disclosure spillover: evidence from going-private activity...wave of going-private activity in the...
TRANSCRIPT
Disclosure Spillover: Evidence from Going-Private Activity
Lisa Hinson
Fisher School of Accounting
University of Florida
Jeffery Piao
Fisher School of Accounting
University of Florida
July 2019
We thank Nick Cicone, Joost Impink, Mike Mayberry, Hyunjong Park, Jenny Tucker and Diana
Weng for their generous help and guidance. We give special thanks to Robert Bartlett, Professor
of Law at the University of California-Berkeley School of Law, for providing guidance on the
going-private selection process. Jeffery thanks the Linton E. Grinter Fellowship for providing
financial support.
Disclosure Spillover: Evidence from Going-Private Activity
ABSTRACT
In the U.S., there have been waves of public firms going private since the 1980s. Firms that go
private continue to operate yet are no longer subject to SEC financial reporting requirements.
While prior research focuses on the consequences of going-private transactions on the going-
private firms themselves or on their shareholders, little research investigates the externalities of
going-private transactions. In this paper, we examine peer firmsβ disclosure responses following
going-private events. We find that industry peers increase disclosure quality, which suggests that
the loss of information spillover from the now-private firms imposes a negative externality on
firms that remain public. We find that increases in disclosure quality subsequent to going-private
activity are lower for firms with high proprietary cost concerns and higher for firms that want to
attract analyst following or limited investor resources.
JEL Classification: D80; L22; M41
Keywords: going-private, information externality, competitive costs
Data availability: Data are publicly available from the sources identified in the paper.
2
1. Introduction
Going private is a transaction or series of transactions that converts a publicly traded firm
into a private firm. Since the 1980s, there have been waves of going-private activity in the U.S. A
wave of going-private activity in the 80s was fueled by leveraged buyouts and another wave in the
2000s was fueled by the private equity market (Bharath and Dittmar 2010). Increased going-
private activity in recent years is again attributable to the size and importance of the private equity
market. The magnitude of going-private transactions has led researchers to study the firms that go
private and their shareholders; however, it is also important to understand the externalities of going
private on other firms, which is the goal of our study.
When companies go private, they are no longer subject to the SEC reporting regime. While
public firms in the U.S. are subject to stringent disclosure regulations under the Securities
Exchange Act of 1934, Sarbanes-Oxley Act of 2002 (SOX) and the Dodd-Frank Act of 2010,
private firms in the U.S. face little disclosure regulation. Private firms are not required to provide
financial reports to the public and few voluntarily do so (Minnis and Shroff 2017). As such, going
private substantially shrinks the information environment around the now private firm.
We examine peer firm disclosure responses to going-private activity. Admati and
Pfleiderer (2000) show that when firm values correlate, information from one firm is used to
evaluate other firms. This information spillover is an externality of the disclosure process that
creates social value. Empirical work documents that information externalities provide benefits
such as improved investment decisions and lower cost of equity capital to industry peer firms
(Badertscher, Shroff, and White 2013; Baginski and Hinson 2016; Shroff, Verdi, and Yost 2017).
These benefits arise because a firmβs disclosure improves the industry-wide information
environment by reducing uncertainty about cost conditions and common supply and demand forces
3
(Albuquerque 2009). When a firm goes private, the lost information transfer signal imposes a
negative externality on peer firms, which likely induces a disclosure response.
In making disclosure response decisions, we expect peer firms to weigh the costs and
benefits of disclosure changes. Prior to going-private activity, a peer firmβs marginal benefit of
greater disclosure quality above and beyond the information transfer signal is not sufficient to
incur the costs of greater disclosure quality. However, going-private activity represents a shock to
the costs and benefits of peer firmsβ equilibrium disclosure practices. We hypothesize that after
going-private activity, the peer firmβs benefits from the information transfer signal decline such
that the marginal benefit of increased disclosure quality is now greater than the costs and the peer
firm increases disclosure quality. Thus, on average, we predict that the peer firm response will be
an increase in disclosure quality to replace the lost information spillover.
While we expect peer firms to replace the lost information transfer signal after going-
private activity, competitive costs provide significant tension in this setting. Rival firms can benefit
from each otherβs disclosures if the disclosures reveal actionable strategic information. After
going-private events, peer firms can no longer extract valuable strategic information about their
going-private competitors. Unlike other events such as acquisitions by other public firms or
bankruptcies, going-private transactions enable a firm to continue its operations while terminating
its reporting obligations.1 Remaining public firms continue to compete with the now-private firms,
but the exchange of information becomes one-directional. The now-private firms can gain access
1 Companies may also suspend their reporting obligations by deregistering their common stock from the SEC, an event
referred to as βgoing dark.β Leuz, Triantis and Wang (2008) suggest going dark and going private are distinct
transactions and have different economic consequences. The most notable difference between going private and going
dark is that in the latter case, the going dark firms continue to trade in the over-the-counter (OTC) market, pink sheets
or the bulletin board after the date of deregistration while the going-private firms no longer trade their securities
publicly (Leuz et al. 2008).
4
to public firmsβ disclosures, but not vice versa. Thus, competitive forces are a relevant
consideration in peersβ strategic disclosure reactions. 2 If the increase in peer competitive costs
after going-private activity removes or outweighs the marginal informational benefits from self-
disclosure, then firms will not increase or might even decrease disclosure quality so as to provide
less precise actionable information. On average, we predict a positive change in net benefits and
increased peer disclosure quality; however, we hypothesize that the disclosure quality increases
will be weaker for firms facing high competitive pressures.
Using a hand-collected sample of 482 going-private transactions from 2006 to 2015, we
test whether industry peer firms that remain public change their disclosure quality after going-
private activity. We measure disclosure quality using the disaggregation quality (DQ) measure
introduced by Chen, Miao and Shevlin (2015). The DQ measure considers the level of
disaggregation in firmsβ annual financial reports and captures the fineness or precision of
mandatory accounting information. We find that going-private intensity is associated with
increases in industry peer firmsβ disclosure quality. This result is consistent with the loss of
information spillover from the now-private firms imposing a negative externality on firms that
remain public.
We measure competitive cost concerns using product market fluidity from Hoberg, Phillips
and Prabhala (2014) and the Herfindahl-Hirschman index. While on average going-private activity
is associated with peer disclosure quality increases, the relation is weaker or even goes away for
firms with high competitive concerns. Thus, firms account for competitive costs in their disclosure
2 Although firms that go private continue operations, some may argue that competitive costs are immaterial if the
going-private firms are poor performers (Halpern, Kieschnick and Rotenberg 1999). However, struggling companies
are not always good going-private candidates because firms that consider going private but have a slim chance of
success are unlikely to procure the necessary financing to complete the transaction (Koenig 2004). Thus, going-private
firms represent credible competitive threats to their public peers.
5
decisions and for a subset of firms, the increases in informational benefits derived from enhanced
disclosure precision do not outweigh the increases in competitive costs.
We further examine two cross-sectional motivations for disclosure quality changes
subsequent to going-private activity. First, subsequent to reductions in analyst following, firms
increase disclosure to recoup analyst coverage (Anantharaman and Zhang 2011). Consistent with
the motivation to attract analyst following, we find that the relation between going-private activity
and peer firm disclosure quality increases is greater for firms that have recently experienced
substantial reductions in analyst following. Second, firms with high substitutability disclose high
quality information to compete for limited investor resources (Fishman and Hagerty 1989; Park,
Schrand and Zhou 2019). Consistent with the motivation to attract investors, we find that peer firm
disclosure quality increases after going-private activity are greater for firms with high
substitutability.
This study contributes to two streams of literature. First, the study extends the research on
going-private transactions. Prior studies focus on the going-private firms themselves or the
shareholders of the going-private firms (DeAngelo, DeAngelo and Rice 1984; Travlos and Cornett
1989; Halpern, Kieschnick and Rotenberg 1999; Bharath and Dittmar 2010). An exception is
Slovin, Sushka and Bendeck (1991) that documents positive valuation effects for industry peers of
firms that receive bids to go private. We extend the literature on the intra-industry effects of going-
private activity by examining disclosure externalities. Examining externalities from a disclosure
perspective is important because going private results in a substantial reduction in public
information. Our evidence suggests that the lost information imposes a negative externality on
peers that remain public. The negative externality prompts peers to incur costs to regain the lost
informational benefits. Understanding the spillover effects of going-private transactions can aid
6
regulators in making policy decisions on measures aimed at making public company status
attractive or alternatively regulation that could place additional burdens on public companies. We
provide evidence to decision-makers on the oft-unnoticed externalities of public companiesβ
going-private decisions from a disclosure perspective.
Second, this paper extends prior literature on disclosure externalities and information
spillovers from a unique angle. Theoretical work by Admati and Pfleiderer (2000) suggests
information disclosure generates positive externalities in a multi-firm setting when firm values are
correlated. Recent empirical work shows that corporate disclosures have spillover effects on peer
firmsβ information environments, voluntary disclosure practices and investment decisions
(Badertscher et al. 2013; Baginski and Hinson 2016; Shroff, Verdi, and Yost 2017; Breuer,
Hombach, and MΓΌller 2018). We provide evidence of spillover effects on peersβ mandatory
disclosure practices, which suggests that free-riding not only affects voluntary disclosure quality
but also mandatory disclosure quality. That is, firms could potentially provide more disaggregated
or detailed financial statements to the public but they chose not to do so in the presence of greater
competitor disclosure as information spillovers make self-disclosure and competitor disclosure
substitutes. When information spillovers weaken, or disappear as rivals leave the SEC reporting
regime, companies who remain public bridge the information gap by providing more detailed
financial statements, increasing the amount and precision of mandatory disclosures.
2. Institutional Background on Going-Private Transactions
In a general business sense, βgoing privateβ occurs when a small group of investors,
including managers, acquires all publicly owned shares (DeAngelo, DeAngelo and Rice 1984).
However, for the purpose of U.S. federal securities laws, βgoing privateβ has a much narrower
meaning (Morrison & Foerster 2012). The SEC defines going private as circumstances in which
7
βa public company is eligible to deregister a class of its equity securities, either because those
securities are no longer widely held or because they are delisted from an exchangeβ (SEC 2011).
Going private can be accomplished through a restructuring that concentrates managersβ and private
equity investorsβ ownership or an acquisition of a public firm by a private firm (Leuz et al. 2008;
Bartlett 2009).
Prior accounting studies mainly focus on going-private deals through Rule 13e-3 of the
Securities Exchange Act of 1934 (Leuz et al. 2008; Engel, Hayes and Wang 2007; Bianchi,
Minutti-Meza, Phillips and Vulcheva 2018). Rule 13e-3 applies to situations in which a company
or an affiliate of the company engages in a tender offer, merger, or reverse stock split to qualify
the firmβs equity securities for deregistration.3 A public company may deregister when there are
less than 300 shareholders of record (or 500 shareholders of record for companies with
insignificant assets) and will no longer be required to file periodic reports with the SEC. When a
firm goes private through a Rule 13e-3 transaction, the firm is said to go private under SEC rules
(SEC 2011).4
In addition to transactions subject to Rule 13e-3 disclosure requirements, public firms may
go private through other buyout transactions or takeover mechanisms in which neither substantial
stockholders nor managers are aligned with the acquiring group (Koenig 2004). Certain
transactions are also explicitly exempted from Rule 13e-3 such as clean-up transactions following
a tender offer by an unaffiliated third party. Going-private companies need to file a Schedule 13E
3 A tender offer that takes a firm private is a public, open offer by another company or individual to purchase all or
most of the companyβs public shares. A company could also go private by merging or selling all or most of its assets
to a privately held firm. In addition, a company could reduce the number of shareholders through a reverse stock split.
In that case, existing shareholders exchange a block of 10, 100 or even 1,000 old shares for a single new share. If the
old shareholder does not have a full block of shares, the company pays cash to redeem the old shares instead of giving
a new share. This removes some smaller shareholders and reduces the number of total shareholders.
4 For detailed information on Rule 13e-3 transactions see https://www.sec.gov/fast-answers/answersgoprivhtm.html.
8
-3 only if the acquiring firm is an affiliate of the public target.5 In other words, there are going-
private transactions that are not subject to Rule 13e-3. Therefore, solely relying on Schedule 13E-
3 to identify going-private transactions might omit firms that effectively go private through
transactions not accompanied by Rule 13e-3 disclosure.
We define going private broadly as transactions by a controlling individual or a group of
individuals that reduce the number of shareholders below 300, thereby terminating the companyβs
public firm status and periodic reporting obligations. 6 This definition includes Rule 13e-3
transactions as well as other public-to-private buyout transactions and thus encompasses a
comprehensive set of going-private transactions. When public firms finalize their public-to-private
deals, they delist their securities from stock exchanges and deregister their publicly traded shares
from the SEC. In practice, going-private firms file Form 15 to suspend their duty to file financial
reports under Section 13 and 15(d) of the Exchange Act following the completion of the going-
private transactions.
Historically, there have been several going-private waves in the U.S. In the 1980s, an
unprecedented number of public companies went private through leveraged buyouts (Bharath and
Dittmar 2010). Another wave of public-to-private transactions emerged in 1997. The years from
1997 to 2002 saw a steep rise in U.S. going-private activity with a total transaction value of $65
billion (Renneboog and Simons 2005). With the growth of the private equity market, public-to-
private transactions reappeared in the mid-2000s when the U.S. experienced a second leveraged
5 Rule 13e-3(a)-(1) defines an βaffiliateβ as βa person that directly or indirectly through one or more intermediaries
controls, is controlled by, or is under common control with such issuer.β
6 Section 15(d) (1) of the Securities Exchange Act of 1934 states. ββ¦The duty to file under this subsection shall also
be automatically suspended as to any fiscal year, other than the fiscal year within which such registration statement
became effective, if, at the beginning of such fiscal year, the securities of each class, other than any class of asset-
backed securities, to which the registration statement relates are held of record by less than 300 persons.β
9
buyout boom. Private equity firms committed a tremendous amount of capital and raised
approximately $225 billion in 2006 (Bharath and Dittmar 2010; Kaplan and StrΓΆmberg 2009). The
financial crisis of 2008 and 2009 resulted in a decline in going-private transactions sponsored by
private equity. Recently, however, private equity sponsor interest and activity has increased. In
2016, there were 47 public-to-private transactions compared to 39 in 2015 and 37 in 2014.7
Reasons companies go private include not experiencing the benefits of access to capital,
analyst coverage and trading volume, wishing to avoid regulatory and information production
expenses, wanting to focus on long-term strategies and value, not participating in the market for
corporate control by using stock to make acquisitions, and wanting to decrease agency concerns
(Koenig 2004; Bharath and Dittmar 2010). Specifically, relative to firms that remain public, firms
that go private are larger but they have lower analyst following, less institutional ownership, lower
liquidity, lower market-to-book and fewer mergers (Bharath and Dittmar 2010). Firms that go
private may also face greater agency problems as they tend to have higher amounts of free cash
flow, but that is only the case before the 1990s. Interestingly, many of the characteristics of firms
that go private are inherent and are thus present long before the going-private decision. In Bharath
and Dittmarβs sample, firms go private 13.5 years after their IPO, on average. At the time of the
IPO, firms that eventually go private are larger, have lower analyst following and liquidity, and
have higher free cash flow pre-1990s relative to firms that remain public (Bharath and Dittmar
2010). Therefore, some determinants of going private develop over time, but many are stable and
are not due to a change or shock.8
7 βTake Private Trendβ: https://www.goodwinlaw.com/publications/2017/05/05_08_2017-take-private-deal-activity.
Accessed on August 29, 2018. We report slightly different numbers of going-private transactions than the law firm
Goodwin Procter because we exclude going-private transactions of foreign private issuers. 8 It is unlikely that these firm-specific determinants of going-private, rather than the going-private activity itself, drive
the changes in peer firm disclosure quality. First, many of the determinants are inherent characteristics of going-
10
Private firms in the U.S. face virtually no financial reporting regulation. They are not
required to provide audited financial statements to regulators or the public. Furthermore, in the
absence of regulation, few private firms voluntarily produce financial reports and provide audited
financial disclosures to the public (Allee and Yohn 2009; Minnis and Shroff 2017). This reduction
in public disclosure resulting from going-private transactions means that the information
environment around the now private firm shrinks substantially. Disclosures that might have
previously provided information transfer signals to analysts and investors or proprietary
information to competitors no longer exist, thus altering the information set in the going-private
firmsβ industries.
3. Hypothesis Development
3.1 GOING-PRIVATE ACTIVITY AND PEER FIRM DISCLOSURE RESPONSE
Theory shows that when firm values or cash flows are correlated, information from one
firm is used to evaluate other firms (Dye 1990; Admati and Pfleiderer 2000). This information
spillover is an externality of the disclosure process that creates social value. However, the
individual firm does not account for the social value of information, so the optimal level of
disclosure for a single firm is lower than the socially optimal level of disclosure for multiple firms
(Admati and Pfleiderer 2000). This outcome suggests a free-rider problem in which costly
disclosures of other firms are used to value the free-rider. The free-rider obtains information
benefits through the spillover while avoiding costs of information production and dissemination
as well as competitive costs.
private firms and do not represent changes that would prompt disclosure changes. Second, they are characteristics of
the going-private firms and not the peer firms. Third, we nevertheless include many controls for peer firm
characteristics in empirical tests to mitigate concerns.
11
Empirical work provides evidence on information externalities benefiting firms with weak
information environments. Badertscher et al. (2013) document a spillover effect of public firmsβ
disclosures on private firmsβ investment decisions. Specifically, they find private firms operating
in industries with greater public firm presence benefit from a better industry-wide information
environment and make more efficient investment decisions. Shroff et al. (2017) show that a high-
quality peer information environment reduces a firmβs cost of capital when firm information is
scarce (i.e., for firms that have recently raised public capital for the first time). That relation
weakens or goes away as the amount of firm information increases and reduces the benefits of peer
information spillover.
Prior empirical research also documents externalities in the voluntary disclosure setting.
Baginski and Hinson (2016) provide evidence of voluntary disclosure free-riders. Following the
cessation of quarterly management earnings forecasts by stoppers, previously non-forecasting peer
firms who can no longer free ride on the stoppersβ disclosures replace the lost information transfer
signal by initiating quarterly management earnings forecasts. Breuer et al. (2018) use a size-based
disclosure regulation in Germany and document that unregulated firms reduce their voluntary
disclosures in the presence of regulated firmsβ mandatory disclosures. The effect is stronger when
information spillover is greater.
In making disclosure decisions, a firm weighs the costs and benefits. Costs of disclosure
arise from preparation, dissemination, attestation, loss of competitive advantages, and loss of
bargaining power. These costs are incurred when a firm self-discloses and avoided when a firm
free-rides. The primary benefit of high-quality disclosure is a reduction in information asymmetry
12
and risk and thus a reduction in the cost of capital.9 This benefit can be obtained from self-
disclosing and free-riding (Admati and Pfleiderer 2000; Baginski and Hinson 2016; Shroff et al.
2017). Prior to going-private activity, the peer firmβs marginal benefit of increased disclosure
quality above and beyond the information transfer signal is not sufficient to incur the costs of
increased disclosure quality.
A firm that goes private is no longer required to provide financial reports and few private
firms voluntarily do so (Allee and Yohn 2009; Minnis and Shroff 2017). If the disclosures of the
now private firm previously provided social value in the form of information spillovers, then
going-private activity will negatively impact peer firmsβ information environments.10 Therefore,
we hypothesize that after going-private activity, the peer firmβs benefits from the information
transfer signal decline such that the marginal benefit of increased disclosure quality is now greater
than the costs and the peer firm increases disclosure quality.
H1: Going-private activity is associated with increases in peer firm disclosure quality.
Although we predict that, on average, the net informational benefits will exceed the costs
of increased disclosure quality, proprietary costs are a significant source of tension, particularly in
our setting. Proprietary costs consist of the competitive costs of revealing information that
intensifies competition from industry rivals and the costs of losing bargaining power in the supply
chain (Berger et al. 2019). The proprietary cost literature implies that, in making decisions to
9 While some disclosures may be motivated by opportunism and not informational benefits, we do not consider those
disclosures to result in increased quality. Further, our measure of disclosure quality is disaggregation of financial
statements, which is not a tool that has been documented to achieve opportunistic motivations such as hyping the stock,
creating a positive earnings surprise or increasing or decreasing stock price prior to insider trades and option grants.
10 In any given industry and year, some firms may go from public to private and others may go from private to public.
Given the substantial uncertainty around IPOs, we do not expect reliable information spillovers from disclosures of
newly public firms. Over time, the uncertainty surrounding these firms likely lessens. However, in contrast to going-
private activity, IPO activity does not provide a shock to the industry information environment.
13
disclose valuable private information, firms are concerned about reductions in future cash flows
arising from damage to competitive positions (Healy and Palepu 2001; Dye 2017). Verrecchia
(1983) suggests that managers may be motivated to withhold information due to the existence of
proprietary costs even when the information is favorable in nature.
Empirical evidence supports effects of proprietary costs on disclosure. Li (2010) finds that
competition from potential entrants increases disclosure quantity while competition from existing
rivals decreases disclosure quantity. Huang, Jennings and Yu (2017) use large reductions in U.S.
import tariff rates as an exogenous increase in competition and find subsequent reductions in
domestic firmsβ voluntary disclosures. Cao, Ma, Tucker, and Wan (2018) document a negative
relation between technological competition and product disclosure, a type of disclosure that reveals
strategies and provides actionable information to competitors. Evidence that firms facing greater
competitive pressures disclose less suggests that firms weigh the costs of revealing proprietary
information in making disclosure decisions.
We expect proprietary cost concerns to be particularly strong in our setting. Prior to going-
private activity, peer firms can access the going-private firmsβ financial disclosures, thus both
peers and going-private firms have some information on competitorsβ operational results and
strategic moves. After firms go private and leave the SEC reporting regime, the remaining public
firms can no longer see the performance of the going-private firms via mandatory or voluntary
disclosures. This shock to the supply of competitorsβ information likely increases peer firmsβ
sensitivity to proprietary costs.
H2: The association between going-private activity and peer firm disclosure quality increases is
weaker for firms facing high competition.
14
3.2 DISCLOSURE RESPONSE TO ATTRACT ANALYST COVERAGE
Analyst coverage provides benefits to firms such as greater liquidity, lower trading costs,
lower cost of capital, and higher investor cognizance and media coverage which can increase
trading volume and market value (Merton 1987; Frankel and Li 2004; Bonner, Hugon and Walther
2007). The benefits incentivize managers to take actions to attract and retain analyst following
such as engaging in investor relations activities and providing additional disclosures
(Anantharaman and Zhang 2011; Bushee and Miller 2012). Specifically, Anantharaman and Zhang
(2011) find that firms that experience reductions in analyst coverage respond by increasing
voluntary disclosure. Increased voluntary disclosure attracts new analyst following, but only for
the firms that experienced recent declines in coverage. While increases in analyst coverage could
also prompt managers to increase voluntary disclosure so as to assist new analysts, Anantharaman
and Zhang (2011) do not find support for this conjecture. Thus, we expect reductions in analyst
coverage to influence disclosure decisions.
Reductions in analyst following could influence disclosure changes for two reasons. First,
managers might increase disclosure quality to recoup analyst coverage. Second, managers might
increase disclosure quality to replace the lost analyst information. While difficult to disentangle,
attracting analysts is likely to at least partially influence managersβ decisions. Replacing lost
information provides informational benefits such as greater liquidity and lower information
asymmetry. However, those benefits are stronger when firms have higher analyst coverage because
analysts aid in processing and interpreting firm information (Roulstone 2003). Thus, firms enhance
disclosure quality to directly replace the lost information and to recoup analysts to magnify the
informational benefits.
15
We hypothesize that the increases in firm disclosure quality subsequent to going-private
activity will be greater for firms that have experienced recent reductions in analyst coverage
because these firms are not only replacing lost information transfer but also replacing lost analyst
information and making an effort to regain analyst following.
H3: The association between going-private activity and peer firm disclosure quality increases is
stronger for firms with substantial reductions in analyst coverage.
3.3 DISCLOSURE RESPONSE TO ATTRACT INVESTORS
Another motivation to enhance disclosure quality is to attract limited investor resources
(Fishman and Hagerty 1989; Park et al. 2019). Fishman and Hagerty (1989) present a model in
which firms provide disclosure to not only decrease information asymmetry, but also to win
traders.11 Park et al. (2019) test the model and find empirical support for the investor-seeking
disclosure motivation. Specifically, when firms face greater competition for investor resources
because substitutability of assets with peer assets is greater (measured as return comovement),
firms issue more management forecasts. The increased disclosure improves liquidity and price
efficiency, but the marginal benefits diminish as the motive to attract investor resources increases.
This is consistent with disclosure for dual purposes: investor-seeking and traditional informational
benefits (Park et al. 2019).
Higher substitutability among peers could exacerbate the relation between going-private
activity and disclosure quality increases because of the motivation to attract investors. On the other
hand, higher substitutability could indicate greater opportunities to free-ride and thus weaken the
relation between going-private activity and disclosure quality increases. Finally, substitutability
11 The motivation to attract investor resources co-exists with the motivation to reduce information asymmetry, but is
separate and distinct.
16
could loosely proxy for the strength of the lost information transfer signal; we would expect a
stronger lost signal to result in stronger disclosure quality increases. While ultimately an empirical
question, we hypothesize that, for firms with high substitutability, the motivation to attract limited
investor resources away from competitors will result in exacerbated disclosure quality increases
subsequent to going-private activity.
H4: The association between going-private activity and peer firm disclosure quality increases is
stronger for firms with high substitutability.
4. Sample and Research Design
4.1 SAMPLE SELECTION
4.1.1 Going-private Transactions. Following Bartlett (2009), we construct a
comprehensive sample of going-private transactions using CRSP Historical Delisting data, SDC
Platinum and SEC filings.12 Our going-private sample selection process consists of three phases.
First, we obtain a comprehensive sample of delisting events from CRSP Historical Delisting.13
From that sample, we retain the delisting events that are likely to be going-private events using the
delisting codes provided.14 Second, we merge the subset of delisting events with the SDC Platinum
Mergers and Acquisitions dataset and keep the mergers and acquisitions initiated by private
acquirers. Third, for delisting firms that do not have matching SDC records, we hand check their
12 Our sample selection process is different from prior studies that rely solely on Schedule 13e-3 filings (Leuz et al.
2008; Engel et al., 2007; Bianchi et al., 2018). We construct a more comprehensive sample because relying solely on
Schedule 13E-3 tends to understate the number of going-private transactions (Bartlett 2009).
13 A firm typically delists its publicly traded shares from exchanges when the going-private transaction becomes
effective by filing a Form 25-NSE.
14 Specifically, we retain delisting events with the following delisting codes: 233 (when merged, shareholders receive
cash payments), 261(when merged, shareholders primarily receive cash and preferred stock, or warrants, or rights, or
debentures, or notes), 262 (when merged, shareholders primarily receive cash and other property), 570 (delisted by
current exchange-company request (no reason given)) and 573 (delisted by current exchange-company request,
deregistration (gone private)).
17
SEC filings to ensure the delistings are due to going-private transactions. We classify delisting
records accompanied by Schedule 13E-3 filings as going-private transactions. For delisting records
without Schedule 13E-3 filings, we read the 8-K filings that explain the delisting.15 If the delisting
is due to an acquisition and attached press releases or business press articles indicate that the
acquirer is a private-equity firm, we classify the delisting as a going-private event.
We exclude going-private companies that go public with a new initial public offering in
the subsequent two-year period. Lastly, we verify the going-private events by checking that
subsequent EDGAR filings are limited to Schedule 13E-3 or Form 15 filings (i.e., filings to revoke
securities registration). This final step ensures that going-private firms no longer provide financial
reports to the public after the effective dates of the going-private transactions. After the sample
collection and screening processes, we obtain 482 going-private transactions completed from 2006
to 2015 (see Table 1 Panel A). The going-private sample size is similar to that of Bartlett (2009).
4.1.2 Peer Firms. Firms belonging to the same industry are subject to similar economic
forces such as common supply and demand shocks and their cash flows correlate with each other.
Thus, we expect the loss of information after going-private transactions to impact industry peers.
We define industry peers as co-members within the same 4-digit SIC industry as the going-private
firms.16 We exclude non-12/31 firms because the majority of firms end their fiscal years on 12/31;
if the majority increases disclosure quality then firms with year-ends thereafter might have new
15 Typically, a Schedule 13E-3 filing is accompanied by a Form 15-12d or 15-12f filing. If a firm did not file a Schedule
13e-3, but did file a Form 15-12d or 15-12f, we treat the firm as we would other firms without Schedule 13E-3 filings
and check its 8-K filings to determine the reason for delisting.
16 We use SIC codes provided by COMPUSTAT instead of CRSP, mainly because there is a large discrepancy between
CRSP SIC codes and SIC codes on firmsβ EDGAR record pages. Guenther and Rosman (1994) suggest there are
considerable differences between SIC codes assigned to companies by COMPUSTAT and CRSP and COMPUSTAT
codes are superior for research purposes. Therefore, we extract SIC codes assigned to the going-private sample firms
assigned by COMPUSTAT in the most recent year before the firm went private.
18
information on which to free-ride and results would attenuate.17 The extent to which firms have
fiscal year-ends in the months just before December will bias against our results. We further
exclude firms in utilities and financial services industries as well as firms missing SIC codes or
control variables. Our peer firm sample consists of 14,327 firm-years (see Table 1 Panel B).
4.2 MODEL AND VARIABLE SPECIFICATIONS
To test peer disclosure responses to going-private activity (H1), we estimate OLS models
of disclosure quality for firm i in year t on going-private intensity in industry Ο in year t as follows:
π·ππ,π‘ = π½0 + π½1πΊππ,π‘ + π½2π·ππ,π‘β1 + πΏππ,π‘ + ππΌπππ’π π‘ππ¦ πΉπΈ + πππππ πΉπΈ + ππ (1)
where the dependent variable is disaggregation quality (DQ) and the independent variable of
interest is going-private activity (GP) described below. We include lagged DQ on the right-hand
side to measure firm iβs change in DQ from year t-1 to year t.18 We estimate the model with
industry and year fixed effects because disaggregation scores vary by industry and year (Chen et
al. 2015). Standard errors are clustered at the firm level.
In our tests, going-private activity is measured during the 12-month period ended on the
date of peer firm iβs year t fiscal year end. The disclosure quality measure uses annual report data.
A firm typically issues its annual report two to three months after its fiscal year end.19 Thus, a peer
17 During our sample period, 73.16% of all Compustat firm-year observations have a fiscal year ended on December
31st. Nevertheless, in a sample of 12/31 and non-12/31 firms, we continue to find that peer firms increase disclosure
quality subsequent to going-private activity (p<0.10). 18 We use lagged DQ instead of firm fixed effects because we want to compare disclosure quality in year t to disclosure
quality in year t-1 and not to the average disclosure quality for the firm across the entire sample period.
19 According to the Securities Act of 1933, Large Accelerated Filers are required to file their 10-Ks within 60 days
following their fiscal year ends; Accelerated Filers are required to file within 75 days; and non-Accelerated Filers are
required to file within 90 days. We use the SEC filings record dataset provided by Johannes Impink to calculate the
lag between our sample firmsβ fiscal year end and their 10-K filing date. We find 35.37% file within 60 days, 86.41%
file within 90 days and 97.90% file within 120 days. The mean lag between the fiscal year end and the 10-K filing
date for our sample firms is 73 days.
19
firm has time to assess the going-private activity and incorporate the assessment into its disclosure
decisions. Refer to Appendix B for a timeline.
4.3 DISCLOSURE QUALITY
Our dependent variable is disclosure quality measured using disaggregation quality (DQ)
from Chen et al. (2015). DQ captures the level of financial statement and footnote disaggregation
in companiesβ annual reports. In the United States, despite preparing financial reports in
accordance with Generally Accepted Accounting Principles (GAAP), managers have considerable
latitude in determining the level of disaggregation when presenting particular line items (Beck,
Glendening, and Hogan 2016). 20 This measure, therefore, reflects discretion in mandatory
disclosure. The underlying theoretical premise of DQ is that finer information is of higher quality
(Chen et al. 2015).
DQ captures the detail of annual reports through a count of non-missing items in Compustat,
which includes items in the financial statements and in the footnotes. 21 The following formula
measures balance sheet disaggregation:
π·π_π΅π = β {(#ππππππ π πππ πΌπ‘πππ
#πππ‘ππ πΌπ‘πππ )π Γ
$π΄π π ππ‘π π
$πππ‘ππ π΄π π ππ‘π }11
π=1 Γ· 2 (2)
where k represents group accounts. Chen et al. (2015) identify 11 balance sheet group accounts
and map 93 subaccounts into those group accounts. The proportion of non-missing subaccounts
within each group account is value-weighted based on the ratio of the balance of the group account
20 Regulation S-X regulates the form and content of and requirements for financial statements. The regulation provides
a general guideline on various line items and certain additional disclosures that should appear on firmsβ financial
statements. The regulation, however, gives managers considerable discretion in deciding on the level of disaggregation
of financial statements. The regulation states, βAdditional line items may be presented to facilitate the usefulness of
the financial statements.β 21 A firm can have a missing item because it (1) chooses not to report the item or (2) does not have the underlying
item. The Chen et al. (2015) measure includes screening mechanisms to mitigate (2).
20
to total asset value to account for differential materiality of the 11 group accounts.22 Because
balance sheets have two sides β assets and liabilities/equity β the measure is divided by 2, leading
to a score between 0 and 1. The following formula measures income statement disaggregation:
π·π_πΌπ = β {(#ππππππ π πππ πΌπ‘πππ
#πππ‘ππ πΌπ‘πππ )π Γ
1
7}7
π=1 (3)
where k again indexes group accounts. For the income statement, Chen et al. (2015) link 51
subaccounts to 7 group accounts. Because value-weighting is problematic for the income statement,
the proportions of non-missing subaccounts are equal-weighted (Chen et al. 2015). DQ_IS can
range from 0 to 1. The final DQ measure is a simple average of DQ_BS and DQ_IS.
Using DQ as the measure of disclosure quality has three key advantages in the context of
our study. First, Admati and Pfleiderer (2000) model companiesβ disclosures decisions using the
choice of precision for the disclosed signal. DQ captures the discretionary choice of the level of
detail in mandatory financial reports and thus, maps nicely to the choice of βprecision levelβ in the
theoretical model. In addition, one of the core assumptions in the Admati and Pfleiderer (2000)
model is that disclosure costs increase in the precision of the disclosed information. A higher level
of disaggregation is costly to the firm, as it not only requires high-quality accounting information
systems but also provides the firmβs rivals with more proprietary information. Second, according
to Chen et al. (2015), DQ is particularly suitable for cross-sectional and event studies. Since we
focus on disclosure reactions to going-private events, DQ is a fitting choice. Third, unlike
voluntary disclosure measures, which typically only apply to a subset of public firms, researchers
22 In our study, we are interested in changes in DQ year-over-year. To guard against the concern that changes are due
to changes in the weights rather than changes in disclosure, we employ two alternative weighting schemes for DQ_BS.
The first alternative is a constant-weight scheme in which we calculate the group weights in the first year that a firm
appears in the sample and use those weights for all subsequent years. The second alternative is an equal-weight scheme
in which each proportion of non-missing items is weighted by 1/11.
21
can construct DQ for virtually all firms in Compustat, resulting in a larger and more representative
sample.
4.4 GOING-PRIVATE ACTIVITY
We measure going-private activity as the intensity of going-private events in a given
industry-year. πΊπΟ,t is calculated as the sum of the total assets of going-private firms in a 4-digit
SIC industry Ο in year t scaled by the total assets of the same industry in the same year. This
percentage of industry assets that go private and no longer participate in the SEC reporting regime
conceptually measures the intensity of going-private transactions.
4.5 CONTROL VARIABLES
Xi,t is a vector of firm characteristic variables to control for the impact of firm-level
attributes on corporate disclosures. Firm size has a significant impact on firmsβ disclosures (Lev
and Penman 1990; Frankel, McNichols, and Wilson 1995); thus, we include the logarithm of
market value of equity (Size). To control for the impacts of operational complexity, volatility and
risk on disaggregation (Huang et al. 2017; Chen et al. 2015), we include a return volatility variable,
StdROEi,t , the amount of special items, Special Itemsi,t, the number of business segments,
NumSegi,t
, an indicator for restructuring activity, Restructuringi,t, and an indicator variable for
merger and acquisition activity, Acquisitioni,t
. We include a firmβs leverage ratio, Leveragei,t
, to
control for the potential relationship between a firmβs capital structure and its disclosure policy (Li
2010; Bertomeu, Beyer and Dye 2011). Litigation risk affects corporate disclosure strategies
(Francis, Philbrick, and Schipper 1994). Following Francis et al. (1994), we create HiLit, an
indicator variable equal to 1if the firm operates in a high litigation risk industry.
22
Growth opportunities are a determinant of managersβ disclosure incentives. Bamber and
Cheon (1998) argue that managers are more reluctant to reveal proprietary information when there
are greater growth opportunities. We control for potential growth opportunities using BTMi,t which
is the ratio of book value of equity to market value of equity.
Miller (2002) documents that performance influences disclosure strategies. We include
four variables to control for firm performance: ROAi,t , Lossi,t , EPSInci,t , and CARi,t . ROAi,t
measures firm iβs profitability in year t. Lossi,t is an indicator variable that equals one if firm i
incurs a loss in year t. EPSInci,t, an indicator variable equal to one if the firmβs diluted earnings
per share excluding extraordinary items (EPSFX) is higher than that of the previous year (Aobdia
2018). CARi,t is the firmβs cumulative abnormal returns during the past 12 months.
Outside parties can influence disclosure quality decisions. Ajinkya, Bhojraj and Sengupta
(2005) suggest that firms with higher institutional ownership issue more and higher quality
voluntary disclosure. We control for institutional ownership (IO) by calculating the percentage of
outstanding shares owned by institutional investors at the fiscal year-end using 13-F filings. We
control for analysts influence on managersβ disclosure choices using analyst following (Analysts)
(Chapman and Green 2018). Prior research documents interactions between financial statement
disaggregation and audit processes (Libby and Brown 2013; Beck et al. 2016). We include a
dummy variable Big4i,t
if the firm is audited by a Big-4 audit firm in year t based on data from
Audit Analytics.
Frankel et al. (1995) document a positive association between raising capital in public
markets and disclosing earnings forecasts. We include two indicator variables, NewEquityi,t
and
NewDebti,t , equal to one, respectively, if the firm issues public equity and debt during the
23
subsequent two-year window (year t+1 to t+2). Appendix A provides detailed definitions of all
the variables in the analyses.
4.6 CROSS-SECTIONAL MODELS AND VARIABLE SPECIFICATIONS
For cross-sectional tests of peer disclosure responses to going-private activity (H2-H4), we
estimate the following using OLS regression:
π·ππ,π‘ = π½0 + π½1πΊππ,π‘ + π½2ππ,π‘ + π½3πΊππ,π‘ β ππ,π‘ + π½4π·ππ,π‘β1 + πΏππ,π‘ + ππΌπππ’π π‘ππ¦ πΉπΈ + πππππ πΉπΈ + ππ (4)
where Wi,t is one of the cross-sectional variables of interest described below.
H2 predicts that the relation between going-private activity and peer firm disclosure quality
increases will be weaker for firms with high proprietary costs (Ξ²3<0). We measure proprietary
costs in two ways. First, we use the text-based product market fluidity measure from Hoberg et al.
(2014). The measure compares a firmβs product words in its 10-K business description to rivalsβ
words and captures how intensely the firmβs product market is changing in a given year. We set
Hi_Fluidity equal to 1 if the firmβs product market fluidity measure is in the highest decile in the
industry-year. Our second measure of proprietary costs is the Herfindahl-Hirschman index, a
measure of competition calculated as the sum of squared market share across all firms in the same
4-digit SIC industry. Hi_Comp equals 1 if the firm operates in an industry which has a Herfindahl-
Hirschman index in the lowest quintile in a given year.
H3 predicts that peer disclosure responses to going-private activity are stronger for firms
that have recently experienced reductions in analyst following (Ξ²3>0). We set Red_Analysts equal
to 1 if a firmβs analyst following decreased by more than 50% from the prior year. Finally, H4
predicts an exacerbated relation between going-private activity and peer firms disclosure quality
increases for firms with high comovement (Ξ²3>0). We measure comovement as a firmβs modified
24
stock price synchronicity. For this measure, we first regress firm iβs stock return on the past,
contemporaneous and future value-weighted 2-digit SIC industry returns by firm-year as follows:
π ππ‘π,π‘ = π½0 + π½1πΌππ_π ππ‘π,π‘β1 + π½2πΌππ_π ππ‘π,π‘ + π½3πΌππ_π ππ‘π,π‘+1 + νπ,π‘ (5)
Then, we take the adjusted R-squared of each firm-year regression and calculate a stock price co-
movement measure following Park et al. (2019).
πΆππππ£π = log (1 +π΄ππ.π 2
1βπ΄ππ.π 2) (6)
Hi_Comove equals 1 if Comove is in the top decile in the industry-year.
5. Results
5.1 DESCRIPTIVE STATISTICS
Table 2 Panel A presents descriptive statistics. All continuous variables are winsorized at
the 1st and 99th percentiles to mitigate the potential impact of outliers. The mean (median) DQ in
the sample is 0.797 (0.799). The mean and median of the constant-weighted DQ measure, DQCW,
are both 0.798. The mean and median of the equal-weighted DQ measure, DQEW, are slightly
higher at 0.825 and 0.813, respectively. On average, 0.2% of total industry assets go private in a
year.
A typical industry peer of a going-private firm has a mean (median) market capitalization
of $652 ($644) million, return-on-assets (ROA) of -0.6% (3.5%), standard deviation of return-on-
equity (StdROE) of 0.636 (0.093), number of business segments (NumSeg) of 1.520 (1), book-to-
market ratio (BTM) of 0.538 (0.444) and a leverage ratio (Leverage) of 0.500 (0.469). Sample
firms experience losses (Loss) in 30.9% of the sample years, experience EPS increases year-over-
25
year (EPSInc) in 55.1% of sample years, and experience a mean 12-month market-adjusted
cumulative abnormal returns of 2.8%.
Mean special items (Special Items) constitute -2.0% of total assets. On average, 34.7% of
the sample restructures (Restructuring), 41.7% makes an acquisition (Acquisition), 30.8% operates
in a high litigation-risk environment (HiLit), 76.5% has Big-4 auditors (Big4), 5.3% experiences a
substantial reduction in analyst following (Red_Analysts), 34.0% has high comovement, 5.9% has
high product market fluidity (Hi_Fluidity), 18.1% experiences high competition based on the
Herfindahl-Hirschman index (Hi_Comp), and 15.1% (20.4%) raises public equity (debt) in the
subsequent two-year period (NewEquity; NewDebt). The mean (median) institutional ownership
(IO) is 64.4% (72.5%) and analyst following (Analysts) is 7.243 (5).
Table 2 Panel B tabulates the distribution of going-private transactions by calendar year.
The largest number of going-private transactions in a year was 79 in 2007. The sample year with
the least going-private activity was 2015 with 27 transactions. Across the 10 sample years, the
mean number of going-private transactions in a calendar year is 48.2.
Table 3 presents a correlation matrix with Pearson correlations above the diagonal and
Spearman correlations below. The three disaggregation quality measures β DQ, DQCW, and
DQEW β are highly positively correlated with correlations between 0.85 and 0.97. DQ is positively
correlated with GP, suggesting a positive linear relationship between peer firmsβ disclosure quality
and going-private events. The correlations of 0.09 (Pearson) and 0.16 (Spearman) are fairly low,
but DQ is a level measure here and we are interested in changes in our tests.
26
5.2 MULTIVARIATE ANALYSIS
5.2.1 Peer Firm Disclosure Response. This section reports on the multivariate tests of peer
firm disclosure responses to going-private activity. Table 4 presents results of Equation 1 across
various specifications. The dependent variable in Column (1) is DQ from Chen et al. (2015). To
guard against the concern that changes in DQ are due to changes in the measurementβs weights
rather than changes in the presentation of line items, we employ two alternative weighting schemes.
Column (2) uses a constant-weighted measure as the dependent variable, DQCW, and Column (3)
uses an equal-weighted measure, DQEW. Across the three columns, we find a statistically
significant positive association between DQ and GP. Thus, consistent with H1, peer firms respond
to the lost information spillover from going-private activity by increasing the fineness or precision
of their mandatory financial reports. This response suggests that going-private activity imposes a
negative externality. The negative externality prompts peer firms to incur costs to regain the lost
informational benefits.
Table 4 Columns (1) through (3) show that firm size is negatively associated with
disclosure quality, which is inconsistent with prior studies that typically find a positive association
between size and the amount of disclosure. This could be the case because prior studies typically
use voluntary disclosure proxies to measure the amount of disclosure while DQ captures firmsβ
reporting strategies in mandatory disclosures. BTM, Leverage, Restructuring, Acquisition, and
NewDebt are negatively associated with DQ while HiLit, EPSInc and Analysts are positively
associated with DQ, generally consistent with findings of prior studies (Aobdia 2018; Xi 2010;
Ajinkya et al. 2005).
27
In the tests of Equation (1) in Table 4, we find evidence that the intensity of going-private
transactions in an industry is positively associated with disclosure quality increases of remaining
public firms on average. It suggests that the information externality concern dominates
managementβs financial statement disaggregation decisions after their industry peers go private.
However, managers may still consider proprietary costs when making disaggregation decisions.
Hence, we conduct cross-sectional analyses to see whether the main effect varies with the level of
competitive costs.
Table 5 presents the results. The measure of competition in Columns (1) to (3) is high
product market fluidity (Hi_Fluidity) and the measure in Columns (4) to (6) is high competitive
concerns based on the Herfindahl-Hirschman index (Hi_Comp). Across all six columns, we find a
negative coefficient on the interaction term (p<0.10 or better). The main effect of GP remains
significant at the 5% level. Thus, consistent with H2, the relation between going-private activity
and peer firm disclosure quality increases is weaker for firms with greater proprietary cost concerns.
In fact, in four of the six specifications, we fail to document any increased disclosure quality
subsequent to going-private activity among firms facing high competition, consistent with
Verrecchia and Weber (2006) and Li (2010).23
5.2.2 Disclosure to Attract Analysts. H3 predicts that the relation between going-private
activity and peer firm disclosure quality increases will be stronger for firms that want to attract
analyst coverage. We interact GP with Red_Analysts, an indicator variable equal to one if a firmβs
analyst following decreased by more than 50% from the prior year and zero otherwise. Table 6
presents the results of the cross-sectional tests. Consistent with H3, we find a significantly positive
23 The sum of the main effect and interaction is statistically insignificant based on F-tests in columns (3) through (6).
28
coefficient on the interaction term in all three columns (p < 0.05). This suggests that the net benefits
of providing more disaggregated financial reports are greater for firms that not only want to replace
lost information transfer but also want to regain analyst coverage.
5.2.3 Disclosure to Attract Investors. H4 predicts that peer firm disclosure responses to
going-private activity will be greater for firms that want to attract limited investor resources. We
interact GP with an indicator variable equal to one for firms with high return synchronicity with
industry co-members (Hi_Comove). Firms with higher comovement represent firms with greater
substitutability of assets. Because these firms cannot rely on unique company operations to attract
investors, they turn to other means such as disclosure (Fishman and Hagerty 1989; Park et al. 2019).
Consistent with H4, we find significantly positive coefficients on the interaction term in Table 7
Columns (1) and (2) (p<0.10). However, the interaction term in Column (3) is positive but
insignificant. It is not surprising that the high comovement result is weak given that comovement
with industry peers not only captures competition for investor resources, but also captures the
ability to free-ride on remaining public firm peers. Overall, some evidence suggests that the
motivation to increase disclosure quality is exacerbated when firms are competing with industry
peers for limited investor resources.
6. Conclusion
For decades, U.S. capital markets have seen publicly listed firms go private. Going private
typically terminates reporting duties under the Exchange Act of 1934 resulting in a substantial
reduction in the information environment surrounding newly private firms. We examine the
spillover effects of going-private activity on industry peersβ disclosure quality. We find that
subsequent to going-private activity, industry peers increase the disaggregation of their mandatory
financial reports. This suggests that the lost information transfer signal imposes a negative
29
externality on peer firms that remain public, inducing peers to increase costly disclosures to regain
informational benefits. We further find evidence that firms consider proprietary costs in making
their disclosure decisions: firms facing high competition are less likely to increase disaggregation
following going-private activity. Finally, our results suggest that firms have stronger increases in
disclosure quality when motivated to regain analyst coverage or attract limited investor resources.
We contribute to the going-private literature. Most prior research focuses on the going-
private firms themselves or their shareholders; however, it is important to understand the broader
implications of going private. Understanding the externalities imposed on other firms can help
inform policy decisions in areas such as alleviating or adding to public companiesβ regulatory costs.
We extend the literature on disclosure spillover by providing evidence that free-riding impacts
mandatory disclosure practices. Increases in mandatory disclosure quality subsequent to lost
information transfer suggest that firms could provide more detailed financial statements but choose
not to do so when information spillover and thus free-riding ability are high.
30
REFERENCES
Admati, A., and P. Pfleiderer. 2000. Forcing firms to talk: Financial disclosure regulation and
externalities. Review of Financial Studies 13 (2): 479-519
Ajinkya, B., S. Bhojraj., and P. Sengupta. 2005. The association between outside directors,
institutional investors and the properties of management earnings forecasts. Journal of
Accounting Research 43 (3): 343-376
Albuquerque, A. 2009. Peer firms in relative performance evaluation. Journal of Accounting and
Economics 48 (1): 69-89
Allee, K., and T. L. Yohn. 2009. The demand for financial statements in an unregulated
environment: An examination of the production and use of financial statements by
privately held small businesses. The Accounting Review 84 (1): 1-25
Aobdia, D. 2018. Employee mobility, non-compete agreements, product market competition and
company disclosure. Review of Accounting Studies 23 (1): 296-346
Badertscher, B., N. Shroff, and H. White. 2013. Externalities of public firm presence: Evidence
from private firmsβ investment decisions. Journal of Financial and Economics 109 (3):
682-706.
Baginski, S., and L. Hinson. 2016. Cost of capital free-riders. The Accounting Review 91 (5): 1291-
1313.
Bamber, L. S., and Y. S. Cheon. 1998. Discretionary management earnings forecast disclosures:
Antecedents and outcomes associated with forecast venue and forecast specificity choices.
Journal of Accounting Research 36 (2): 167-190
Bartlett, R. P. 2009. Going private but staying public: Reexamining the effect of Sarbanes-Oxley
on firmsβ going-private decisions. University of Chicago Law Review 76 (1): 7-44
Beck, M. J., M. Glendening and C. E. Hogan. 2018. Financial statements disaggregation, auditor
effort and financial reporting quality. Working Paper, Michigan State University and
University of Missouri.
Bertomeu, J., A. Beyer, and R. Dye. 2011. Capital structure, cost of capital and voluntary
disclosures. The Accounting Review 86 (3): 857-886
Bharath, T. S., and A. Dittmar. 2010. Why do firms use private equity to opt out of public markets?
Review of Financial Studies 23 (5): 1771-1818
Bianchi, P. A., M. Minutti-Meza, M. A. Philips, and M. Vulcheva. 2018. Disclosure, Litigation,
and Negotiation Outcomes in Going Private Transactions. Working Paper, University of
South Florida and University of Miami.
Bonner, S., A. Hugon, and B. Walther. 2007. Investor reaction to celebrity analysts: The case of
earnings forecast revisions. Journal of Accounting Research 45 (3): 481-513.
Bova, F., K. Kolev, J. Thomas, and X. Zhang. 2015. Non-executive employee ownership and
corporate risk. The Accounting Review 90: 115-145.
Breuer, M., K. Hombach, and M.A. MΓΌller. 2018. When you talk, I remain silent: Spillover effects
of peersβ mandatory disclosures on firmsβ voluntary disclosures. Working paper, Columbia
University, Frankfurt School of Finance and Management and WHU-Otto Beisheim
School of Management.
Cao, S., C. Wan, G. Ma, and J. Tucker. Technological peer pressure and product disclosure. The
Accounting Review. Forthcoming.
31
Chapman, K., and J. R. Green. 2018. Analystsβ influence on managersβ guidance. The Accounting
Review. 93 (1): 45-69
Chen, S., B. Miao, and T. Shevlin. 2015. A new measure of disclosure quality: The level of
disaggregation of accounting data in annual reports. Journal of Accounting Research 53(5):
1017-1054.
DeAngelo, H., L. DeAngelo, and E. Rice. 1984. Going private: Minority freezeouts and
stockholder wealth. Journal of Law and Economics 27 (2): 367-401.
Dye, R. 1990. Mandatory versus voluntary disclosures: The cases of financial and real externalities.
The Accounting Review 65 (1): 1-24.
Engle, E., R. Hayes and X. Wang. 2007. The Sarbanes-Oxley Act and firmsβ going-private
decisions. Journal of Accounting and Economics 44: 116-145.
Fishman, M. J., and K.M. Hagerty. 1989. Disclosure decisions by firms and the competition for
price efficiency. The Journal of Finance 44(3): 633-646.
Francis, J., D. Philbrick., and K. Schipper. 1994. Shareholder litigation and corporate disclosures.
Journal of Accounting Research 32 (2): 137-164.
Frankel, R., and X. Li. 2004. Characteristics of a firmβs information environment and the
information asymmetry between insiders and outsiders. Journal of Accounting and
Economics 37 (2): 229-259.
Frankel, R., M. McNichols, and G. P. Wilson. 1995. Discretionary disclosure and external
financing. The Accounting Review 70 (1): 135-150
Guenther, D., and A. Rosman. 1994. Differences between COMPUSTAT and CRSP SIC codes
and related effects on research. Journal of Accounting and Economics 18: 115-128
Halpern, P., R. Kieschnick, and W. Rotenberg. 1999. On the heterogeneity of leveraged going
private transactions. Review of Financial Studies 12 (2): 281-309
Healy, P., and K. Palepu. 2001. Information asymmetry, corporate disclosure, and the capital
markets: a review of the empirical disclosure literature. Journal of Accounting and
Economics 31: 405-440
Hirst. D. E., L. Koonce, and S. Venkataraman. 2008. Management earnings forecasts: A review
and framework. Accounting Horizons 22 (3): 315-338.
Hoberg, G., G. Phillips, and N. Prabhala. 2014. Product market threats, payouts, and financial
flexibility. Journal of Finance 69 (1): 293-324.
Huang, Y., R. Jennings, and Y. Yu. 2017. Product market competition and managerial disclosure
of earnings forecasts: Evidence from import tariff rate reductions. The Accounting Review
92 (3): 185-207.
Kaplan, S. and P. StrΓΆmberg. 2009. Leveraged buyouts and private equity. Journal of Economic
Perspectives 23 (1): 121-146
Koenig, J. M. 2004. A brief roadmap to going private. Columbia Business Law Review 2: 506-539
Leuz, C., A. Triantis, and T. Wang. 2008. Why do firms go dark? Causes and economic
consequences of voluntary SEC deregistrations. Journal of Accounting and Economics 45:
181- 208
Lev, B., and S. Penman. 1990. Voluntary forecast disclosure, non-disclosure, and stock prices.
Journal of Accounting Research 28 (1): 49-76
32
Li, X. 2010. The impacts of product market competition on the quantity and quality of voluntary
disclosures. Review of Accounting Studies 15 (3): 663-711
Libby, R., and T. Brown. 2013. Financial statement disaggregation decisions and auditorsβ
tolerance for misstatement. The Accounting Review 88 (2): 641-665.
Merton, R. 1987. A simple model of capital market equilibrium with incomplete information.
Journal of Finance 42 (3): 483-510.
Miller, G. S. 2002. Earnings performance and discretionary disclosure. Journal of Accounting
Research 40 (1): 173-204.
Minnis, M. and N. Shroff. 2017. Why regulate private firm disclosure and auditing? Journal of
Accounting and Business Research 47: 473-502
Morrison & Foerster. 2012. The Mofo guide to U.S. privatizations. San Francisco, CA: Morrison
& Foerster LLP
Park, S. Y., C. M. Schrand, and F. S. Zhou. 2019. Management forecasts and competition for
limited investor resources. Working paper, University of Pennsylvania.
Renneboog, L. D. R., and T. Simons. 2005. Public-to-private transactions: LBOs, MBOs, MBIs
and IBOs. CenterER Discussion Paper.
Roulstone, D. 2003. Analyst following and market liquidity. Contemporary Accounting Research
20 (3): 552-578.
Securities and Exchange Commission (SEC). 2018. Securities and Exchange Act of 1934.
Available at: https://www.sec.gov/answers/about-lawsshtml.html
Securities and Exchange Commission (SEC). 2011. Going private. Available at:
https://www.sec.gov/fast-answers/answersgoprivhtm.html
Shroff, N., R. Verdi, and B. Yost. 2017. When does the peer information environment matter?
Journal of Accounting and Economics 64: 183-214
Slovin, M., M. Sushka, and Y. Bendeck. 1991. The intra-industry effects of going-private
transactions. Journal of Finance. 46 (4): 1537-1550
Travlos, N. G., and M. M. Cornett. 1993. Going private buyouts and determinants of shareholdersβ
returns. Journal of Accounting, Auditing and Finance 8 (1): 1-25
Verrecchia, R. E. 1983. Discretionary disclosure. Journal of Accounting and Economics 5 (C):
179-194
33
APPENDIX A
Variable Definitions
Variable Definition Data Source
DQ
Disclosure quality (DQ) measure calculated following Chen et al.
(2015). The measure is calculated by counting the number of non-
missing line items in firms' year-end financial statements (balance
sheets and income statements). Chen et al. (2015) value-weight the
percentage of non-missing line items in the balance sheet using the
respective group account balance relative to total assets to reflect the
differential materiality of each group account. The weight is firm-
year specific.
Compustat
DQCW
Constant-weight DQ measure. We modified the weighting scheme
of the DQ measure constructed by Chen et al. (2015) by employing
a constant-weight scheme. We fix the weights by using a firmβs
weights from the first year it appears in our sample for all years. This
approach still accommodates the differential materiality of each
group account but removes the confounding effects of changing
weights.
Compustat
DQEW
Equal-weight DQ measure. We modified the weighting scheme of
the DQ measure constructed by Chen et al. (2015) by employing an
equal-weight scheme. We equally weight the percentage of non-
missing items so that variations in the measure are solely driven by
changes in line item disaggregation and not changes in weights.
Compustat
GP Total assets of going-private firms in a 4-digit SIC industry in a given
year divided by the total assets of the same industry in the same year. Compustat
Size Logarithm of market value of equity.
=LN(CSHO*PRCC_F) Compustat
StdROE The standard deviation of the firmβs return on equity (ROE) over the
past five years (t-5 to t). Compustat
Special Items Special items scaled by total assets.
=SPI/ AT Compustat
NumSeg Number of industry segments of the firm. Compustat
Segment Data
Restructuring An indicator variable that equals 1 if the firmβs Restructuring Costs
Pretax (RCP) is nonzero. Compustat
Acquisition
An indicator variable that equals 1 if the firmβs AQC is greater than
0. AQC represents cash outflow of funds used for and/or the costs
relating to acquisition of a company in the current year or effects of
an acquisition in a prior year carried over to the current year.
Compustat
Leverage
Leverage ratio calculated as total liabilities minus deferred taxes
scaled by total assets.
=(LT-TXDB)/AT
Compustat
HiLit
An indicator variable that equals 1 if the firm operates in the
following high litigation industries: biotechnology (SIC codes 2833-
2836 and 8731-8734), computers (SIC codes 3570-3577 and 7370-
7374), electronics (SIC codes 3600-3674) and retailing (SIC codes
5200-5961).
Compustat
BTM Book value of equity divided by market value of equity.
=CEQ/(CSHO*PRCC_F) Compustat
ROA
The firmβs accounting return-on-assets calculated as income before
extraordinary items scaled by total assets.
= IB/AT
Compustat
Loss An indicator variable that equals 1 if the firmβs income before
extraordinary items (IB) is negative. Compustat
34
Variable Definition Data Source
EPSInc
An indicator variable that equals 1 if the firmβs current-year diluted
earnings per share excluding extraordinary items (EPSFX) is higher
than that of the previous year.
Compustat
CAR The firmβs cumulative market-adjusted abnormal returns during the
past 12-months. CRSP
IO
The firmβs institutional ownership calculated as the percentage of
outstanding shares owned by institutional investors at the fiscal year-
end using 13-F filings.
Thomson Reuters
13-F
Analysts Number of analysts following the firm. I/B/E/S
Big4
An indicator variable that equals 1 if the firm has a Big4 auditor. We
use Audit Analytics to obtain firmsβ auditors and create the dummy
variable that equals 1 if the firmβs Auditor_fkey in the Audit Opinions
dataset is between 1 and 4.
Audit Analytics
NewEquity An indicator variable that equals 1 if the firm issued public equity
in a subsequent two-year period (t+1 to t+2). SDC
NewDebt An indicator variable that equals 1 if the firm issued public debt in
a subsequent two-year period (t+1 to t+2). SDC
Fluidity
Product market fluidity measure constructed by Hoberg et al. (2014)
using computational linguistics techniques. Fluidity measures how
dynamically the product market space around a firm is changing in a
given year.
Hoberg and
Philips Data
Library
Hi_Fluidity
An indicator variable that equals 1 if the firmβs product market
fluidity measure is within the highest decile in the industry-year.
Hoberg and
Philips Data
Library
Hi_Comp An indicator variable that equals 1 if the firmβs Herfindahl-
Hirshman index is within the lowest quintile in a fiscal year. Compustat
Red_Analysts An indicator variable that equals 1 if a firmβs analyst following
decreased by more than 50% from the previous year. I/B/E/S
Comove
A firmβs modified stock price synchronicity. We first regress firm iβs
stock return on the past, contemporaneous and future value-weighted
2-digit SIC industry returns by firm-year as follows:
π ππ‘π,π‘ = π½0 + π½1πΌππ_π ππ‘π,π‘β1 + π½2πΌππ_π ππ‘π,π‘ + π½3πΌππ_π ππ‘π,π‘+1 + νπ,π‘
Then, we take the adjusted R-squared of each firm-year regression
and calculate a stock price co-movement measure following Park et
al. (2019).
πΆππππ£π = log (1 +π΄ππ.π 2
1βπ΄ππ.π 2)
CRSP
Hi_Comove An indicator variable that equals 1 if Comove is within the top
decile in the industry-year. CRSP
35
APPENDIX B
Timeline and Going-private Measure
Figure 1: Timeline
Appendix B explains the calculation of the main independent variable, GP, which involves several steps.
We follow Bartlett (2009) in obtaining the list of going-private transactions. First, we obtain a
comprehensive list of delisting events from the CRSP Delisting dataset and use the delisting codes to screen
out a list of delisting events that are likely due to going-private transactions. Then, we merge the filtered
delisting events firms with the SDC Mergers and Acquisitions dataset using CUSIP. Certain delisting events
are simply attributable to the acquisition of the delisting firm by another publicly-traded firm. We do not
consider these acquisitions as going-private transactions as the acquiring firm is subject to the SEC public
reporting rules. We only retain the acquisitions of the delisting firms by private acquirers. After this step,
we are left with a list of delisting events without matches to the SDC Mergers and Acquisitions dataset. We
manually check this list of remaining delisting firmsβ SEC EDGAR records to look for filings of SEC Form
15 (Certification and Notice of Termination of Registration) or SEC Form 13e-3. We search for reasons
that cause firms to file Form 15 through business presses and companiesβ 8-K filings and exclude firms that
simply deregister their securities for βgoing-darkβ reasons (Leuz et al. 2008). The final list of going-private
events include 13e-3 going-private transactions and buyouts by private equity firms and the firmβs
management. To ensure the going-private transactions represent meaningful decreases in the public
information available surrounding the going-private companies, we manually check the going-private firmsβ
EDGAR record page to see if the companies did cease their reporting obligations after the effective date of
the transaction.
We then match the going-private firms with their COMPUSTAT records to obtain their most-recent total
assets prior to going private. For each peer firm, we calculate GP for year t as the total assets of going-
private firms in its 4-digit SIC industry during a fiscal year t scaled by the total assets of all COMPUSTAT
firms in that 4-digit SIC industry in the same fiscal year. The measure captures the intensity of going-private
activity within an industry and thus the amount of public information loss due to going-private activity. Our
research design only retains firms with calendar fiscal year ends for sharper identification purposes. The
intuition is that firms with fiscal years ended on December 31st base their disclosure decisions on the same
set of information. Most importantly, these firms make their disclosure decisions simultaneously. Firms
that do not have a calendar fiscal year end observe not only a set of going-private transactions but also
disaggregation decisions of their industry peers who end their fiscal year on December 31st of a given year.
Therefore, they may βfree-rideβ on the information provided by their industry peers who have earlier fiscal
year ends and provide less disaggregated financial reports.
12/31(FYE) of Year t 12/31(FYE) of Year t -1
t t-1
Going-private transactions
t+1
10-K filing date
Disaggregation decisions, 10-K
preparation and year-end audit work
36
TABLE 1: Sample Selection
Panel A: Going-private Sample
Total number of delistings from Jan 3, 2006 to Dec 31, 2015 4,577
Exclude delistings with delisting codes other than 233, 261, 262, 570, 573 (2,758)
Exclude financial firms and regulated utilities firms (404)
Exclude mergers & acquisition deals with public acquirers (841)
Exclude foreign private issuers, firms that voluntarily disclose financial
statements and going-dark firms (86)
Exclude firms with missing SIC codes (6)
Final number of going-private transactions 482
Panel B: Peer Sample
COMPUSTAT firms with fiscal years ended during 2006-2015 with
Dec 31st fiscal year ends 68,290
Exclude firms that changed fiscal year end during the sample period (5,511)
Exclude firms with missing SIC codes (10,444) Exclude regulated utilities firms and financial firms (15,151)
Exclude firms with missing DQ measures or control variables (22,857)
Final peer sample 14,327
Note: Table 1 describes the sample selection process. Panel A shows the sample of going-private firms
and Panel B shows the sample of peer firms.
37
TABLE 2: Descriptive Statistics
Panel A: Summary Statistics
Variable N Mean Std. Dev 1th Pct. 25th Pct. Median 75th Pct. 99th Pct.
DQ 14,327 0.797 0.069 0.600 0.767 0.799 0.840 0.936
DQCW 14,248 0.798 0.067 0.609 0.767 0.798 0.839 0.938
DQEW 14,248 0.825 0.050 0.731 0.790 0.813 0.858 0.952
GP 14,327 0.002 0.006 0 0 0 0.000 0.030
Size 14,327 6.480 1.932 2.115 5.193 6.467 7.748 10.950
StdROE 14,327 0.636 2.212 0.012 0.047 0.093 0.245 15.471
Special Items 14,327 -0.020 0.069 -0.423 -0.015 -0.002 0.000 0.093
NumSeg 14,327 1.520 0.824 1 1 1 2 4
Restructuring 14,327 0.347 0.476 0 0 0 1 1
Acquisition 14,327 0.417 0.493 0 0 0 1 1
Leverage 14,327 0.500 0.295 0.069 0.302 0.469 0.643 1.374
HiLit 14,327 0.308 0.462 0 0 0 1 1
BTM 14,327 0.538 0.701 -1.307 0.245 0.444 0.743 3.398
ROA 14,327 -0.006 0.206 -0.751 -0.021 0.035 0.075 0.265
Loss 14,327 0.309 0.462 0 0 0 1 1
EPSInc 14,327 0.551 0.497 0 0 1 1 1
CAR 14,327 0.028 0.456 -1.222 -0.215 0.023 0.267 1.331
IO 14,327 0.644 0.305 0.002 0.417 0.725 0.892 1.086
Analysts 14,327 7.243 6.646 0 2 5 11 26
Big4 14,327 0.765 0.424 0 1 1 1 1
NewEquity 14,327 0.151 0.358 0 0 0 0 1
NewDebt 14,327 0.204 0.403 0 0 0 0 1
Fluidity 14,170 6.448 3.367 1.421 4.060 5.774 8.075 16.957
Comove 13,964 0.340 0.497 -0.288 -0.043 0.223 0.598 1.894
Red_Analysts 14,327 0.053 0.225 0 0 0 0 1
Hi_Fluidity 14,170 0.059 0.236 0 0 0 0 1
Hi_Comp 14,327 0.181 0.385 0 0 0 0 1
Hi_Comove 13,964 0.059 0.236 0 0 0 0 1
38
Panel B: Distribution of Going-Private Transactions by Calendar Year
Year Number of Transactions
2006 62
2007 79
2008 45
2009 35
2010 42
2011 55
2012 48
2013 51
2014 38
2015 27
Note: Table 2 Panel A provides descriptive statistics for the variables used throughout the analyses.
Continuous variables are winsorized at the 1st and 99th percentiles. See variable definitions in Appendix A.
Panel B displays the number of going-private transactions in each sample calendar year.
39
TABLE 3: Pairwise Correlations
Variable (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16)
(1) DQ
0.97 0.85 0.09 -0.15 -0.06 0.00 -0.04 -0.31 0.21 -0.02 0.06 -0.04 -0.12 -0.17 -0.16
(2) DQCW 0.97
0.86 0.09 -0.16 -0.06 -0.01 -0.04 -0.28 0.21 -0.03 0.04 -0.05 -0.13 -0.16 -0.16
(3) DQEW 0.90 0.89
0.07 -0.18 -0.05 0.03 -0.15 -0.32 0.18 -0.01 0.02 -0.10 -0.10 -0.04 -0.12
(4) GP 0.16 0.15 0.15
-0.06 0.00 0.00 -0.02 -0.01 0.10 -0.04 0.00 -0.02 -0.02 -0.01 -0.07
(5) Size -0.14 -0.15 -0.18 -0.05
0.00 0.16 0.17 0.03 -0.05 -0.25 0.30 0.54 0.75 0.08 0.26
(6) StdROE -0.11 -0.09 -0.05 0.04 -0.19
0.00 0.03 0.28 0.02 -0.17 -0.09 -0.04 -0.01 0.07 -0.02
(7) Special Items 0.12 0.11 0.18 0.03 0.01 -0.03
-0.15 -0.10 -0.01 -0.05 0.51 0.03 0.07 0.00 0.01
(8) Restructuring -0.07 -0.06 -0.12 -0.04 0.17 -0.04 -0.39
0.16 0.00 -0.05 -0.05 0.16 0.08 -0.11 0.06
(9) Leverage -0.38 -0.35 -0.36 -0.09 0.10 0.36 -0.17 0.19
-0.02 -0.39 -0.26 0.01 0.02 0.05 0.02
(10) HiLit 0.20 0.20 0.18 0.31 -0.06 0.07 0.00 0.00 -0.06
-0.07 -0.09 -0.04 0.03 0.22 -0.10
(11) BTM -0.06 -0.07 -0.06 -0.10 -0.35 -0.30 -0.05 0.00 -0.31 -0.13
0.04 -0.10 -0.15 -0.08 0.02
(12) ROA 0.10 0.08 0.08 -0.04 0.39 -0.23 0.33 -0.10 -0.22 -0.09 -0.21
0.18 0.15 -0.19 0.07
(13) IO -0.05 -0.05 -0.09 -0.02 0.54 -0.18 -0.09 0.15 0.06 -0.04 -0.12 0.18
0.45 0.05 0.18
(14) Analysts -0.09 -0.09 -0.10 0.04 0.78 -0.10 -0.02 0.10 0.07 0.02 -0.26 0.23 0.53
0.16 0.25
(15) Fluidity -0.08 -0.08 -0.05 0.14 0.06 0.16 0.02 -0.12 -0.03 0.22 -0.08 -0.17 0.04 0.17
0.01
(16) Comove -0.13 -0.13 -0.11 -0.05 0.25 -0.04 -0.03 0.06 0.05 -0.08 0.04 0.08 0.16 0.24 0.01
Note: Table 3 presents Pearson correlation coefficients above the diagonal and Spearman correlations below the diagonal for the main variables in
the analyses. See variable definitions in Appendix A. Bolded coefficients are significant at the 1% level.
40
TABLE 4: Main Analysis
Variables (1) DQt (2) DQCWt (3) DQEWt
GP 0.131** 0.133** 0.106** (2.285) (2.328) (1.970)
DQt-1 0.708*** 0.726*** 0.634*** (78.258) (84.667) (78.154)
Size -0.001*** -0.001** -0.001*** (-2.691) (-2.508) (-3.557)
StdROE 0.000** 0.000 0.000 (2.046) (1.371) (1.221)
Special Items 0.011** -0.002 0.005 (2.177) (-0.518) (1.058)
NumSeg -0.000 -0.000 -0.001*** (-0.443) (-0.691) (-2.827)
Restructuring -0.004*** -0.004*** -0.006*** (-6.292) (-6.797) (-9.187)
Acquisition -0.004*** -0.005*** -0.008*** (-6.447) (-8.434) (-12.157)
Leverage -0.022*** -0.018*** -0.020*** (-12.111) (-10.065) (-11.240)
HiLit 0.005*** 0.004*** 0.005*** (4.505) (4.141) (4.651)
BTM -0.004*** -0.003*** -0.004*** (-7.038) (-6.645) (-7.650)
ROA 0.003* 0.002 0.001 (1.673) (1.123) (0.532)
Loss -0.001 -0.000 -0.000 (-0.864) (-0.513) (-0.637)
EPSInc 0.003*** 0.003*** 0.003*** (5.079) (5.109) (4.299)
CAR 0.001 -0.000 -0.000 (0.621) (-0.268) (-0.417)
IO 0.000 0.000 -0.001 (0.229) (0.321) (-0.709)
Analysts 0.000** 0.000* 0.000** (1.974) (1.787) (2.353)
Big4 -0.000 -0.000 -0.000 (-0.539) (-0.203) (-0.369)
NewEquity 0.001 0.002* 0.001* (1.517) (1.943) (1.664)
NewDebt -0.002*** -0.002** -0.002** (-2.986) (-2.533) (-2.552)
Intercept 0.243*** 0.229*** 0.324*** (16.656) (18.690) (39.197)
Year FE Yes Yes Yes
Industry FE Yes Yes Yes
Cluster SE? Firm Firm Firm
Observations 14,327 14,234 14,234
Adjusted R2 0.759 0.756 0.613
41
Note: Table 4 tabulates the OLS estimation results of the following regression equation:
DQt=Ξ±+Ξ²1GPt + ΞΈDQt-1 + β Ξ³ πΆπππ‘πππ πππππππππ π‘ + β Year FE + β Industry FE + νπ‘
Column 1 uses the DQ measure constructed by Chen et al. (2015) which employs a value-weighting scheme
in computing the balance-sheet component of the DQ measure (DQ_BS). Specifically, the DQ_BS measure
is calculated as following:
β {(#ππππππ π πππ πΌπ‘πππ
#πππ‘ππ πΌπ‘πππ )π Γ
$π΄π π ππ‘π π
$πππ‘ππ π΄π π ππ‘π }
11
π=1
Γ· 2
where k indexes group accounts. Chen et al. (2015) classify 93 subaccounts in firmsβ balance sheets into
11 group accounts. Then, they calculate the proportion of non-missing subaccounts within each group
account. The proportion of non-missing subaccounts within each group account is value-weighted based
on the ratio of the balance of the group account over the total asset value to account for differential
materiality of the 11 group accounts. Model 2 uses a modified version of the Chen et al. (2015) measure,
which employs a constant-weight scheme, DQCW. The weight used by Chen et al. (2015) is firm-year
specific, which means the measure could vary due to changes in weights rather than changes in the number
of non-missing accounts. We modify the measure by using the weight of each sample firm in the first year
it appears in the sample. In other words, we fix the weight to mitigate any confounding effects of changing
weights. Model 3 uses an equal-weight measure which weights the percentage of non-missing items of each
group account equally, DQEW. Control variables are defined in Appendix A. We include year- and
industry- fixed effects in all specifications. t-statistics are provided in the parentheses. Standard errors are
clustered by firm. ***, ** and * indicate significance at the 1%, 5% and 10% levels, respectively.
42
TABLE 5: Competitive Costs
Variables (1) DQt (2) DQCWt (3) DQEWt (4) DQt (5) DQCWt (6) DQEWt
GP 0.135** 0.135** 0.107** 0.162*** 0.161*** 0.151***
(2.324) (2.321) (1.964) (2.720) (2.676) (2.666)
Hi_Fluidity (Hi_Comp) -0.001 -0.002 0.000 -0.002 -0.002 -0.002
(-0.613) (-1.261) (0.301) (-1.524) (-1.482) (-1.352)
GPxHi_Fluidity (GPxHi_Comp) -0.036** -0.032** -0.033*** -0.149* -0.155** -0.177**
(-2.472) (-2.567) (-3.330) (-1.915) (-2.004) (-2.275)
DQt-1 0.709*** 0.726*** 0.634*** 0.662*** 0.685*** 0.612***
(77.926) (84.344) (77.881) (71.826) (77.991) (71.777)
Size -0.001** -0.001** -0.001*** -0.000 -0.000 -0.001***
(-2.497) (-2.303) (-3.352) (-1.291) (-1.188) (-3.022)
StdROE 0.000** 0.000 0.000 0.000* 0.000 0.000
(2.161) (1.503) (1.293) (1.854) (1.211) (1.394)
Special Items 0.011** -0.002 0.005 0.017*** 0.003 0.008*
(2.203) (-0.423) (1.011) (3.289) (0.714) (1.649)
NumSeg -0.000 -0.000 -0.001*** -0.000 -0.000 -0.001**
(-0.455) (-0.735) (-2.811) (-0.829) (-1.113) (-2.423)
Restructuring -0.004*** -0.005*** -0.006*** -0.005*** -0.005*** -0.007***
(-6.425) (-6.959) (-9.263) (-7.249) (-7.724) (-9.997)
Acquisition -0.004*** -0.006*** -0.008*** -0.005*** -0.006*** -0.008***
(-6.445) (-8.453) (-12.160) (-7.884) (-9.824) (-12.674)
Leverage -0.022*** -0.018*** -0.020*** -0.023*** -0.018*** -0.020***
(-11.850) (-9.900) (-11.093) (-12.335) (-10.574) (-11.453)
HiLit 0.005*** 0.004*** 0.005*** 0.005*** 0.004** 0.005***
(4.577) (4.298) (4.605) (2.936) (2.478) (2.764)
BTM -0.004*** -0.003*** -0.004*** -0.004*** -0.003*** -0.003***
(-6.873) (-6.475) (-7.488) (-6.569) (-5.976) (-6.876)
ROA 0.003 0.002 0.001 0.001 0.000 0.000
(1.645) (1.065) (0.643) (0.597) (0.047) (0.075)
Loss -0.001 -0.000 -0.000 -0.001 -0.001 -0.001
(-0.633) (-0.224) (-0.455) (-1.467) (-1.021) (-1.017)
EPSInc 0.003*** 0.003*** 0.002*** 0.003*** 0.003*** 0.002***
(5.046) (5.058) (4.253) (4.470) (4.568) (3.903)
CAR 0.001 -0.000 -0.000 -0.000 -0.001 -0.000
(0.626) (-0.273) (-0.429) (-0.050) (-0.780) (-0.607)
IO 0.000 0.000 -0.001 -0.001 -0.001 -0.001
(0.143) (0.268) (-0.810) (-0.696) (-0.635) (-1.124)
Analysts 0.000* 0.000* 0.000** 0.000 0.000 0.000**
(1.885) (1.739) (2.246) (1.447) (1.216) (2.196)
Big4 -0.000 -0.000 -0.000 -0.000 0.000 -0.000
(-0.453) (-0.118) (-0.316) (-0.487) (0.131) (-0.038)
NewEquity 0.001 0.002* 0.001 0.002* 0.002** 0.001*
(1.469) (1.959) (1.577) (1.855) (2.232) (1.822)
NewDebt -0.002*** -0.002** -0.002** -0.002*** -0.002*** -0.002***
(-2.867) (-2.433) (-2.435) (-2.949) (-2.587) (-2.799)
Intercept 0.239*** 0.226*** 0.321*** 0.278*** 0.261*** 0.342***
(13.643) (15.485) (33.899) (17.640) (19.610) (39.487)
Year FE Yes Yes Yes Yes Yes Yes
Industry FE Yes Yes Yes Yes Yes Yes
Cluster SE? Firm Firm Firm Firm Firm Firm
Observations 14,170 14,079 14,079 14,327 14,234 14,234
Adjusted R2 0.758 0.755 0.613 0.768 0.764 0.621
43
Note: Table 5 tabulates the results of our competitive costs tests. In all models, we use DQ-based measures as
the dependent variable as in Table 4. We use two measures to capture the competitive costs that a firm faces:
product market fluidity and the Herfindahl-Hirshman index. Product market fluidity is constructed by Hoberg
et al. (2014) using computational linguistics, which measures the angle between a firmβs own product-related
word vector and the aggregate vocabulary change vector in the firmβs product market. High product market
fluidity indicates high product market threats and stronger product market competition. In Columns (1) to (3),
we create a dummy variable Hi_Fluidity equal to 1 if the firmβs product market fluidity is within the highest
decile in a given industry-year. We then interact Hi_Fluidity with our main variable of interest, GP.
The Herfindahl-Hirshman index is calculated as the sum of the market shares squared of all the firms in a 4-digit
SIC industry. A higher Herfindahl index indicates higher industry concentration and less intense industry
competition. In Columns (4) to (6), we create a dummy variable Hi_Comp that equals 1 if the firm is operating
in an industry with a Herfindahl index within the lowest quintile in a given year. We then interact Hi_Comp with
our main variable of interest, GP. Control variables are defined in Appendix A. We include year- and industry-
fixed effects in all specifications. t-statistics are provided in the parentheses. Standard errors are clustered by
firm. ***, ** and * indicate significance at the 1%, 5% and 10% levels, respectively.
44
TABLE 6: Analyst Coverage
Variables (1) DQt (2) DQCWt (3) DQEWt
GP 0.127** 0.123** 0.111** (2.177) (2.112) (1.995)
Red_Analysts -0.003** -0.003*** -0.003** (-2.245) (-2.620) (-2.550)
GP x Red_Analysts 0.042** 0.044** 0.043** (2.230) (2.523) (2.115)
DQt-1 0.662*** 0.685*** 0.612*** (71.813) (78.073) (71.804)
Size -0.000 -0.000 -0.001*** (-1.317) (-1.215) (-3.050)
StdROE 0.000* 0.000 0.000 (1.834) (1.185) (1.371)
Special Items 0.017*** 0.003 0.008* (3.301) (0.715) (1.656)
NumSeg -0.000 -0.000 -0.001** (-0.817) (-1.103) (-2.407)
Restructuring -0.005*** -0.005*** -0.007*** (-7.214) (-7.679) (-9.959)
Acquisition -0.005*** -0.007*** -0.008*** (-7.941) (-9.889) (-12.738)
Leverage -0.023*** -0.018*** -0.020*** (-12.306) (-10.500) (-11.380)
HiLit 0.004*** 0.003** 0.004*** (2.744) (2.292) (2.621)
BTM -0.004*** -0.003*** -0.003*** (-6.533) (-5.943) (-6.839)
ROA 0.001 -0.000 -0.000 (0.487) (-0.074) (-0.046)
Loss -0.001 -0.001 -0.001 (-1.426) (-0.978) (-0.979)
EPSInc 0.003*** 0.003*** 0.002*** (4.511) (4.614) (3.952)
CAR -0.000 -0.001 -0.001 (-0.161) (-0.914) (-0.743)
IO -0.001 -0.001 -0.001 (-0.626) (-0.572) (-1.056)
Analysts 0.000 0.000 0.000* (1.146) (0.861) (1.853)
Big4 -0.000 0.000 -0.000 (-0.460) (0.157) (-0.012)
NewEquity 0.002* 0.002** 0.001* (1.802) (2.166) (1.758)
NewDebt -0.002*** -0.002*** -0.002*** (-2.954) (-2.593) (-2.809)
Intercept 0.278*** 0.261*** 0.342*** (17.660) (19.639) (39.493)
Year FE Yes Yes Yes
Industry FE Yes Yes Yes
Cluster SE? Firm Firm Firm
Observations 14,327 14,234 14,234
Adjusted R2 0.768 0.764 0.621
45
Note: Table 6 tabulates the results of our analyst coverage tests. In all models, we use DQ-based measures as
the dependent variable as in Table 4. We calculate the change in a firmβs analysts following and create a
dummy variable equal to 1 if the firm experiences a decrease in analyst following of more than 50%. We then
interact the dummy variable with our main variable of interest, GP. Control variables are defined in Appendix
A. We include year- and industry- fixed effects in all specifications. t-statistics are provided in the parentheses.
Standard errors are clustered by firm. ***, ** and * indicate significance at the 1%, 5% and 10% levels,
respectively.
46
TABLE 7: Investor Attraction
Variables (1) DQt (2) DQCWt (3) DQEWt
GP 0.136** 0.138** 0.116**
(2.322) (2.351) (2.111)
Hi_Comove. 0.000 0.000 0.001
(0.239) (0.224) (0.925)
GP x Hi_Comove. 0.076* 0.082* 0.046
(1.737) (1.844) (0.931)
DQt-1 0.707*** 0.726*** 0.636***
(77.248) (83.628) (77.447)
Size -0.001** -0.001** -0.001***
(-2.481) (-2.307) (-3.404)
StdROE 0.000* 0.000 0.000
(1.913) (1.203) (1.216)
Special Items 0.011** -0.002 0.005
(1.982) (-0.331) (1.110)
NumSeg -0.000 -0.000 -0.001**
(-0.327) (-0.525) (-2.424)
Restructuring -0.004*** -0.005*** -0.006***
(-6.317) (-6.817) (-9.239)
Acquisition -0.004*** -0.005*** -0.007***
(-6.091) (-8.001) (-11.750)
Leverage -0.022*** -0.017*** -0.019***
(-11.695) (-9.629) (-10.808)
HiLit 0.005*** 0.004*** 0.005***
(4.369) (3.996) (4.493)
BTM -0.004*** -0.003*** -0.004***
(-7.085) (-6.312) (-7.167)
ROA 0.005** 0.003 0.002
(2.249) (1.431) (1.004)
Loss -0.000 -0.000 -0.000
(-0.548) (-0.318) (-0.496)
EPSInc 0.003*** 0.003*** 0.003***
(5.051) (5.088) (4.405)
CAR 0.000 -0.000 -0.000
(0.417) (-0.330) (-0.462)
IO -0.000 -0.000 -0.001
(-0.131) (-0.035) (-0.942)
Analysts 0.000* 0.000 0.000**
(1.771) (1.602) (2.203)
Big4 -0.000 -0.000 -0.000
(-0.312) (-0.001) (-0.168)
NewEquity 0.001 0.002** 0.001*
(1.601) (2.048) (1.738)
NewDebt -0.003*** -0.002*** -0.002***
(-3.179) (-2.747) (-2.712)
Intercept 0.224*** 0.215*** 0.318***
(15.538) (16.357) (36.330)
Year FE Yes Yes Yes
Industry FE Yes Yes Yes
Cluster SE? Firm Firm Firm
Observations 13,964 13,871 13,871
Adjusted R2 0.759 0.756 0.613
47
Note: Table 7 tabulates the results of our investor attraction tests. In all models, we use DQ-based measures as
the dependent variable as in Table 4. We calculate a modified stock price synchronicity measure as in Park,
Schrand and Zhou (2019). We first regress a firmβs stock return on the past, contemporaneous and future
value-weighted 2-digit SIC industry returns by firm-year as follows:
π ππ‘π,π‘ = π½0 + π½1πΌππ_π ππ‘π,π‘β1 + π½2πΌππ_π ππ‘π,π‘ + π½3πΌππ_π ππ‘π,π‘+1 + νπ,π‘
Then, we take the adjusted R-squared of each firm-year regression and calculate a stock price co-movement
measure following Park et al. (2019). The measure is calculated as follows:
πΆππππ£π = log (1 +π΄ππ. π 2
1 β π΄ππ. π 2)
We create a dummy variable equal to 1 if the firmβs stock price co-movement measure is within the top decile
in a given industry-year. We then interact the dummy variable with our main variable of interest, GP. Control
variables are defined in Appendix A. We include year- and industry- fixed effects in all specifications. t-
statistics are provided in the parentheses. Standard errors are clustered by firm. ***, ** and * indicate significance
at the 1%, 5% and 10% levels, respectively.