episodes
This table presents average cumulative abnormal size-decile adjusted returns (CARs) for portfolios of firms in the
opacity sample and the matched sample. The opacity sample consists of firm year observations in industry i in year y if
industry i-year y is an opacity episode determined by a significant industry-level increase in FOG in two-year rolling
regressions for the 1SEG sample (OPACITY1SEG). The matched sample consists of all other firm year observations,
excluding firms in industry i in years y+1 through y+3 if industry i-year y is an opacity episode. Returns are presented for
months 1-, 3-, 6-, 12-, 24- and 36 after month four (April) of the opacity episode year y. Panels A and B present average
CARs for single segment firms and single and multi-segment firms, respectively. Panels C and D present average CARs
for single segment firms in high (negative) TAILRISK and low TAILRISK industries, respectively. *, **, and *** equal
10%, 5%, and 1% significance at the 2-tailed level.
Ret1mon
Panel A: Single segment firms
Opacity sample: Return
0.0367***
N
1,750
Matched sample: Return
N
0.0043***
28,523
Difference in returns
0.0324***
t-value
6.72
Panel B: Single+multi-segment firms
Opacity sample: Return
0.0319***
N
2,246
Matched sample: Return
N
0.0033***
44,141
Difference in returns
t-value
0.0286***
6.98
Ret3mon
Ret6mon
0.0583***
1,716
0.0961***
1,667
-0.0015
28,020
-0.0042*
27,285
Ret12mon
0.0691***
1,596
Ret24mon
-0.0205
1,452
Ret36mon
0.0318
1,295
0.0036
25,929
0.0214***
23,437
0.0259***
20,578
0.0598***
7.62
0.1003***
8.77
0.0655***
4.02
-0.0419**
2.13
0.0059
0.23
0.0529***
2,204
0.0825***
2,142
0.0660***
2,060
-0.0148
1,893
0.0301
1,708
0.0004
42,350
0.0012
40,365
0.0821***
8.55
0.0648***
4.73
-0.0002
43,403
0.0531***
8.07
Panel C: High TAILRISK Single segment firms
Opacity sample: Return
0.0625**
0.0627
N
27
26
-0.0249
1.53
0.0116**
32,269
0.0185
0.87
-0.0260
23
-0.2979*
19
-0.3654***
18
-0.0289***
1,873
-0.0188
1,760
-0.0042
1,549
-0.0329
1,285
Difference in returns
0.0618*
0.0668
t-value
1.92
1.23
Panel D: Low TAILRISK Single segment firms
Opacity sample: Return
0.0589***
0.0989***
N
982
952
0.0728
0.93
-0.0072
0.04
-0.2937
1.58
-0.3325***
3.11
Matched sample: Return
N
0.0044***
28,128
-0.0013
27,649
Difference in returns
t-value
0.0545***
7.65
0.1002***
8.83
Matched sample: Return
N
0.0007
1,977
0.0439
25
0.0101***
36,720
-0.0041
1,936
0.1640***
919
45
0.1549***
870
0.0194*
790
0.1256***
695
-0.0021
26,938
0.0049
25,642
0.0226***
23,239
0.0282***
20,208
0.1661***
9.87
0.1500***
6.18
-0.0032
0.11
0.0974***
2.60
Table 6: Descriptive statistics for industry-level characteristics
This table provides descriptive statistics on industry-level characteristics that are the explanatory variables in the logit
model. Industry size is the average number of firms in the industry with non-missing sales and assets data on
Compustat. TAILRISK is an indicator variable that equals one for industries that have high negative expected return
skewness computed based on the methods in Van Buskirk (2011), and zero otherwise (see Appendix A). IND_WIDE is
the negative measure of industry heterogeneity, which is measured as the average monthly standard deviation of the
idiosyncratic component of returns within an industry in year y. %CRSP proxies for industry-level equity incentives and
is the average annual number of firms with CRSP data relative to the number of firms with Compustat data (see
Appendix B). VOLATILITY is the average implied volatility of at-the-money options for firms in the industry
following Van Buskirk (2011). PUBLIC proxies for the availability of public information and is the adjusted R2 values
from estimation of a standard market model that includes seven macro-economic factors described in Appendix A for
each firm i within an industry, estimated using monthly return and factor data over the period 1994-2008. LIT_RISK is
an industry-year measure of litigation risk (see Appendix A). HERF is the revenue-based herfindahl index for the 50
largest firms in the industry. Panel B presents the correlations between these variables by sample. Statistical significance
in the correlation matrices at 10%, 5%, and 1% are denoted by *, **, and ***, respectively.
Panel A: Average annual measures (477 industry-year observations in the FULL sample)
Industry size
Negative shock (TAILRISK)
Homogeneous (IND_WIDE)
Equity incentives (%CRSP)
Uncertainty (VOLATILITY)
Public information (PUBLIC)
Litigation risk (LIT_RISK)
Industry concentration (HERF)
Mean
Median
Std. Dev.
150
0.3774
-0.1610
1.0940
0.4474
0.1224
0.0460
0.1012
105
0.0000
-0.1552
0.9861
0.4465
0.1103
0.0428
0.0732
124.79
0.4852
0.0506
0.5444
0.0772
0.0360
0.0176
0.1757
Panel B: Correlation matrix (FULL sample)
TAILRISK
IND_WIDE
%CRSP
VOLATILITY
PUBLIC
LIT_RISK
HERF
Industry size
TAILRISK
0.007
-0.223***
0.093**
0.315***
0.237***
0.344***
-0.253***
-0.014
0.061
0.206***
-0.140***
0.137***
0.035
Industry size
TAILRISK
-0.077
-0.214***
0.093*
0.271***
0.336***
0.318***
-0.131***
0.039
0.053
0.122**
-0.123**
0.078
0.152***
(1SEG sample)
TAILRISK
IND_WIDE
%CRSP
VOLATILITY
PUBLIC
LIT_RISK
HERF
IND_
WIDE
%CRSP
0.215***
-0.587***
-0.068
-0.234***
-0.031
IND_
WIDE
-0.042
0.281***
-0.077
0.074
%CRSP
0.246***
-0.606***
-0.114**
-0.253***
-0.124**
46
-0.062
0.247***
-0.087*
0.107**
VOLATILITY
0.320***
0.391***
0.134***
VOLATILITY
0.356***
0.407***
0.245***
PUBLIC
LIT_RISK
0.296***
0.200***
-0.042
PUBLIC
LIT_RISK
0.340***
0.191***
0.048
Table 7: Characteristics of industries that have opacity episodes
This table presents estimated marginal effects from a logit regression model of opacity episode occurrence on industry
and industry-year level correlates. The unit of observation is at the industry-year level. Panel A presents results for
opacity episodes determined by a significant industry-level increase in FOG in two-year rolling regressions (OPACITY).
Panel B presents results for two alternative measures of opacity episodes. Column 1 defines opacity episodes as the
subset of episodes from Panel A for which influential firms have a significant positive unexplained FOG (OPACITY
INFL). Column 2 defines withholding similarly to Panel A, but excludes firms with multiple business segments from the
measure of withholding (OPACITY1SEG) and from the regression. See Table 6 for definitions of the independent
variables. Statistical significance (one-sided if predicted, two-sided otherwise) at the 10%, 5% and 1% level is denoted by
*, **, and ***, respectively.
Panel A: Opacity episodes identified by significant increase in industry-average FOG for the full sample
• 17 opacity episodes; 120 non-opacity industry-year observations
Intercept
Negative shock (TAILRISK)
Homogeneous (IND_WIDE)
TAILRISK * IND_WIDE
Equity incentives (%CRSP)
Uncertainty (VOLATILITY)
Public information (PUBLIC)
Litigation risk (LIT_RISK)
Industry concentration (HERF)
Predicted
Sign
+
?
+
+
+
?
?
OPACITY
Coefficient
z-statistic
-0.5116***
-2.81
0.2791*
1.61
-1.6853**
-1.97
1.6756**
1.81
0.0942***
2.39
-0.3466
-0.74
-0.7592
-0.96
3.2978**
2.16
0.3419
1.25
Pseudo R2
Wald χ2 test statistic
Wald χ2 p-value
22.73%
13.49
0.10
Panel B: Alternative measures of opacity episodes
• OPACITYINFL: 12 opacity episodes; 125 non-withholding industry-year observations
• OPACITY1SEG: 14 opacity episodes; 112 non-withholding industry-year observations
Intercept
Negative shock (TAILRISK)
Homogeneous (IND_WIDE)
TAILRISK * IND_WIDE
Equity incentives (%CRSP)
Uncertainty (VOLATILITY)
Public information (PUBLIC)
Litigation risk (LIT_RISK)
Industry concentration (HERF)
Pseudo R2
Wald χ2 test statistic
Wald χ2 p-value
Predicted
Sign
+
?
+
+
+
?
?
(1) OPACITYINFL
Coefficient
z-statistic
-0.3397**
-2.33
0.1648*
1.42
-0.5304
-0.88
0.9414*
1.50
0.0677***
2.32
0.1232
0.33
-1.0298**
-1.63
2.0731*
1.91
0.0611
0.26
22.29%
10.77
0.22
47
(2) OPACITY1SEG
Coefficient
z-statistic
-0.3772**
-2.40
-0.0467
-0.30
-1.0126
-1.51
0.0214
0.03
0.0450
1.23
-0.2004
-0.49
-0.2907
-0.42
1.7524
1.46
0.6057*
1.84
23.31%
12.01
0.15
Table 8: Falsification test: Characteristics of industries that have transparency episodes
This table presents estimated marginal effects from a logit regression model of transparency episodes (TRANSP) on
industry and industry-year level correlates. The unit of observation is at the industry-year level. Column (1) reports
results from Table 7 for opacity episodes for convenient comparison. The transparency (opacity) episodes are
determined by a significant industry-level decrease (increase) in FOG in two-year rolling regressions using the FULL
sample. See Table 6 for definitions of the independent variables. Statistical significance (one-sided if predicted for
opacity episodes, two-sided otherwise) at the 10%, 5% and 1% level is denoted by *, **, and ***, respectively.
Intercept
Negative shock (TAILRISK)
Homogeneous (IND_WIDE)
TAILRISK * IND_WIDE
Equity incentives (%CRSP)
Uncertainty (VOLATILITY)
Public information (PUBLIC)
Litigation risk (LIT_RISK)
Industry concentration (HERF)
Pseudo R2
Wald χ2 test statistic
Wald χ2 p-value
Predicted
Sign
+
?
+
+
+
?
?
(1) OPACITY
Coefficient
z-statistic
-0.5116***
-2.81
0.2791*
1.61
-1.6853**
-1.97
1.6756**
1.81
0.0942***
2.39
-0.3466
-0.74
-0.7592
-0.96
3.2978**
2.16
0.3419
1.25
22.73%
13.49
0.10
48
(2) TRANSPARENCY
Coefficient
z-statistic
-0.1565
-1.21
0.0532
0.48
-0.3638
-0.87
0.2717
0.49
-0.0388
-0.66
0.1844
0.67
-0.3236
-0.63
-0.3392
-0.29
-0.1835
-0.72
6.63%
6.57
0.58
Table 9: Supplemental analysis of %CRSP as a determinant of opacity episodes
This table presents estimated marginal effects from logit regression models of opacity episode occurrence on industry
and industry-year level correlates allowing for a separate relation between firms with low and high equity incentives as
measured by %CRSP. The unit of observation is at the industry-year level. Panels A and B present results for opacity
episodes determined by a significant industry-level increase in FOG in two-year rolling regressions (OPACITY) and by a
significant industry-level increase in FOG combined with positive abnormal FOG for influential firms (OPACITYINFL),
respectively. Column (1) in each panel reports results for the first model from Table 7 with an additional interaction
term of %CRSP and LOW, which is an indicator variable for industry-year observations that have a %CRSP less than or
equal to the median industry-year level. Columns (2) and (3) in each panel report results for the subsample of
observations with low equity incentives (LOW = 1) and high equity incentives (LOW = 0), respectively. Statistical
significance (one-sided if predicted, two-sided otherwise) at the 10%, 5% and 1% level is denoted by *, **, and ***,
respectively.
Panel A: OPACITY used to identify opacity episodes 22
Predicted
(1) Interaction
Sign
Coefficient z-stat
Intercept
-0.4854***
-2.61
Negative shock (TAILRISK)
+
0.2789**
1.64
Homogeneous (IND_WIDE)
?
-1.5763*
-1.83
TAILRISK * IND_WIDE
+
1.6458**
1.81
Equity incentives (%CRSP)
+
0.0811**
1.78
%CRSP * LOW
-0.0349
-0.55
Uncertainty (VOLATILITY)
+
-0.2615
-0.54
Public information (PUBLIC)
-0.7962
-1.02
Litigation risk (LIT_RISK)
?
3.1663**
2.07
Industry concentration (HERF)
?
0.3031
1.09
Pseudo R2
Wald χ2 test statistic
Wald χ2 p-value
23.01%
13.83
0.13
Panel B: OPACITYINFL used to identify opacity episodes 23
Predicted
(1) Interaction
Sign
Coefficient z-stat
Intercept
-0.3082**
-2.09
Negative shock (TAILRISK)
+
0.1733*
1.53
Homogeneous (IND_WIDE)
?
-0.4760
-0.81
TAILRISK * IND_WIDE
+
0.9530*
1.58
Equity incentives (%CRSP)
+
0.0524*
1.60
%CRSP * LOW
-0.0370
-0.85
Uncertainty (VOLATILITY)
+
0.1589
0.44
Public information (PUBLIC)
-1.0153*
-1.64
Litigation risk (LIT_RISK)
?
2.0177*
1.87
Industry concentration (HERF)
?
0.0407
0.18
Pseudo R2
Wald χ2 test statistic
Wald χ2 p-value
23.16%
10.63
0.30
(2) LOW %CRSP
Coefficient
z-stat
-0.4366**
-1.99
0.3218*
1.54
-0.7063
-0.73
1.7930*
1.51
0.2228
1.12
(3) HIGH %CRSP
Coefficient
z-stat
-0.5554*
-1.64
0.3760*
1.28
-3.5577**
-2.09
2.3492*
1.52
0.1387**
1.91
-0.4435
0.4584
2.0593
0.4927
-0.9340
-2.1740*
3.9447
0.8151
-0.76
0.42
1.42
1.16
27.63%
5.51
0.70
-1.19
-1.62
1.47
1.60
27.88%
8.80
0.36
(2) LOW %CRSP
Coefficient
z-stat
-0.0556
-0.70
0.1809
1.10
0.1085
0.47
1.2249
1.15
0.0382
0.52
(3) HIGH %CRSP
Coefficient
z-stat
-0.4990
-1.48
0.2089
1.14
-1.1504
-1.10
1.0700
1.12
0.0658*
1.32
-0.0093
-0.3075
0.8240
0.0945
0.3166
-0.5962
0.9252
-0.0801
29.38%
1.63
0.99
-0.06
-0.81
0.91
0.53
0.71
-0.91
0.63
-0.29
42.64%
2.92
0.94
22 Using the OPACITY measure includes 17 collusion episodes and 120 non-collusion industry-year observations. The
LOW (HIGH) %CRSP sample has 7 (10) collusion episodes; 74 (46) non-collusion industry-year observations.
23 Using the OPACITY
INFL includes 12 collusion episodes and 125 non-collusion industry-year observations. The LOW
(HIGH) %CRSP sample has 5 (7) collusion episodes; 76 (49) non-collusion industry-year observations.
49
Appendix A: Variable definitions
This appendix provides detailed definitions of variables that are included in the analysis.
Control variables used in the FOG model
MVE
= Log of market value of equity (CSHO * PRCC_F), winsorized at 1%.
MTB
= Market to book ratio (MVE + LT / AT), winsorized at 1%.
Special items
= Special items scaled by total assets (SPI/AT), winsorized at 1%.
Return volatility = Standard deviation of monthly stock returns for the 12 months ending in the third month of the
current year (i.e., from current month three back to lag nine), winsorized at 1%. This variable is set to
missing if there are less than 11 months of return data.
Non-missing items = Number of non-missing items in Compustat, winsorized at 1%, to proxy for complexity. The total
number of items possible is based on all data items for the balance sheet, income statement, and cash
flow statement per the WRDS Compustat web interface.
Firm age
= Number of years a firm has been listed on CRSP.
Delaware
= An indicator = 1 if the firm was incorporated in Delaware (INCORP= “DE”), 0 otherwise.
GEO Segments = Log of the number of geographic segments plus one, obtained from the Compustat Segment file (but
collapsed into FF49 industries). The variable is set to one if missing in the datafile.
BUS Segments = Log of the number of business segments plus one, obtained from the Compustat Segment file (but
collapsed based on FF49 industries). The variable is set to one if missing in the datafile.
Explanatory variables used in the logit model of the opacity episodes
TAILRISK: Industry tail risk
Using option data from OptionMetrics, we calculate the Van Buskirk (2011) measure of expected volatility skew before
each I/B/E/S earnings announcement that meets his data requirements. This measure is calculated as the average
implied volatility for out-of-the-money puts less the average implied volatility for at-the-money calls. We compute skew
for 85,293 firm-quarters between 1995 and 2010. We calculate the firm-year average skew so that firms with more
populated data do not influence the measurement. The average (and median) firm-year have volatility skew of
approximately 0.03 (higher values indicate more negative skewness). On average, the industries have 32.8 firms per year
(median = 20.2). The minimum (maximum) is 1.7 (131.2) for industry 1 (industry 36). The inter-quartile range is 5.9 to
50.9. For each industry, we obtained the median and maximum skew over the 13 years. Industries with a maximum
greater than the median industry maximum (0.061) and a median greater than the global industry median of 0.03 are
designated as having high positive skew or high negative tailrisk (TAILRISK = 1).
INDUSTRY_WIDE: Intra-industry homogeneity
We estimate a single factor market model using monthly returns for each FF49 industry from January 1994-December
2008. We then calculate the standard deviation of residuals for each industry-month. Industry-year heterogeneity
measure equals the average of the monthly standard deviations across all months in each calendar year for each industry.
Industry-year homogeneity (INDUSTRY_WIDE) is equal to the negative of the heterogeneity industry-year measure.
%CRSP: Stock price incentives to collude
For each FF49 industry-year, we count the firms with greater than 200 daily return observations on CRSP and divide by
the number of firms in the Compustat fundamentals annual file with an SIC code, sales, and total assets data.
VOLATILITY: Industry uncertainty
Using option data from OptionMetrics, we calculate the Van Buskirk (2011) measure of implied volatility for at-themoney options before each I/B/E/S earnings announcement that meets the data requirements. We calculate the firm
average implied volatility by year to convert this measure to firm-year observations so that firms with more populated data
do not influence the measurement.
PUBLIC: Availability of public information
For each FF49 industry, we estimate a factor model (A1) using monthly data from January 1994 to December 2008:
where
rim
𝑧
+ 𝜀𝑖𝑚
𝑟𝑖𝑚 = 𝛼𝑗 + 𝛽𝑗𝑚𝑘𝑡 𝑟𝑚𝑘𝑡,𝑚 + 𝛽𝑗𝑖𝑛𝑑 𝑟𝑖𝑛𝑑,𝑚 + ∑7𝑧=1 𝛿𝑗𝑧 𝐹𝐴𝐶𝑇𝑂𝑅𝑚
(A1)
is the monthly return for firm i in industry j (j = 1 to 49); 𝑟𝑚𝑘𝑡,𝑚 is the monthly return on the CRSP equally-
𝑧
weighted market index; 𝑟𝑖𝑛𝑑,𝑚 is the monthly return on the equally-weighted industry index; and 𝐹𝐴𝐶𝑇𝑂𝑅𝑚
is the
50
monthly value of one of seven macro-economic risk factors identified in prior research and described below. PUBLIC is
the adjusted R2 value of the factor model. A greater adjusted R2 for an industry suggests that more information about the
industry is available through public signals.
The seven factors in Equation (A1) are:
(1) Short-term interest rates = the 3-month treasury bill rate from CRSP.
(2) Default
premium
=
Moody's
Seasoned
Baa
Corporate
Bond
Yield
(available
from
http://www.federalreserve.gov/releases/h15/data.htm) minus the 10-year constant maturity government bond yield
(from http://research.stlouisfed.org/fred2/).
(3) Term premium = the yield on 10-year constant maturity government bonds minus the yield on one-year constant
maturity government bonds (from http://research.stlouisfed.org/fred2/).
(4) Foreign Exchange Rates = weighted average of the foreign exchange value of the U.S. dollar against a subset of the
broad index currencies that circulate widely outside the country of issue (from http://research.stlouisfed.org/fred2/).
(5) Producer price index = the total finished goods producer price index from the Bureau of Labor Statistics.
(6) Small minus Big (SMB) = the Fama-French monthly benchmark factor for the performance of small stocks relative to
big stocks.
(7) High – low book-to-market (HML) = the Fama-French monthly benchmark factor for the performance of value
stocks relative to growth stocks.
SMB and HML are from Kenneth French’s website:
(http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html).
LIT_RISK: Litigation risk
Our proxy for litigation risk is based on Model 3 from Table 7 of Kim and Skinner (KS, 2012). This model is intended to
capture the factors that make a firm more vulnerable to securities litigation prior to the revelation of “triggering events”
like major price declines. In this sense, the model provides an ex ante probability of litigation risk. Specifically, we
estimate the following model:
𝑆𝑈𝐸𝐷 = 𝑏0 + 𝑏1 (𝐹𝑃𝑆𝑡 ) + 𝑏2 (𝐿𝑁𝐴𝑆𝑆𝐸𝑇𝑆𝑡−1 ) + 𝑏3 (𝑆𝐴𝐿𝐸𝑆_𝐺𝑅𝑂𝑊𝑇𝐻𝑡−1 ) + 𝑏4 (𝑅𝐸𝑇𝑈𝑅𝑁𝑡−1 )
+ 𝑏5 (𝑅𝐸𝑇𝑈𝑅𝑁_𝑆𝐾𝐸𝑊𝑁𝐸𝑆𝑆𝑡−1 ) + 𝑏6 (𝑅𝐸𝑇𝑈𝑅𝑁_𝑆𝑇𝐷_𝐷𝐸𝑉𝑡−1 ) + 𝑏7 (𝑇𝑈𝑅𝑁𝑂𝑉𝐸𝑅𝑡−1 ) + 𝑒
We estimate LIT_RISK for fiscal years between 1996 and 2008 for a sample of 48,844 firm-years (including 2,667
lawsuits). The firm-year observations are averaged to create the industry-year observations. Our sample of sued firms is
generated from a database provided by Woodruff-Sawyer, a San Francisco-based insurance brokerage firm. As explained
in Rogers and Van Buskirk (2009), Woodruff-Sawyer aggregates data from a number of sources including Securities Class
Action Clearinghouse (SCAC), which is the source of the sued firm sample in KS. Estimating the model on this sample
allows us to relax some of KS’s sample requirements that are not necessary for our study (e.g., they require return data to
be available for three years prior to the beginning of each fiscal year). As expected, the sign and significance of each of
the independent variables is similar to that reported by KS (see KS for specific variable definitions).
HERF: Industry concentration
We compute a revenue-based herfindahl index for each FF49 industry for each year as:
𝑚
𝑚
𝑖=1
𝑖=1
2
𝐻𝐸𝑅𝐹𝑗𝑦 = �� �𝑆𝑎𝑙𝑒𝑠𝑖 �� 𝑆𝑎𝑙𝑒𝑠𝑖 � �
where Salesi is revenues for each firm i in industry j in year y and m equals 50 if there are at least 50 firms in the industry
and equals the number of firms in industry j in year y if the number is less than 50. We similarly compute a herfindahl
index based on market share of total assets.
We also compute industry-year level concentration ratios as the sum of the market shares of sales or total assets for the n
largest firms in an industry:
𝐶𝑛 = ∑𝑛𝑖=1(𝑀𝑎𝑟𝑘𝑒𝑡 𝑆ℎ𝑎𝑟𝑒)𝑖
We compute the concentration ratio for the top four, six, and eight firms. Concentration ratios are commonly used in the
cartel literature, but there is no consensus on the appropriate n to include.
51
Appendix B: Summary of explanatory variables across the Fama-French 49 industries
An asterisk after the industry name indicates that the industry is designated as having high tail risk (TAILRISK=1). The
remaining columns report the average annual number of firms in each FF49 industry over the period 1994-2008 with
available data on Compustat and CRSP, the average annual percent of firms on CRSP relative to Compustat (%CRSP), the
PUBLIC score, and the average industry-year HERF.
Average annual # of firms
Industry
Compustat
CRSP
%CRSP
PUBLIC
HERF
1 Agric
Agriculture
18.4
16.7
92.0%
0.128
0.232
2 Food
Food Products
89.5
81.8
92.9%
0.064
0.065
3 Soda
Candy & Soda
11.1
20.1
191.9%
0.135
0.306
4 Beer
Beer & Liquor
12.5
23.7
198.1%
0.096
0.329
5 Smoke Tobacco Products
5.9
8.2
143.3%
0.203
0.822
6 Toys
Recreation
*
53.3
52.7
106.2%
0.103
0.156
7 Fun
Entertainment
*
102.3
87.5
85.2%
0.090
0.143
8 Books
Printing and Publishing
45.9
58.5
129.2%
0.102
0.064
9 Hshld
Consumer Goods
*
111.5
95.7
85.7%
0.089
0.127
10 Clths
Apparel
*
79.1
62.4
80.0%
0.107
0.070
11 Hlth
Healthcare
112.3
119.5
105.6%
0.080
0.089
12 Medeq Medical Equipment
186.9
187.4
104.1%
0.103
0.074
13 Drugs
Pharmaceutical Products
*
316.7
324.1
104.1%
0.160
0.092
14 Chems Chemicals
92.0
103.3
113.4%
0.103
0.059
15 Rubbr
Rubber and Plastic Products
57.8
43.8
73.3%
0.101
0.070
16 Txtls
Textiles
32.8
29.3
91.6%
0.122
0.155
17 Bldmt
Construction Materials
103.5
88.3
84.2%
0.087
0.071
18 Constr Construction
*
69.8
70.9
103.8%
0.100
0.061
19 Steel
Steel Works Etc.
75.6
82.2
112.4%
0.185
0.068
20 Fabpr
Fabricated Products
22.5
19.7
103.4%
0.131
0.141
21 Mach
Machinery
183.8
177.3
98.2%
0.126
0.055
22 Elceq
Electrical Equipment
*
79.7
135.8
166.7%
0.133
0.230
23 Autos
Automobiles and Trucks
*
79.9
90.1
113.4%
0.166
0.220
24 Aero
Aircraft
22.2
26.3
119.2%
0.139
0.256
25 Ships
Shipbuilding, Railroad Equip.
10.7
8.5
81.8%
0.245
0.425
26 Guns
Defense
*
9.8
9.5
96.4%
0.227
0.747
27 Gold
Precious Metals
21.3
64.5
308.7%
0.246
0.501
28 Mines
Non-Metallic/Industrial Metal Mining
18.2
29.1
163.5%
0.184
0.192
29 Coal
Coal
*
7.1
12.2
174.5%
0.361
0.251
30 Oil
Petroleum and Natural Gas
198.9
242.7
124.6%
0.194
0.121
31 Util
Utilities
223.8
170.5
78.6%
0.133
0.026
32 Telcm
Communication
*
193.0
238.4
133.6%
0.161
0.067
33 Persv
Personal Services
*
68.8
68.1
100.6%
0.086
0.066
34 Bussv
Business Services
334.3
387.0
122.9%
0.109
0.037
35 Hardw Computers
163.1
137.5
87.3%
0.171
0.136
36 Softw
Computer Software
524.1
446.7
87.8%
0.186
0.150
37 Chips
Electronic Equipment
337.7
350.0
106.5%
0.213
0.066
38 Labeq
Measuring & Control Equip.
123.9
122.4
100.2%
0.124
0.093
39 Paper
Business Supplies
65.7
67.1
106.9%
0.113
0.074
40 Boxes
Shipping Containers
16.0
17.5
111.3%
0.148
0.140
41 Trans
Transportation
*
139.2
152.3
113.4%
0.110
0.045
42 Whlsl
Wholesale
234.3
250.7
109.6%
0.078
0.057
43 Rtail
Retail
*
312.9
287.7
93.8%
0.103
0.066
44 Meals
Restaurants, Hotels, Motels
*
115.2
131.3
116.7%
0.112
0.075
45 Banks
Banking
337.1
723.0
246.2%
0.122
0.056
46 Insur
Insurance
*
207.0
200.3
99.7%
0.108
0.045
47 Rlest
Real Estate
56.3
49.5
90.3%
0.103
0.146
48 Fin
Trading
*
315.5
1085.2
364.6%
0.138
0.110
49 Other
Almost Nothing
79.0
25.5
31.5%
0.097
0.261
52