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The Effect of Market Transparency on Corporate Disclosure
by
Georg Rickmann
S.M. Management Research, MIT Sloan, 2020
MSc. Finance, Warwick Business School, 2013
BSc. Business Administration, University of Göttingen, 2012
SUBMITTED TO THE SLOAN SCHOOL OF MANAGEMENT IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY IN MANAGEMENT
at the
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
SEPTEMBER 2020
©2020 Massachusetts Institute of Technology. All rights reserved.
Signature of Author:__________________________________________________________ Department of Management
August 6, 2020 Certified by: ________________________________________________________________
Eric So Sarofim Family Career Development Associate Professor
Associate Professor, Accounting
Certified by: ________________________________________________________________ Rodrigo Verdi
Nanyang Technological University Professor of Accounting Professor, Accounting
Accepted by: _______________________________________________________________
Catherine Tucker Sloan Distinguished Professor of Management
Professor, Marketing Faculty Chair, MIT Sloan PhD Program
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The Effect of Market Transparency on Corporate Disclosure
by
Georg A. Rickmann
Submitted to the Sloan School of Management on August 6, 2020, in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Management
Abstract:
Market prices and trading in financial markets are important information signals that reveal firm-specific information to the public. I study how the observability of such prices and trading (hereafter, “market transparency”) affects firms’ disclosure incentives. I exploit the staggered introduction of TRACE, which made bond prices and transactions publicly observable, and show that firms provide more guidance when their bonds’ prices and trading become observable. This effect is stronger for firms with informationally sensitive bonds and firms without exchange-listed bonds prior to TRACE. Also, firms become particularly more likely to disclose bad news, consistent with the notion that investors’ access to market information limits managers’ incentives to withhold information. I corroborate my results using (1) a small controlled experiment, in which prices and trading are revealed for a randomized set of bonds, and (2) threshold rules used by the regulator. Taken together, my results suggest that observable market outcomes inform investors not only directly, by aggregating and revealing investors’ information and beliefs, but also indirectly by increasing corporate disclosure.
Thesis Supervisor: Eric So Title: Sarofim Family Career Development Associate Professor, Associate Professor of Accounting
Thesis Supervisor: Rodrigo Verdi Title: Nanyang Technological University Professor of Accounting, Professor of Accounting
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1 Introduction
I study how the observability of market prices and trading (hereafter “market transparency”)
affects firms’ incentives to disclose information. My study is motivated by the idea that market
prices and trading are important information signals. They aggregate and summarize investors’
private information and, if observable, reveal this information to the broad public (e.g., Kyle, 1985;
Glosten and Milgrom, 1985). In transparent markets, in which prices and trading are broadly
observable, investors can learn from market outcomes about changes in firm value, firm risk, or
the probability of underlying value-relevant events (e.g., not-yet-announced M&A decisions or
changes in firms’ investment strategies). Ultimately, investors have better access to firm-specific
information in transparent markets, which changes how they interpret and react to managers’
disclosure decisions. How managers, in turn, alter their disclosure behavior is the focus of this
paper.
Economic theory suggests that access to information alters market participants’
interpretation of both disclosures and non-disclosures, and therefore plays a key role in managers’
disclosure decisions (Verrecchia, 2001; Dye, 2001; Beyer, Cohen, Lys and Walther, 2010).
However, whether increased access to market information increases or decreases managers’
incentives to disclose their information is theoretically ambiguous. On the one hand, increased
access to market information can lead to increased disclosure for at least two reasons. First,
observable trading and returns help investors assess whether a material event occurred, which
spurs increased demand for apparently informed managers to disclose their material information.1
Second, observable price and transaction data are central inputs to securities litigation. They help
investors assess investment losses, attribute these losses to particular revelations, and coordinate
1 This argument is a natural extension of models such as Dye (1985) or Jung and Kwon (1988), in which uncertainty about whether managers have material information prevents full disclosure.
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class action lawsuits.2 Thus, the costs of withholding or delaying information are likely higher in
transparent markets, resulting in greater disclosure of material information (Skinner, 1994, 1997).
On the other hand, investors’ access to market prices/trading may reduce managers’
incentives to disclose information. For example, market data reduce information asymmetries
about firm value, which can reduce investors’ demand for disclosure and the equilibrium
disclosure level (e.g., Verrecchia, 1983, 1990; Diamond and Verrecchia, 1991).3 Ultimately, the
net effect of these channels is an empirical question, which I explore in this paper.
A major challenge when testing predictions about the effect of market transparency on
disclosure is finding a setting that elicits exogenous variation in market transparency. I overcome
this challenge by studying the introduction of the Trade Reporting and Compliance Engine
(TRACE), which generates plausibly exogenous variation in bond market transparency. In
essence, TRACE makes previously unobservable trading and the resulting prices observable to
market participants. Corporate bonds are generally traded over the counter (OTC), and prior to
TRACE, transaction information such as realized prices and volumes was largely private
information exclusive to the transacting parties. TRACE fundamentally changed this exclusivity
by requiring dealers to report their transactions (e.g., price, trade size, and timing), which are then
disseminated to the public. Since TRACE’s full implementation, investors have almost-real-time
access to more than 99% of the total activity in US corporate bonds (FINRA, 2007).
2 Also, observable market data help plaintiffs demonstrate that the firm’s bond market is sufficiently efficient to apply the fraud-on-the-market principle. The fraud-on-the market principle lifts the requirement that plaintiffs demonstrate direct reliance on a particular disclosure or non-disclosure (because investors indirectly relied on it through prices). 3 The expression “demand for disclosure” can be confusing because there is no market for disclosure, in which disclosure is sold for money and the price sets demand equal to supply. In many accounting theories, equilibrium is achieved in the security market, and “demand,” or “pressure,” for disclosure formalizes as a price discount investors rationally impose in the absence of disclosure (e.g., Verrecchia, 1983; Dye, 1985; Jung and Kwon, 1988, Verrecchia, 1990).
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In addition to generating large-sample variation in the observability of prices and trading,
the introduction of TRACE offers three main empirical advantages. First, TRACE dissemination
was introduced in four main phases (in 2002, 2003, 2004, and 2005). This staggering allows me
to compare firms whose bonds become observable with control firms whose bond observability
does not change at that time. Also, I can restrict this comparison to public firms with public debt,
reducing selection concerns stemming from differential reporting requirements or the endogenous
decisions to issue bonds or to become/stay public. Second, the National Association of Securities
Dealers (NASD) conducted a small randomized controlled experiment, in which prices and trading
of 120 randomly selected bonds were revealed through TRACE. I use this experiment to
corroborate my large-sample inferences. Finally, I also exploit the threshold rules regulators used
to assign bonds to phases and how they translate to the firm level. I describe the TRACE
introduction in more detail in Section 2.
Using the introduction of TRACE as an empirical setting, I test three predictions. First, I test
whether and how much TRACE alters managers’ disclosure. Second, I test whether TRACE has a
larger effect in situations in which the bond market outcomes contain more incremental
information. Finally, I test the prediction that, to the extent managers prefer to withhold bad news,
TRACE has a larger effect on bad news disclosures by reducing managers’ incentives to withhold
information.
In my main analysis, I study firms’ voluntary disclosure mainly in terms of their use of
guidance forecasts. Guidance forecasts reflect firms’ projections of key financial statement
parameters – including earnings, sales, and capital expenditures – and are an important source of
information for investors.4 My sample consists of 13,206 firm-quarters between July 2001 (one
4 For example, Beyer et al. (2010) estimate that management forecasts provide more than half of firms’ accounting-
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year before the first phase introduction) and February 2006 (one year after the last phase
introduction). I define a firm-quarter as having observable bond pricing/trading (i.e., as being
“treated”) if the firm has a bond that has started dissemination through TRACE.
My main finding is that firms increase their disclosure in response to increased market
transparency. Using difference-in-differences regressions that exploit the staggered nature of the
TRACE introduction, I estimate that firms provide 0.30 more forecasts per quarter when their debt
trading/prices become observable, which corresponds to a 19% increase relative to the
unconditional mean of the quarterly forecast frequency. This increase in the disclosure frequency
is attributable to firms forecasting (i) more topics (e.g., CapEx in addition to earnings), (ii) more
horizons (e.g., multiple fiscal years), and (iii) on more dates. The absolute return reactions to the
disclosures increase, suggesting that managers provide not only more disclosure but also more
informative disclosure. Moreover, the increased information disclosure appears not to be limited
to management forecasts: using the disclosure quality measure by Chen, Miao and Shevlin (2015),
I find firms significantly improve the quality of their financial statements following the
introduction of TRACE.5 Overall, these results are consistent with theory predicting that
improving investors’ access to market information limits managers’ incentives to withhold
information.
To sharpen my inferences, I study the timing of the disclosure effect. I find that disclosure
remains flat before the TRACE introduction, jumps as prices and trading become observable, and
remains high thereafter. Because a firm’s market transparency increases discontinuously around
based information to investors. 5 Moreover, I use as a third measure the forward-looking-statements measure in Bozanic et al. (2018).
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its phase introduction date, this pattern suggests that the increase in disclosure is attributable to
TRACE rather than to confounding effects.
Moreover, I examine the phase introductions separately and I estimate for each of the four
introductions that firms provide increased disclosure after their prices/trading become observable
through TRACE (significant for all phases but the first). This consistency further suggests that the
disclosure increase is attributable to the introduction of TRACE rather than to confounding factors.
My second prediction is that TRACE has a larger effect on disclosure when the revealed
price and trading data likely contain more information. I find that TRACE has a substantially
smaller effect on firms with exchange-listed bonds, which is intuitive because investors could
observe price quotes prior to TRACE. I also find that TRACE has a larger effect on firms with
high credit risk, which is also intuitive because the informational sensitivity of debt increases with
the firm’s credit risk (Merton, 1974). These results are consistent with the idea that the increased
disclosure is driven by the revealed information content of prices and trading.
My third prediction is that the increased disclosure more likely reflects bad news because
TRACE reduces managers' incentives to withhold news. Consistent with this prediction, I find a
significant increase in the forecast frequency of bad, but not good, news, where I use the
announcement return to classify the content of announcements. Similarly, I also find that the
average return reaction to the guidance announcements becomes more negative as firms’
prices/trading become observable.
A potential disadvantage of the staggered TRACE introduction is that firms were not
randomized into treatment groups. Instead, assignment to a TRACE phase was based on bonds’
ratings and issue sizes, with earlier phases tending to contain larger and better-rated bonds (see
Section 2 for a detailed description). Firms in different treatment groups thus differ in their
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associated characteristics, most notably their (public) debt amounts and creditworthiness, which
raises the concern that disclosures from the treated and control firms might change differently
around the TRACE introduction dates due to confounding factors rather than the TRACE
introduction itself.
To address these concerns about confounding effects, I corroborate my results using a small
randomized controlled experiment in which NASD randomly chose 120 BBB-rated bonds and
started to make their transactions available through TRACE. Using this experiment, I find
corroborating evidence that firms increase their management forecast frequency in response to
their debt prices/trading becoming observable, again particularly for bad news disclosure.
I also exploit that the threshold rules used to assign bonds’ treatment sometimes lead to
discrepancies in the treatment of otherwise-similar firms. For example, a firm receives treatment
in Phase 1 if it has one investment-grade bond with an issue size of $1.2 billion, but not if it has
two investment-grade bonds with issue sizes of $600 million each. This discrepancy in the
treatment group assignment allows me to hold constant the total issue size of a firm’s investment
grade bonds, which is the firm-level equivalent of the variable used to assign bonds’ treatment. I
exploit similar cutoff rules for Phases 2 and 3A. Holding explicitly constant the total issue-size-
rating combination used to assign treatment to bonds, I corroborate the result that market
transparency increases disclosure. The treatment effect estimates based on (i) this threshold-based
analysis, (ii) the randomized experiment, and (iii) the staggered main introduction are similar in
magnitude, ranging from 0.30 to 0.36 forecasts per quarter.
Finally, I examine potential channels through which TRACE spurred firms’ disclosure. I
find evidence for two non-exclusive informational channels: observable market data plausibly
increase disclosure by (i) increasing the litigation costs associated with withholding information
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and (ii) reducing uncertainty about whether firms have material information about material events.
I also test and find evidence against the alternative that disclosure increases because TRACE
increases firms’ bond issuance and related public disclosure.
My article makes two contributions. First, my results help evaluate the consequences of the
introduction of TRACE, an important regulatory change aimed to increase price transparency in
the corporate bond market. My findings highlight externalities of market transparency and have
practical implications for regulators concerned with the transparency of financial markets.
Understanding the consequences of mandated market transparency is important, in part, because
of increased regulatory efforts with regard to market transparency.6 My results suggest that
increased market transparency improves investors’ access to corporate information not only
directly, by revealing the information contained in prices/trading, but also indirectly by increasing
corporate disclosure. My findings are thus relevant for regulators who are concerned with
investors’ access to information, and who regard both market transparency and disclosure as key
drivers for long-term growth (e.g., the SEC does so).7
Second, my article extends the literature on managers’ disclosure incentives by studying how
withholding incentives are mitigated by investors’ access to market prices and trading information.
Prior literature already suggests that managers have incentives to withhold information (e.g.,
Kothari et al., 2009), and my results suggest that when investors can learn from prices and trading,
managers reveal more information, and the additional disclosure tends to contain bad news.
6 The increased regulatory efforts are reflected in recent successfully implemented regulations that follow TRACE’s example, such as the introduction of real-time public reporting for all swap transactions (e.g., credit default swaps and industry swaps) in 2012 and the introduction of MiFID II/R in Europe in 2018. 7 To quote Arthur Levitt, then Chairman of the SEC, “Transparency, disclosure and accountability aren’t just catchwords. They are the essential ingredients to confidence. And without it, markets can neither sustain long-term growth nor adapt to a rapidly changing environment.” His speech can be found at https://www.sec.gov/news/speech/speecharchive/1998/spch218.htm.
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My article is special in that I focus on the observability of prices and trading, which reveal
changes in the market’s aggregate information (the “wisdom of the crowds”). The importance of
these information constructs is evidenced by their ubiquitous use to learn about different aspects
of the firms (e.g., academics ubiquitously use returns in their tests to learn about different aspects
or changes in firms). However, despite their importance as information constructs, evidence on the
consequences of observable market prices and trading is limited, arguably due to the difficulty of
observing even remotely-exogenous variation in the observability of prices/trading. My paper
contributes to filling this gap by studying the impact of observable market returns and transactions
on managers’ disclosure incentives.8 My study is unique in that my setting plausibly allows me to
isolate the effect of the observability of bond prices and transactions. This allows me to focus on
the informational aspect of prices and trading, and also to draw plausibly causal inferences about
the direction and strength of the studied effects.
Taken together, my study extends the emerging literature on how external market forces
shape managers’ disclosure incentives (e.g., Sletten, 2012; Balakrishnan, Billings, Kelly and
Ljungqvist, 2014; Hu, 2017; Kim, Shroff, Vyas and Wittenberg-Moerman, 2018) and also the
literature relating investors’ uncertainty and management forecasts (e.g., Waymire, 1985 and
Bozanic et al., 2018). Section 3.3 contains discussions of these papers.
2 Description of the TRACE Introduction
The Transaction Reporting and Compliance Engine (TRACE) was introduced between 2002
and 2005 to increase price transparency in the corporate debt market (FINRA, 2018). Before
8 To the best of my knowledge, my paper is the first to study is the first to study the impact of the observability of market returns/trading on corporate disclosure. Conceptually, the closest paper I am aware of is Hu (2016), which studies a market-wide change in 2007 in the frequency/delay at which exchanges report open short-interest (from once per month to twice per month). See Section 3.3 for a differentiation from Hu (2016).
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TRACE’s introduction, corporate bond markets were opaque with low levels of pre- and post-trade
transparency. Corporate bonds are traded primarily over the counter, and prior to TRACE,
transaction parties were not required to make their trading or the realized market prices public.
Realized market outcomes were generally private information of the transaction parties and not
broadly shared. Pre-trade quotations were available only to market professionals, most often by
telephone (Bessembinder and Maxwell, 2008). In addition, institutions could consult a vendor
providing estimated prices (so-called “matrix prices”) (Asquith, Covert and Pathak, 2013). To
quote Arthur Levitt, then Chairman of the SEC, “the sad truth is that investors in the corporate
bond market do not enjoy the same access to information as a car buyer or a homebuyer or, dare I
say, a fruit buyer.”9
The introduction of TRACE substantially increased transparency in the corporate bond
market. For each transaction in TRACE-eligible securities, dealers are required to report to
TRACE the bond identifier, date and time of the transaction, the trade size, trade price and yield,
and whether the dealer bought or sold the bond.10,11 TRACE then releases to the public the price
and yield, the trade size, and the date and time of execution.12 After full implementation, investors
could access information on 100% of OTC activity, which represents more than 99% of the total
activity in US corporate bonds (FINRA, 2007). Investors could access the data free of charge on
the NASD website or through third-party vendors, such as Bloomberg, Reuters, or MarketAxess.
According to a Wall Street Journal Article, TRACE was seen as “a major step forward in the
9 His speech can be found at https://www.sec.gov/news/speech/speecharchive/1998/spch218.htm. 10 A “TRACE-eligible security” is a debt security that is US-dollar denominated and is issued by a US or foreign private issuer, and registered under the Securities Act; or issued pursuant to Section 4(2) of the Securities Act and purchased or sold pursuant to Securities Act Rule 144A. See FINRA Rule 6710 for a more information. 11 Dealers are required to report their trades to FINRA in a timely fashion. Starting from 2002, dealers were required to report their trades to TRACE within 75 minutes. This window was reduced to 45 minutes (on October 1, 2003), 30 minutes (October 1, 2004), 15 minutes (July 1, 2005), and finally to immediate reporting (January 9, 2006). 12 The disseminated trade size is capped at $5 million for investment-grade bonds and at $1 million for junk bonds.
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evolution of the corporate bond market, along the lines of the stock-market tickers in the early 20th
century.” 13
The timeline for the introduction of TRACE starts in 1998, when the SEC reviewed the
transparency of US debt markets. After this review, the SEC asked NASD, a predecessor
organization to the Financial Industry Regulatory Authority (FINRA), to take three steps to address
the lack of bond market transparency: (i) adopt rules requiring dealers to report their transactions
and develop systems that receive and distribute the transaction prices on an immediate basis; (ii)
create a database of transactions in corporate bonds, which would enable regulators to proactively
supervise the corporate debt market; and (iii) in conjunction with the development of a database,
create a surveillance program to better detect fraud in order to foster investor confidence and the
fairness of these markets. In January 2001, the SEC approved the TRACE rules, and in July 2002,
NASD formally introduced TRACE.
Starting from July 1, 2002, dealers had to report their transactions to TRACE; however, the
dissemination (i.e., observability) of this information to the public was introduced in four main
“phases.” (in 2002, 2003, 2004, and 2005). Figure 1 and Table 1 summarize the phases’
introduction dates and the requirements for bonds to be included in a phase. Phase 1 started on
July 1, 2002, and included investment-grade bonds with an initial issue size of at least $1 billion.14
Phase 2 began on March 3, 2003, expanding dissemination to the next-largest bonds (initial
issue size $100 million) with a rating of at least A. In addition, NASD conducted a randomized
controlled experiment, selecting 120 BBB bonds with issue sizes between $10 million and $1
13 In Major Shift, NASD Expands Corporate Bond-Price Reports. https://www.wsj.com/articles/SB1046714678626958720?mod=searchresults&page=1&pos=1. 14 In addition to the Phase 1 bonds, 50 non-investment-grade bonds that previously had information released under the Fixed Income Pricing System (FIPS) were transferred to TRACE. I do not use these “TRACE 50” bonds for my treatment group assignment because the list changed frequently and information about some bonds was available for only a short while. According to FINRA employees, the list changed on a quarterly basis.
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billion and started to disseminate these bonds’ information on April 14, 2003, as part of Phase 2.
In Section 6, I describe this experiment in more detail and use it to corroborate the inferences from
my main tests.
Phase 3 expanded TRACE to cover almost all bonds and was introduced in two parts. Phase
3A was introduced on October 1, 2004, and expanded the TRACE system to bonds that are “not
eligible for delayed dissemination.” Phase 3B was introduced on February 7, 2005, for bonds that
are “eligible for delayed dissemination.” Practically speaking, bonds eligible for “delayed
dissemination” are junk bonds.15 I refer the readers to Asquith et al. (2013) and Bessembinder and
Maxwell (2008) for excellent, more extensive descriptions of the TRACE introduction.
3 Bond Informativeness and Hypothesis Development
3.1 Informativeness of Bond Prices and Trading
My study builds on the idea that the observability of bond market trading and prices adds to
the firm's information environment. Informed investors reveal their private information through
trading (Grossman and Stiglitz, 1980; Kyle, 1985; Glosten and Milgrom, 1985), and TRACE
makes this trading and the resulting prices observable. As a consequence, not only do the
transaction parties learn about the private information underlying the transactions, but so does the
broader public. Ultimately, investors “share” their otherwise private information in transparent
markets.
The corporate bond market is dominated by large, sophisticated institutions with excellent
access to information (e.g., Bessembinder, Kahle and Maxwell, 2009; Even-Tov, 2017). These
15 Bonds with a rating of BB or worse were eligible for delayed dissemination: If a BB-rated [B-or-worse-rated] bond trades less than once per day on average, transactions are made available after two [four] business days. In addition, dissemination was delayed for newly issued bonds with a rating of BBB or below: for BBB-rated [BB-or-worse-rated] bonds, transactions within the first two [ten] days after pricing were released on the third [eleventh] day. On January 9, 2006, TRACE stopped the delayed dissemination.
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institutions spend significant resources on information acquisition, which their trades then reflect.
Back and Crotty (2015) find that price discovery in bond markets spills over to equity markets,
and vice versa.16 Other articles document informed trading and price adjustments prior to
economic news events, such as acquisition announcements, earnings announcements, or class
action lawsuits (Wei and Zhou, 2016; Kedia and Zhou, 2014; Billings and Klein, 2011).
Security prices reflect information about the issuing firm. Because a security’s value is
driven by changes in firm value and risk, it thus reflects changes in (i) firm value and (i) firm risk
(Merton, 1974).17 Moreover, because the changes of firm values and risk are driven by underlying
economic actions and events, security returns reflect (iii) the presence/probability of such
actions/events.
The information content of observable bond prices/trading is incremental to that of equity
prices/trading, and vice versa. Observing both signals, equity and debt returns, allows investors to
learn incrementally about changes in firm value, firm risk, and the probability of underlying value-
relevant events. The arguably most important conceptual reason for this is that equity and debt
have different payoff profiles as a function of firm value/performance and thus reflect the same
underlying information set (or event) differently. A notable implication of this differential
reflection of information is that the debt signal reveals additional information about the firm even
if the equity signal were “perfect” in the sense that (i) equity is perfectly efficient, (ii) equity
contains no noise, and (iii) all information debt investors incorporate into debt prices is also
incorporated into equity prices.
16 Back and Crotty (2015) extend the Kyle (1985) model to allow for cross-market spillovers (from bond order flow to equity prices, and vice versa). They infer that price discovery occurs in both the bond and equity markets, and that it is not dominated by either market with respect to the information conveyed by order flows. 17 More generally, a security’s value depends on the risk-neutral distribution of a firm’s payoff. In the Merton model, this distribution is summarized by value (mean) and risk (variance).
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To better illustrate this point, consider the example of Klein and Zur (2011) who use debt
returns in addition to equity returns to learn how hedge fund activism affects firms. They find that
hedge fund activism increases equity value but decreases debt value, consistent with wealth
transfers from debt to equityholders. Observing the debt signal in addition to the equity signal
allowed them to better learn about at least three aspects of the firm. First, because the firm consists
of equity and debt, observing both equity and debt returns allows for better assessment of the firm
returns (i.e., of how the “size of the pie” changes). Second, observing equity and debt returns
suggests how the “allocation of the pie” changes, and thereby how firm risk changes (or
expectations of other wealth shifting events). Finally, changes of firm value and risk reflect value-
relevant actions and events and are thus informative about the presence/probability of such actions
and events. For example, in Klein and Zur (2011) the return profile suggests wealth shifting
events/actions. When the authors test for such events, they indeed find subsequent increases in
firm risk, dividend payouts, and debt issuance.
There are at least three more reasons why the information content of bonds is not subsumed
by that of equity, and vice versa. First, subsets of equity and bond investors are specialized and
trade on their private information only in either the equity or debt market. Therefore, equity and
bond prices incorporate different pieces of private information, and observing both “signals” is
more informative than observing only one. Consistent with this argument, Back and Crotty (2015)
find that price discovery occurs in both the bond and equity markets, and Badoer and Demiroglu
(2019) provide evidence that equity prices appear to incorporate more information from debt
markets after TRACE and thus react less negatively to downgrades.18 Second, equity and bond
prices are not perfectly efficient and may thus incorporate events incorrectly. For example, Even-
18 Badoer and Demiroglu also suggest that post-TRACE, credit ratings become more sensitive to bond spread changes.
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Tov (2017) and Bittlingmayer and Moser (2014) use bond returns to predict mispricing in stocks,
meaning bonds contain information that should be, but is not, contained in equity prices. Finally,
both equity and bond prices are noisy, and access to both signals thus leads to more precise
posteriors (e.g., Holmstrom, 1979).
3.2 Hypothesis Development
Managers often have incentives to withhold information from investors. Economic theory
suggests that investors’ access to alternative information is a key determinant of managers’
disclosure decisions, because it determines how investors interpret and react to disclosure (e.g.,
Grossman, 1981; Dye, 1985; Skinner, 1997; and Verrecchia, 1983). For example, the unraveling
result by Grossman (1981) shows that if investors know with certainty whether managers possess
value-relevant information, then informed managers fully reveal their information.19 In contrast,
if investors are uncertain about managers’ information endowments, managers withhold
sufficiently unfavorable news (Dye, 1985; Jung & Kwon, 1988).
There are at least three non-exclusive reasons for increased market transparency to lead to
increased disclosure. First, the litigation and reputational costs associated with withholding
information likely increase when prices and trading become observable. Observable transaction
prices and volumes are central inputs to securities litigation and assist potential plaintiffs in (i)
assessing whether managers had information, (ii) quantifying investment losses stemming from
their reliance on firms' disclosures and non-disclosures, (iii) tying declines in investors’ wealth to
a particular disclosure or revelation, and (iv) coordinating class actions (Park, 2014). Also,
observable market data help plaintiffs demonstrate that the firm’s bond market is sufficiently
efficient to apply the fraud-on-the-market principle. The fraud-on-the market principle lifts the
19 See Beyer et al. (2010) for a discussion of the assumptions underlying the unraveling result.
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requirement that plaintiffs demonstrate direct reliance on a particular disclosure or non-disclosure
(because investors indirectly relied on it through efficient prices) and facilitates the certification
of a class (Park, 2014). In total, TRACE likely increases the threat of litigation, particularly from
bond investors, such that the expected litigation costs associated with withholding information are
higher, which increases managers’ incentives to reveal their information (Skinner, 1994, 1997).20
Second, abnormal trading and returns suggest whether something material occurred in the
firm and thus whether managers have material information. For example, prior literature
documents abnormal trading and price swings before major, not-yet-announced corporate news
(e.g., Kedia and Zhou, 2014; Wei and Zhou, 2016). Investors demand clarification/disclosure
accordingly, such that informed managers ultimately tend to face increased demand for disclosure
and are more likely to disclose. This argument can be seen as a natural extension of Dye (1985)
and Jung & Kwon (1988), who show that uncertainty about the manager’s information endowment
limits disclosure.21 Note that in the Dye and Jung & Kwon models, investors’ “demand” for
disclosure formalizes as a price discount investors apply in the absence of disclosure.
Finally, increased market transparency might increase disclosure by reducing the
proprietary information cost associated with it. To the degree that third parties (competitors,
regulators, labor) extract proprietary information from prices and trading that they would otherwise
extract from disclosures, the proprietary information costs of disclosure decrease, and firms reveal
more information (e.g., Verrecchia, 1983).
20 The threat of bondholder litigation is economically meaningful. Park (2014) finds that 8% of all settlements between 2001 and 2005 include bondholder recoveries. 21 For illustration, suppose the market signals fully reveal whether managers have value-relevant information. In that case, the conditions of the unraveling result are satisfied whenever managers are informed, and these managers thus fully reveal their information.
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There are also arguments why increased market transparency can lead to reduced
disclosure. For example, increased market transparency reduces information asymmetries about
firm value between the manager and investors, which can reduce investors’ demand for
information and equilibrium disclosure (e.g., Verrecchia, 1983, 1990).22 Also, broad access to
market data likely reduces information asymmetry among investors, which may limit the extent to
which disclosure incrementally reduces adverse selection and the cost of capital, and thereby
reduce managers’ disclosure incentives (Diamond and Verrecchia, 1991).
In sum, my main hypothesis is that market prices/returns and trading, by revealing
information to market participants, change managers’ equilibrium incentives to disclose
information. However, it is ex-ante not obvious whether managers disclosure incentives will
increase or decrease. Second, I predict a larger effect in situations in which the revealed market
prices and trading contain more new information. Third, to the extent that managers prefer to
withhold bad news, I predict that TRACE particularly affects bad news disclosure.
3.3 Relevance of Corporate Disclosures for Debt and Equity Investors
Generally, corporations’ most important disclosures reveal predominantly information
about the firm (e.g., about the firm’s earnings, sales, or capex). Prior literature often links such
corporate disclosure to equity prices, which makes sense because equity value is driven by news
about the firm.23 However, this relevance for equity investors should not be misinterpreted as
implying irrelevance for debt investors. In fact, Merton (1974) shows that the value of debt is
driven by (i) firm value and (ii) firm risk. This implies that debt prices are driven by news about
22 In Verrecchia (1983, 1990) increased market transparency reduces disclosure to the degree that the information asymmetries about firm/equity value decrease relative to the cost of disclosure. 23 Exceptional papers linking such disclosures to debt prices include Easton, Monahan, and Vasvari (2011), Shivakumar, Urcan, Vasvari and Zhang (2011), and Lok and Richardson (2011).
19
the firm. Ultimately, Corporate disclosures (e.g., about the firm’s earnings/performance, growth,
or strategy) are therefore of crucial importance to debtholders, by the same logic that makes them
important to equityholders.
3.4 Other Related Literature
The introduction of TRACE has been studied primarily by the market microstructure
literature. This literature suggests that transaction costs decrease for bonds that become observable
through TRACE (e.g., Bessembinder, Maxwell and Venkataraman, 2006; Goldstein, Hotchkiss
and Sirri, 2007). At the same time, however, the literature generally finds no increase in trading
activity. For example, Goldstein et al. (2007) find no effect on trading volume, and Asquith et al.
(2019) find that trading activity does not increase, and by one measure decreases. In my sample, I
test how TRACE dissemination affects firms’ average turnover of bonds and find no significant
effect.
Apart from the aforementioned research, Sletten (2012), Balakrishnan et al. (2014), Zuo
(2016), Hu (2017), and Kim et al. (2018) are likely the papers most closely related to mine. Of
these papers, only Kim et al. (2018) study a debt market setting. The paper provides evidence that
the initiation of CDS trading increases corporate disclosure; however, the paper’s focus is on CDS
insurance itself, not the observability of prices and trading.24
Sletten (2012) studies the disclosure reaction to stock price drops. In contrast to my paper,
(i) her focus is on ex-post disclosure as a function of ex-post price drops and (ii) prices appear to
have no informational role but are assumed to move independently of value. Zuo (2016) shows
24 CDS trading represents primarily an economic action: the transfer of cash flow rights, which leads to the empty creditor problem and reduces monitoring by debtholders. In contrast, TRACE dissemination represents the observability of economic events/actions (or of their absence): for both treatment and control firms, bond trading takes place and is reported to TRACE. For treated firms, however, this information is then made observable.
20
that stock returns tend to be more positively associated with managers’ subsequent earnings
revisions when there is more informed trading, which he interprets as managers revising their
earnings expectations more after observing more informative stock prices. Zuo (2016) differs from
my paper because he studies neither market transparency nor the amount of information disclosure.
In Balakrishnan et al. (2014) studies how managers’ disclosure affects liquidity. The study
is related because it involves studying managers response to reduced retail analyst coverage (due
to brokerage closures/mergers), and finds that this reduction leads to an increase in earnings-
related management forecasts. The authors interpret their results as managers aiming to reduce
information asymmetries between retail investors and institutional investors. Another closely-
related paper is Hu (2017), which studies how an increase in the frequency at which exchanges
release daily open short interest, from once per month (only mid-month) to twice (mid-month and
end-of-month) affects the relative disclosure probabilities around the mid- and end-of-month dates.
My paper differs from Hu’s because I study the effect of making otherwise-unobservable realized
bond prices and trading observable, while Hu studies the latency/frequency at which exchanges
release open short interest. Also, I focus on the total amount of management forecasts, while Hu’s
evidence on the probability of end-of-month disclosure relative to the probability of mid-month
disclosure speaks best to the strategic timing of disclosures around predictable public information
releases.
My study is also related to the literature on the relation between mandatory and voluntary
disclosure are related (e.g., Li and Yang, 2016; Noh, So and Weber, 2019). The high-level question
in this literature is how mandating firms to disclose more information via one disclosure form (e.g.,
through financial statements or 8-K filings) alters their voluntary disclosure via an alternative
disclosure form (e.g., management forecasts). The literature provides mixed evidence on whether
21
voluntary and mandatory disclosure are substitutes or complements: for example, Li and Yang
(2016) estimate that firms provide more earnings forecasts after switching to IFRS, while Noh et
al. (2019) estimate that firms provide fewer management forecasts after being required to disclose
more information through 8-Ks.
My study is related to this literature because, at the highest level, the firm’s own (mandatory)
disclosure also reveals information about the firm. However, I believe that market prices and
trading differ fundamentally from mandatory disclosure in important ways. First, market returns
are formed outside of the firm and beyond the manager’s direct control. Instead, they reflect
changes in investors’ aggregate information and beliefs (“wisdom of the crowds”). Investors with
value-relevant information reveal this information “truthfully” because they make profits [losses]
from trading in the right [wrong] direction.
Second, mandatory disclosure and market returns map differently into managers’ voluntary
disclosure incentives. For example, if a manager has already disclosed a piece of information
through an IFRS financial statement or an 8-K financial statement, then she has no obligation (or
even reason) to disclose this information again. Correspondingly, investors’ demand/pressure for
disclosure is different (e.g., there is no threat of litigation entailed by not disclosing the information
again).
Finally, the nature of the revealed information differs. Market returns and trading reflect new
forward-looking information, whereas financial statements reflect largely backward-looking
information, much of which is already incorporated in market prices (e.g., Kothari, 2001).
Relatedly, market returns reflect a broader set of information but do so less specifically. For
example, stark price swings or abnormal trading may suggest something material is happening in
the firm, but not what exactly happens, which spurs different demand/pressure for disclosure.
22
Overall, I believe that one cannot generalize from the mandated disclosure literature to my
research question. This is in particular the case because the literature provides conflicting evidence
on whether mandatory disclosure leads to increased or decreased voluntary disclosure.
My study also builds on previous studies relating disclosure to uncertainty (e.g., Waymire
(1985) and Bozanic et al. (2018)). Also quote studies finding increase. My study builds on this
literature in at least two ways. First, my study focuses explicitly on differences in the information
about the firm as opposed to differences in the firm’s fundamentals. Most measures of investor
uncertainty measure (e.g., volatility of the firm’s earnings process) capture particularly differences
in the firm’s fundamentals. Second, as pointed out in Healy (2001), interpreting the findings in
studies that rely on simple associations is difficult because of significant endogeneity and
measurement error problems. My study plausibly overcomes this challenge by studying plausibly
exogenous variation in the observability of the market signal.
4 Sample
I use data from the following sources. FINRA gave me lists of the bonds introduced in each
phase and also the 120 randomly chosen treatment bonds used in the FINRA 120 experiment. I
obtain data on bond issue characteristics (such as issue size, maturity, or rating information) from
the Mergent FISD database, data on bond transactions from the TRACE Enhanced database, data
on management forecasts from IBES, data on firms’ accounting fundamentals from Compustat,
and data on stock prices and returns from the CRSP Security Files.
Following prior literature, I use the intersection of CRSP and Compustat as the firm universe
and “standard” corporate bonds in the intersection of TRACE and FISD as the bond universe.25 I
25 “Standard bonds” exclude non-USD bonds, Yankee bonds, Rule 144A bonds (which were also not subject to TRACE), convertible bonds, pay-in-kind bonds, secured or asset-backed bonds, perpetual bonds, variable-coupon bonds, and bonds with an offering amount below $1 million.
23
match bonds to firms using a matching algorithm that relies on bonds’ cusips, issuer names in bond
prospectuses, and S&P RatingsXpress and CRSP tables on WRDS. I keep the firm quarters of all
firms associated with at least one bond in FINRA's phase lists. Thus, my difference-in-differences
design compares only firms with public bonds, mitigating selection concerns stemming from the
decision to issue bonds. I exclude firms with an SIC code between 6000 and 6999 (i.e., financial
institutions and REITs). Finally, I limit the sample period to July 2001 (one year before the first
phase introduction) through February 2006 (one year after the last phase introduction).
The final sample consists of 13,142 firm-quarter observations between July 2001 and
February 2006, with 849 unique firms. Panel A of Table 2 displays descriptive statistics for the
final sample, and Panel B shows the variable means by the TRACE phase group. The sample firms
are relatively large with an average market capitalization of $10.77 billion. Firms in earlier phases
tend to be larger, likely because large firms tend to have larger and more secure bond issues. The
average guidance frequency is 1.59 forecasts per quarter for the pooled sample and ranges from
1.15 forecasts for Phase 3B firms to 2.18 forecasts per quarter for Phase 2 firms.
The conceptual treatment variable is the observability of market trading/prices of a firm's
debt. I use FINRA’s lists of bonds introduced in each phase to code my empirical treatment
variable, TRACE_Dissemination, a dummy variable that equals one if at least one of the firm’s
bonds has started TRACE dissemination and zero otherwise.
5 Research Design and Main Results
My goal is to estimate the treatment effect of market transparency (i.e., of the observability
of bond prices and trading) on corporate disclosure. This section contains my main tests, which
exploit the staggered implementation of TRACE in a generalized difference-in-differences
framework. Doing so offers multiple advantages from an empirical design perspective. First,
24
TRACE made virtually all trading in corporate bonds observable, thereby affecting the large
population of firms with public bonds. This broad impact allows me to run large-sample tests with
a high degree of statistical power. Second, firms’ bond trading became observable at different
times (in 2002, 2003, 2004, and 2005), allowing me to compare firms whose bonds become
observable with those of firms for whom bond observability does not change simultaneously.
Importantly, I can restrict this comparison to public firms with public bonds, alleviating concerns
about selection bias stemming from the endogenous decision to become/stay public or to issue
bonds. Finally, having multiple introduction dates is beneficial because potential confounding
effects that survive the comparison with the control group likely average out over the phases and
thus have less impact on the treatment effect estimator.
5.1 Effect of Market Transparency on Disclosure
My main regression model is outlined in equation (1) and uses a generalized difference-in-
differences approach (e.g., Bertrand and Mullainathan, 2003). This regression model accounts for
the fact that the multiple TRACE phases are introduced in a staggered fashion. The firm fixed
effects control for fixed differences between treated and non-treated firms and the time fixed
effects control for aggregate fluctuations over time. The regression ultimately uses variation
around the TRACE introduction dates and employs all firms whose bond observability does not
change around a given introduction date as control firms, including firms that already have
observable bonds from an earlier TRACE phase or will have observable bonds at a later point in
time.
𝐶𝑜𝑢𝑛𝑡_𝐺𝑢𝑖𝑑𝑎𝑛𝑐𝑒 𝛽 ⋅ 𝑇𝑅𝐴𝐶𝐸_𝐷𝑖𝑠𝑠𝑒𝑚𝑖𝑛𝑎𝑡𝑖𝑜𝑛 𝛾 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠 , 𝛼 𝛼 𝜀 1
25
The outcome variable, Count_Guidance, is the count of management forecasts in the fiscal
quarter.26 TRACE_Dissemination is a dummy variable that equals one if the firm has a bond for
which TRACE dissemination has begun. The coefficient on TRACE_Dissemination estimates the
treatment effect of TRACE on the disclosure frequency. The control variables include the lagged
log-market capitalization, lagged book-to-market, lagged leverage, and firm- and fiscal-year-
quarter fixed effects. These variables control for the effects of firm size, growth opportunities, and
capital structure on firms’ disclosure. My results are robust to including various other control
variables, including different proxies for firm size, credit risk, firm performance, or time trends for
each phase group.27 Table A1 in the appendix contains the variable definitions. I cluster standard
errors at the firm and fiscal-year-quarter levels.
Panel A of Table 3 contains the first main result of my paper. Specifically, Column 1 contains
my main specification from estimating equation (1), which uses firm and year-quarter fixed effects.
The treatment effect estimate is significantly positive, suggesting that disclosure increases in
response to the TRACE-induced increase in market transparency. The coefficient magnitude
suggests that firms provide 0.3 more management forecasts per quarter as their bond trading
becomes observable, which corresponds to an 18.8% increase in the forecast frequency relative to
the sample mean of 1.6. This result can be seen as my paper’s main finding: firms increase their
discretionary disclosure in response to increased market transparency.
26 Some articles use log(1+Count_Guidance) as the disclosure variable. My results are robust to using this transformation as well. However, using the raw Count_Guidance measure allows me to better assess the economic magnitude of my estimates. Using the “logged” measure, assessing the economic magnitude is difficult. The usual “percentage-change” interpretation of logged variables is approximately right if (i) the values in Count_Guidance were large and (ii) the changes in Count_Guidance were relatively small. However, these conditions are not satisfied by Count_Guidance, so a percentage interpretation might be inadequate. 27 Including these different control variable combinations does not alter my inferences, neither qualitative nor with regard to magnitude. I decide to include relatively few control variables in my model, trading off the potential biases from omitting relevant variables vs. those from including “bad controls” (see Angrist and Pischke, 2009).
26
The remaining columns in Table 3 employ different fixed effect structures. For example, to
mitigate concerns that firms in different phase groups are subject to different industry-specific
dynamics in disclosure, I employ industry-times-year-quarter fixed effects. I estimate a disclosure
increase of 0.299 forecasts per quarter (in column 2), which is almost the same as the estimate
from my main specification. In columns 3 and 4, I use less restrictive fixed effects combinations,
with fixed effects for the phase groups rather than for individual firms. Across all specifications,
the estimates are similar in size, ranging from 0.3 to 0.33 forecasts per quarter.
Panel B contains additional estimates of equation using a wide range of control variable
combinations and also time trend variables for each phase group. For example, I control for return
performance, return volatility, return skewness, trading volume, credit rating, analyst forecast
dispersion and the number of analysts following. Note that I face a tradeoff here. On the one hand,
including more time-varying control variables can potentially reduce selection bias. On the other
hand, including more control variables can introduce “bad control” bias into the coefficients, even
if they improve the prediction of disclosure (see Angrist and Pischke, 2008). This is particularly
the case for variables that are affected by directly by TRACE or by the disclosure (the outcome
variable). I find that the treatment effect estimator remains remarkably stable across the columns,
which is reassuring because it suggests that the bias from either excluding or including the
particular variable is limited.
I also include linear time trends for each phase group to mitigate the concern that the different
phase groups drift differently around the phase introductions for reasons other than TRACE.28 The
28 An unwanted side effect is that the time trend variables may also capture part of the treatment effect, to the degree that the effect increases over time.
27
inclusion of these time trend variables has limited impact on my treatment effect estimates
suggesting that my treatment effect estimator is not driven by differential long-term trends.
In Panel C of Table 3, I focus on the decision about whether or not to disclose, i.e., on the
extensive margin of disclosure.29 The dependent variable is D_Guidance, a dummy indicating
whether the firm made at least one management forecast in the quarter. Across all columns, I
estimate that TRACE increases firms’ probability to disclose by 2.5% to 3.2%. For comparison,
the probability of providing guidance is 60.1% in my sample.
In Table A3, I use an alternative difference-in-differences approach. I use windows around
each phase introduction (-1 year, +1 year) and then stack these samples into a combined sample.
The estimated effect of TRACE price/trading observability on disclosure is 0.33 and thus close to
that from the generalized difference-in-differences regressions. Table A3 also reports the
regression coefficients for each phase. I estimate a positive effect of TRACE price observability
on disclosure, although this effect is insignificant for the first phase. I find it reassuring that the
estimated effect is consistently positive for each phase. Admittedly, however, the individual-phase
estimates are noisier and also offer more scope for bias from simultaneous events than the pooled
estimates in Table 3.30
5.2 Parallel Trends and Confounding Effects
29 Note that Count_Guidance can also be seen as capturing the extensive margin in the sense that multiple disclosures reflect multiple decisions on the extensive margin, e.g., the decisions to forecast CapEx or to forecast an additional horizon. 30 I cannot rule out that simultaneously to a given TRACE introduction, some event takes place that increases disclosure more for the treatment group than the control group. I find it unlikely, however, that such events take place around each phase introduction, and each time such that treatment firms’ disclosure increases relative to control firms. Thus, using multiple phase introduction dates in the same regression mitigates the potential impact of such biasing factors (because their potential impact cancels out to some degree).
28
Like most regulation that involves multiple “treatment groups,” NASD did not assign firms
into treatment groups in a purely randomized fashion. Instead, the assignment occured at the bond
level based on credit ratings and issue sizes. Figure 2 illustrates the threshold rules used to assign
bonds into different treatment groups and highlights that bonds in different phase groups differ
systematically in their issue sizes and credit ratings. As a consequence, firms in different phase
groups tend to differ along associated dimensions, such as their amount of public debt, firm size,
and credit risk. These systematic differences are not a problem per se, but they raise the concern
that firms in different treatment groups might, even in the absence of TRACE, have experienced
different changes in disclosure around their respective TRACE introduction dates.
I address potential concerns about parallel trends in two broad ways. First, in this subsection,
I examine the timing of the disclosure increase relative to the TRACE introduction dates and
provide evidence that the parallel trends assumption is satisfied in my sample. My second broad
way of addressing concerns about parallel trends is to focus directly on the source of possible
differences between the treatment and control groups: the non-random assignment of bonds into
treatment groups. In Section 6, I exploit a randomized controlled experiment, in which NASD
started to release the transaction information for a small number of randomly selected bonds. In a
similar spirit, in Section 7, I exploit cutoff rules used by NASD that sometimes lead to
discrepancies in the treatment group assignment of otherwise similar firms.
In this subsection, I examine the timing of changes in firms’ disclosure relative to the
TRACE introduction dates. The intuition underlying this examination is that market transparency
increases discontinuously around a firm’s phase introduction date, while confounding effects
likely do not. Thus, if the observability of trading and prices affects disclosure, I expect a
discontinuous increase in disclosure around the TRACE introduction dates. This intuition is similar
29
to that underlying an RDD test in which the treatment-assigning variable is the time relative to the
TRACE introduction dates. Moreover, by uncovering how a firm’s pre-period disclosure evolved
(relative to the control group), the examination suggests whether the parallel trends assumption is
plausible.
𝐶𝑜𝑢𝑛𝑡_𝐺𝑢𝑖𝑑𝑎𝑛𝑐𝑒 𝛾 𝑄𝑡𝑟𝑃𝑟𝑒3 𝛾 𝑄𝑡𝑟𝑃𝑟𝑒2 𝛾 𝑄𝑡𝑟𝑃𝑟𝑒1 𝛽 𝑄𝑡𝑟𝑃𝑜𝑠𝑡1 𝛽 𝑄𝑡𝑟𝑃𝑜𝑠𝑡2
𝛽 𝑄𝑡𝑟𝑃𝑜𝑠𝑡3 𝛽 𝑄𝑡𝑟𝑃𝑜𝑠𝑡4_𝑝𝑙𝑢𝑠 𝛼 𝛼 𝛾 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠 , 𝜀 2
Equation (2) estimates the treatment effect of the TRACE introductions on disclosure in the
surrounding quarters. The period ending in the fourth quarter before the TRACE introduction is
omitted and thus serves as the benchmark against which the change in disclosure is measured.
Table 4 contains the estimates, and Figure 3 plots the estimates from my main specification.
Consistent with parallel trends, the estimated treatment effects are close to zero and insignificant
for each quarter before a firm’s TRACE introduction and significant and positive for each quarter
after it. In other words, disclosure jumps precisely when a firm’s prices and trading become
observable, but not before, suggesting that disclosure increases because of the information’s
availability in TRACE, rather than confounding factors. Moreover, consistent with the idea that
TRACE reveals more information over time, disclosure appears to increase slightly over the post-
period.
5.3 Effect on Three Dimensions of Managerial Forecast Frequency
To shed further light on the nature of the documented disclosure increase, I provide more
granular evidence on how firms change their disclosure. Specifically, I ask three related questions.
Do managers forecast (i) more “topics” (e.g., CapEx in addition to Earnings), (ii) more horizons
(e.g., the fiscal year 2005 in addition to 2004), and (iii) more frequently (e.g., on February 1 in
addition to March 1)? To capture these dimensions, I code the variables (i) N_Topic as the count
30
of distinct forecasted topics, 31 (ii) N_Target as the count of distinct forecasted target months, and
(iii) N_Date as the count of distinct dates when a forecast is made.
Table 5 contains my estimates. I find that firms significantly increase their disclosure
frequency along all three disclosure dimensions. This finding suggests that firms disclose not only
more frequently the same variables, they also disclose about a broader set of variables (e.g., about
additional topics and periods).
5.4 Cross-Sectional Variation in the Informativeness of Bond Prices and Trading
If the increase in disclosure is driven by the dissemination of price and transaction
information, then I predict a stronger effect in situations in which the disseminated prices and
trading tend to contain more incremental information. I test this prediction in two ways. First, I
test whether the TRACE introduction has a weaker effect on firms with exchange-listed bonds.32
Exchange-listed bonds generally have observable binding quotes and thus had a relatively higher
degree of price transparency even before TRACE. TRACE is thus likely to reveal less incremental
information. I partition the sample based on whether a firm has exchange-listed bonds and estimate
the effect of TRACE in each sample. Table 6 contains the results. The increase in disclosure is half
as strong for firms with exchange-listed bonds as for firms without them (0.16 vs. 0.33), which is
consistent with the idea that the revealed information content of market trading and prices is what
leads to the documented increase in disclosure.
My second test relies on Merton’s (1974) insight that the information sensitivity of debt
increases with the firm’s credit risk. At one extreme, for firms with risk-less debt, debt values do
not change with news about the firm and are thus uninformative. At the other extreme, for close-
31 Possible topics are ‘Earnings’, ‘Sales’, ‘CapEx’, and ‘Other’. 32 I obtain data on whether a bond is listed on the NYSE from Mergent FISD. Mergent generally codes this information based on whether the bond prospectus states that the issuer filed for the bond to be listed on the exchange.
31
to-default firms, debt values are very sensitive to information about firm value and are thus more
reflective of this information. I partition the sample into investment-grade and junk-bond firms
and estimate the effect of TRACE in each sample. Table 7 contains the estimation results. The
estimated increase in disclosure is more than twice as large for the junk-bond firms as it is for the
investment-grade firms (0.36 vs. 0.14), further supporting the notion that the increase in disclosure
is driven by the revealed information content of prices and trading.
5.5 Effect on Bad vs. Good News Disclosure
Accounting theory generally predicts that managers have greater incentives to withhold bad
news than good news. If the increase in disclosure is attributable to less withholding of bad news,
then I expect that the disclosed information content will become more negative, on average. I test
this prediction in two ways. First, I classify disclosures as negative or positive and test whether
TRACE predominantly affects the amount of negative disclosures. Second, I test whether the
average announcement return becomes more negative after TRACE’s introduction to draw
inferences about the average information content of the disclosures.
My first test classifies management forecasts into positive and negative based on whether
the market’s reaction to the forecast is positive or negative (using the 3-day market-adjusted
return). Stock returns correspond to the theoretical information construct in accounting theory and
capture a forecast’s unexpected value implications. Two further advantages of using stock returns
to classify forecasts are that (i) I can classify management forecasts about sales or CapEx, for
which an unexpectedly high forecasted values are not necessarily good news, and (ii) I can
circumvent problems associated with measuring the manager’s expectation revealed through the
forecast and the market’s expectation at the time of the forecast.33 However, using returns to
33 An alternative to using returns to classify announcements is to focus only on earnings forecasts, for which a better-than-expected value corresponds to positive stock reactions. The problem using this approach is the necessity to
32
classify management forecasts requires the stock return to correspond well to the revealed
information in the management forecast, i.e., the announcement returns should not be confounded
by simultaneous events, such as earnings announcements. For this reason, I focus on unbundled
forecasts. Panel A of Table 8 shows the estimation results. The frequency of management forecasts
increases significantly for bad, but not for good, news announcements, consistent with the idea
that the additional disclosures represent otherwise-withheld bad news.
In Panel C, I classify earnings forecasts into good and bad news relative to the analyst
consensus. I focus on earnings forecasts because higher-than-expected earnings imply a positive
market surprise, and thus, I can classify an announcement by comparing (i) the manager’s revealed
expectation and (ii) the market’s expectation before the forecast. For each forecast, I proxy (i) for
the market’s expectation using the analyst consensus and (ii) for the manager’s revealed
expectation using the forecast itself. For range forecasts, which make up most management
forecasts, measuring the revealed expectation of a given forecast is not straightforward. For
example, Ciconte et al. (2014) demonstrate that the range midpoint is a bad proxy for the manager’s
revealed expectation, even on average, and suggest that the lower bound of the range better reflects
the manager’s revealed expectation. I thus use the lower-bound as my first proxy of the manager’s
expectation, but also use the range midpoint as an alternative measure.
Panel C contains the estimation results. I estimate that TRACE increases the number of bad-
news disclosure but not of good-news disclosures when I use the classification based on Ciconte
et al. Using the alternative measure that is based on managers’ midpoint forecasts, the estimated
correctly measure for each forecast (i) what the manager’s revealed earnings expectation was and (ii) what the market’s expectation at the time of the forecast was. One could assume the midpoint of the manager’s range forecast equals the manager’s revealed earnings expectation; however, Ciconte et al. (2014) demonstrate that the range midpoint is a bad proxy for the manager’s revealed expectation, even on average. Likewise, one could assume that past analyst forecasts equal the market’s expectation at the time of the forecast, but the analyst forecast might be stale and also biased, with the bias depending on the forecast horizon (e.g., Kothari et al., 2016).
33
effect is positive on both bad- and good-news disclosure, and that the effect is 35% stronger on the
disclosure of bad news. These results overall suggest that TRACE increases particularly the
disclosure of bad news, consistent with the idea that managers, on average, reveal more bad news
following the introduction of TRACE.
5.6 Signed and Unsigned Market Reactions to Management Forecasts
My results thus far suggest that firms provide significantly more disclosures in response to
their prices and trading becoming observable. However, this does not necessarily mean that the
average disclosure becomes more informative because (i) part of the value-relevant information
may already have been revealed through the observable prices and trading or through previous
additional disclosures. To shed more light on the value-relevance of individual disclosures, I test
how TRACE affects the average market reaction to the management forecasts.
In particular, I test how TRACE affects the reaction strength to unbundled management
forecasts (conditional on a forecast being made). I use the absolute value of the 3-day market-
adjusted return around the announcement to proxy for the strength of the price reaction to a
management forecast. Using the panel of unbundled announcements, I estimate the difference-in-
differences regression
𝑎𝑏𝑠 𝑅𝑒𝑡 𝛽 ⋅ 𝑇𝑅𝐴𝐶𝐸_𝐷𝑖𝑠𝑠𝑒𝑚𝑖𝑛𝑎𝑡𝑖𝑜𝑛 𝛼 𝛼 𝜖 ?
where the subscript 𝑓 refers to a forecast.
Panel A of Table A7 contains the regression results. The absolute return reactions increase
significantly for firms whose prices/trading become observable through TRACE relative to control
firms whose price/trading observability does not change simultaneously. This suggests that
managers reveal more new information to the market, such that investors gain access not only to
more information disclosures but also to more value-relevant corporate disclosures. The estimated
34
effect of 0.7% is economically meaningful (for comparison, the sample mean of the absolute return
reactions is 4.9%).
I also confirm the result that managers tend to reveal more negative information following
the introduction of TRACE. In Panel B, I test how TRACE affects the signed return reaction to
management forecasts (proxied by the 3-day market-adjusted return). I estimate a significantly
negative effect of TRACE on the announcement returns, consistent with the idea that the forecasts,
on average, reveal more negative information. The effect is economically meaningful with a
magnitude of about 1%, suggesting that TRACE increases managers’ incentives to reveal their
negative information in a timely manner.
6 Exploiting Variation from TRACE Controlled Experiment
NASD conducted a small controlled experiment, introducing 120 randomly selected BBB-
rated bonds to TRACE on April 14, 2003. I use this experiment to address the concern that in my
main sample treatment is not randomized and the parallel trends assumption is thus not implied.
The experiment was conducted together with three finance researchers to estimate the effect of
price transparency on liquidity (on volume and estimated bid-ask spreads) and is published in
Goldstein et al. (2007).34
6.1 Replication of the Sample
FINRA kindly provided me with the list of the 120 treated bonds, so I know which bonds
received treatment and which did not. To replicate the pool of bonds that could have received
treatment (hereafter, the “base sample”), I follow the experiment description in Goldstein et al.
34 Goldstein et al. (2007) find (i) no effect on trading volume and (ii) a reduction in estimated “spreads” for their subsample of actively traded bonds. The results are consistent with investors learning from the broader bond pricing data and being able to negotiate better terms with dealers.
35
(2007). The transaction data needed to construct the “base sample” has recently been made
available to researchers.35
The base sample was selected based on non-disseminated TRACE transaction data for BBB
bonds between July 8, 2002, and January 31, 2003. The sample was narrowed down over the course
of multiple steps. An included bond is BBB-rated with an issue size between $100 million and $1
billion. The bond must be a “standard” corporate bond, have at least one more year of maturity,
not be newly issued, not be issued by a bank, not be convertible, and it must have a certain amount
of transaction activity during the selection period. Goldstein et al.’s restrictions on issue size and
rating imply that an included bond had not been disseminated in an earlier phase. Because my
study takes place at the firm level, this step is insufficient, and I explicitly exclude firms in Phases
1 or 2.
After creating the base sample, NASD randomly selects 90 actively traded and 30 thinly
traded bonds.36 For the actively traded bonds, they first pick 90 pairs of similar bonds (matched
on industry, average trades per day, bond age, and time to maturity), and then randomly pick and
treat one bond per pair. For the thinly traded bonds, they select and treat 30 bonds at random from
the base sample of thinly traded bonds.
Goldstein et al. use two kinds of control groups. For both actively and thinly traded bonds,
they use those bonds in their base sample that did not receive treatment as the control group. For
actively traded bonds, they also use the 90 matched bonds as an alternative control group. Since
my analysis takes place at the firm level, my treatment group consists of firms with a bond that
35 I acknowledge that my replication of the base sample likely contains some error. For example, my algorithm to clean the original TRACE data from errors might differ from the one used by NASD. 36 Firms are classified as “actively traded” if they trade at least once per week, on average, during the selection period. Firms are classified as “thinly traded” if they trade between once every two weeks and once every two days.
36
received treatment in the experiment, and my control group consists of untreated firms with a bond
in the base sample (i.e., of firms that could have received treatment but did not).37
The final sample consists of 1,572 firm-quarters in the period starting one year before and
ending one year after FINRA started dissemination for the randomly selected bonds. Panel A of
Table 9 contains descriptive statistics for the base sample. Panel B contains pre-period mean values
for firms in the treatment and control groups, respectively. Most of the mean values for the
treatment and the control groups are relatively similar. When I test for equality of the pre-period
means (using a simple t-test), I find no significant difference for the disclosure variables, leverage,
or performance. However, I find that firms in the treatment group tend to have a larger market
capitalization, which is also reflected in their book-to-market ratios. This difference suggests that
the treatment group assignment within my base sample may not be perfectly random.
Alternatively, the inequality of means could be due to chance.38
6.2 Results
I estimate the treatment effect of price observability on disclosure using the difference-in-
differences regression in equation (3). The control variables in this regression include the lagged
market capitalization, book-to-market ratio, and leverage, and they mainly serve the purpose of
increasing the estimates’ precision.
𝐶𝑜𝑢𝑛𝑡_𝐺𝑢𝑖𝑑𝑎𝑛𝑐𝑒 𝛽 ∗ 𝑃𝑜𝑠𝑡 ∗ 𝑇𝑟𝑒𝑎𝑡 𝛾 𝑃𝑜𝑠𝑡 𝛾 𝑇𝑟𝑒𝑎𝑡 𝜃 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠 𝜀 3
Panel A of Table 10 contains the estimates. In the first column, I run the baseline model
without fixed effects and estimate that firms increase their disclosure by 0.36 forecasts per quarter
37 For robustness, I also use Goldstein et al.’s (2007) matched bond sample, which Edith Hotchkiss kindly shared with me, and find a significant effect on disclosure. The number of observations in that sample is much lower, however, and the tests’ statistical power is likely lower as well. 38 Under the null hypothesis of equal means, the probability of rejecting equality at the 10% significance level for at least one of the 10 variable means is about 65% if the variables are independently distributed and 10% if the variables are perfectly positively correlated.
37
as their bond trading becomes observable. In columns 2 through 4, I add firm and/or quarter fixed
effects. Despite the small sample size, the treatment effect estimates remain relatively stable across
specifications, ranging from 0.30 to 0.36, which suggests that the treatment assignment is
conditionally independent of the added effects. Moreover, the treatment effect estimates are of the
same magnitude as those from my large sample regressions in Table 3, which ranged from 0.30 to
0.33.
In Panel B, I estimate the effect of TRACE market transparency on the amount of negative
and positive disclosures, as classified by the 3-day announcement return. I estimate that bad news
disclosure significantly increases (by 0.17 forecasts per quarter), but I find no effect on good news
disclosure. This finding is consistent with managers withholding less negative news as market
participants become more informed.
The variation from the TRACE experiment, by itself, has both potential advantages and
disadvantages relative to the large-sample staggered introduction. A potential advantage is that the
regulator randomly selected the bonds whose prices and trading were made observable through
TRACE, which alleviates concerns about the parallel trends assumption. However, the study also
has disadvantages relative to the staggered introduction, however. First, the sample size is much
smaller and the power of the test thus lower. Second, I have no control over the assignment of
treatment to bonds, and also cannot rule out that unobserved factors (or simply noise) influence
the experiment or its outcomes. Finally, because the study is less general (it is not spread out over
a longer period and it focuses only on BBB bonds), it would not be clear whether the results are
generalizable to the broader populations.
Ultimately, I believe that one major benefit of using multiple settings is that they alleviate
the potential concerns in the other settings. For example, the potential concern in the staggered
38
introduction is that because treatment is not randomized, the estimated effect may be attributable
to confounding factors rather than the true average treatment effect of TRACE. However, this
concern is mitigated by finding similar effects in settings where the treatment assignment is
plausibly random. Reversely, the potential concerns about the estimates from the TRACE
experiment are mitigated by finding that the estimates are similar in settings where these concerns
are not present.
7 Exploiting Cutoff Rules Used to Assign Bonds to Phases
In the main phase-in of TRACE, firms are assigned to phase groups based on their bonds’
issue size and rating. To address concerns resulting from this non-random treatment assignment, I
exploit that bonds’ treatment assignment is based on threshold rules with regard to the bonds’ issue
size (conditional on rating) and that these threshold rules do not necessarily transfer to the firm
level. To illustrate the intuition: in Phase 1, an investment-grade bond receives treatment if its
issue size exceeds $1 billion. Due to this threshold rule, a firm receives treatment if it has one $1
billion investment-grade bond, but not if it has two $500 million investment-grade bonds. This
discrepancy in firms’ treatment assignments allows me to hold constant the total issue size of the
firm’s investment grade bonds (which is the firm-level equivalent to the treatment-assigning bond-
level variable).39 Phases 2 and 3A have similar cutoff rules.
The treatment-assigning variable and its firm-level equivalent differ across phases. Figure 2
illustrates which issue-size variable triggers bonds’ treatment assignment for a given phase. For
Phase 1, the issue size of investment-grade bonds triggers bonds’ treatment, so I hold constant the
total issue size of the firm’s investment-grade bonds. For Phase 2, the issue size of A-or-better-
39 For Phase 1, treatment is assigned to bonds based on the issue size given investment-grade. The firm-level equivalent of this variable is the total issue size of the firm’s investment-grade bonds.
39
rated bonds triggers treatment, so I hold constant the total issue size of the firm’s A-or-better-rated
bonds. For Phase 3A, the issue size of A-or-better-rated bonds or the issue size of BBB-rated bonds
triggers treatment, so I hold constant the issue size of the firm’s A-or-better-rated bonds and the
issue size of the firm’s BBB-rated bonds. For Phase 3B, no similar threshold exists.
To illustrate the estimation approach, I first explain how to estimate the treatment effect
using only one phase’s introduction date. I first sort firms into bins based on their issue-size-rating
combination relevant to the treatment group assignment. For example, when I analyze the
introduction of Phase 1, I form bins based on the firm’s logged total issue size of investment grade
bonds. I pick a bin size of 15%, which implies that the largest issue size in a bin is at most 15%
above the smallest issue size in that bin, and that the average difference between two firms’ issue
sizes in the same bin is roughly 5%.40
Using a symmetric window that starts one year before and ends one year after a given
TRACE introduction date, I then estimate the following model:
𝐶𝑜𝑢𝑛𝑡_𝐺𝑢𝑖𝑑𝑎𝑛𝑐𝑒 𝛼 𝛼 𝑃𝑜𝑠𝑡 𝛼 𝑇𝑟𝑒𝑎𝑡 𝛽𝑃𝑜𝑠𝑡𝑇𝑟𝑒𝑎𝑡 𝜀 , 4
where 𝑏 indicates the firm’s issue-size-rating bin. This way, for a treated firm in bin 𝑏, the
counterfactual guidance, 𝛼 𝛼 𝑃𝑜𝑠𝑡 𝛼 𝑇𝑟𝑒𝑎𝑡 (which contains no treatment effect), is
estimated only from firms in the same issue-size-rating bin, 𝑏. The treatment estimator 𝛽 is a
weighted average of the treatment effects estimated for each issue-size-rating bin.
Estimating the treatment effect from only the variation within the issue-size-rating bins helps
address potential endogeneity concerns, however, it substantially reduces the precision of my
40 The 5% approximation assumes that aggregate issue sizes are locally uniformly distributed.
40
treatment effect estimates.41 To increase the power of my tests, I stack the individual-phase
samples into a combined regression sample and estimate the difference-in-differences model:
𝐶𝑜𝑢𝑛𝑡_𝐺𝑢𝑖𝑑𝑎𝑛𝑐𝑒 𝛼 𝛼 𝑃𝑜𝑠𝑡 𝛼 𝑇𝑟𝑒𝑎𝑡 𝛽𝑃𝑜𝑠𝑡𝑇𝑟𝑒𝑎𝑡 𝜀 ,
where 𝑐 indicates which of the three phase introductions the data is from, and 𝑏𝑐 thus represents
the issue-size-rating-times-phase-cohort bin. This difference-in-differences model compares only
data from the same phase introduction and the same issue-size-rating category. The treatment
effect estimator 𝛽 can be interpreted as the precision-weighted average of the different phases’
treatment effects, where only firms with similar issue-size-rating combinations are compared.
Table 11 reports the results from the stacked regressions. In column 1, I estimate my baseline
stacked model, and columns 2 and 3 add firm and year-quarter fixed effects (interacted with the
issue-size-phase-cohort bins). Across all three columns, I estimate that TRACE transparency
increases firms’ disclosure frequency by 0.32 to 0.36 forecasts per quarter. These estimates are
very similar to those from my main sample (0.30 to 0.33) and further suggest that the increase in
disclosure is attributable to the dissemination of prices and trading rather than confounding factors.
8 Alternative Disclosure Measures
8.1 Effect on the Disclosure Quality of Financial Statements
In the previous sections, I follow prior literature and study voluntary disclosure in terms of
firms’ provision of management forecasts. Management forecasts have a voluntary character
because firms have discretion about whether and how they make these forward-looking statements.
In this section, I consider whether market transparency also affects firms’ disclosure of backward-
41 The main reason for this is that the average within-bin imbalance between the treatment and control observations is higher than the unconditional imbalance, which reduces the precision of the treatment effect estimates. In the extreme, for bins without any variation in the treatment group assignment, there is no precision at all, and OLS cannot use observations in that bin to estimate the treatment effect.
41
looking information in their financial reports. While all firms in my sample are required to provide
periodic reports (and thus have no discretion about whether they disclose), they arguably have
some discretion regarding the reports’ level of detail or quality. I predict that increased market
transparency induces firms to provide higher quality disclosures.
To test this prediction, I study firms’ disclosure quality using the disaggregation quality
measure (DQ) proposed by Chen et al. (2015). This measure is constructed as the fraction of non-
missing Compustat line items in the financial reports and reflects the “fineness” of the reporting.
All else equal, more detailed reporting results in higher DQ scores.
To gauge the effect of market transparency on disclosure quality, I estimate the following
generalized difference-in-differences model.
𝐷𝑄 𝛽 ⋅ 𝑇𝑅𝐴𝐶𝐸_𝐷𝑖𝑠𝑠𝑒𝑚𝑖𝑛𝑎𝑡𝑖𝑜𝑛 𝛼 𝛼 𝛾 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠 𝜀 , 5
which is the same as in my main regression that underlies Table 3. However, because the DQ
measure is available only at the annual level, I estimate the model using a firm-year panel.
In column 1 of Table 12, I use firm and year fixed effects and estimate that making
information available through TRACE significantly increases firms’ disclosure quality. In the
remaining three columns, I add industry-times-year fixed effects and/or use phase-group fixed
effects instead of firm fixed effects. Across all columns, I estimate that the fraction of non-missing
line items increases by 0.7% to 0.9%, which corresponds to a 0.09 to 0.12 standard deviation
increase in the DQ measure. This finding suggests that in response to their bonds’ prices and
trading becoming observable, firms not only provide more forward-looking disclosure, they also
increase the quality of their financial reports.
42
8.2 Effect on Alternative Forward-Looking-Statements Measure
My main measure of disclosure, Count_Guidance, captures forward-looking quantitative
statements about different topics, including earnings, CapEx, and Sales. There are forward-looking
statements that my measure does not directly capture. For example, Bozanic, Roulstone, and Van
Buskirk (2018) point out that managers make forward-looking statements (i) not only about
earnings but also about other topics, and (ii) not only in quantitatively but also qualitatively.
Although my measure attempts to capture different topics, it may fail to capture some. More
importantly, my measure completely ignores qualitative disclosures, which can also convey
economic information to market participants.
One concern with using an incomplete disclosure measure is that the documented increase
in quantitative forward-looking disclosure may be offset by a sufficiently large reduction in
qualitative disclosure, so that overall disclosure decreases. To reduce such concerns, I use data
from Bozanic et al. (2018), who use textual analysis to measure the amount of forward-looking
statements in firms’ earnings announcements. This data measures directly the number of all
forward-looking statements, irrespective of whether the statement is qualitative vs quantitative, or
what topic it concerns.42
The forward-looking statement data starts only in late 2004, when earnings announcements
were first made electronically available via 8-K filings from EDGAR. I thus need to restrict the
begin of my sample to August 2004 (which appears to be the first month with full coverage). As a
result, only the introduction of the last phase (on February 2005) is sufficiently covered by the
42 I thank the Zahn Bozanic, Darren Roulstone, and Andy Van Buskirk for sharing their data with me.
43
data, such that I can use only this phase’s introduction in my difference-in-differences regressions.
This substantially reduces the power of my tests.
I estimate a simple difference-in-difference regression around the Phase 3B introduction
date, comparing firms whose prices/trading become observable during the TRACE introduction to
firms whose price/trading observability does not change during the time window.
𝐶𝑜𝑢𝑛𝑡_𝐹𝐿𝑆 𝛽 𝑃𝑜𝑠𝑡𝑇𝑟𝑒𝑎𝑡 𝛽 𝑃𝑜𝑠𝑡 𝛽 𝑇𝑟𝑒𝑎𝑡 𝛾𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠 𝜀 ,
where Count_FLS is the number of sentences with forward-looking statements. PostTreat
is the DiD estimator of the effect of price observability on the number of forward-looking
statements firms release in their earnings calls.
As for the time window, I would like to use symmetric one-year pre- and post-windows.
However, full data coverage appears to begin only in August 2004, which is about 6 months before
the treatment date. In column 1, I thus use a truncated time window, which starts 6 months before
the phase introduction and ends 1 year after the phase introduction. For robustness, I also use a
shorter, symmetric time window starting 6 months before and ending six months after the phase
introduction date.
Table A4 contains the results. I find that around the introduction of TRACE, disclosure of
forward-looking statements increases for firms whose prices/trading become observable relative
to firms whose price/trading observability does not change. This increase is significant despite the
low power of my tests, and it suggests that TRACE increases forward-looking disclosure, thereby
mitigating concerns about my main measure Count_Guidance capturing only quantitative
statements.
44
There are two caveats to this result though. First, the data used in this test is collected only
from earnings announcements. Second, only one phase introduction is sufficiently covered by the
data, which substantially reduces the power of my tests and also leaves more scope for bias in my
estimates.
9 Channels through which TRACE increases Disclosure
The evidence in the previous sections suggests that increased market transparency leads to
increased disclosure by revealing the information contained in prices and trading. However, it is
difficult to discriminate among different informational channels because their predictions with
regard to disclosure are similar. In this section, I provide evidence about which channels are most
plausible to contribute to the TRACE effect by testing more channel-specific predictions.
I consider three non-exclusive informational channels and a channel related to firms’ access
to bond capital as potential first-order contributors to the TRACE effect. First, TRACE may
increase the litigation costs associated with withholding of information (the “litigation” channel).
Second, market information may reduce the proprietary cost of disclosure by revealing to third
parties (e.g., competitors) not-yet disclosed proprietary information (the “proprietary cost”
channel). Third, market information suggests to investors whether something material occurred in
the firm, which reduces uncertainty about whether managers have material information, such that
informed managers face increased pressure for disclosure (the “uncertainty” channel). Finally, I
also consider the possibility that TRACE improves firms’ access to bond capital, leading to
increased bond issuance and related public disclosure (the “increased bond financing channel”
channel). My evidence is consistent with all three informational channels contributing to the
overall TRACE effect, but I find no evidence for the “access-to-bond-capital” channel.
45
Litigation channel: Under the litigation channel, improved access to market information
increases the litigation costs associated with withholding information, which ultimately leads to
increased disclosure. If this channel contributes to the TRACE effect, then I expect to find evidence
(i) that TRACE indeed changed firms’ securities litigation environment and (ii) that this change in
the litigation environment is related to the corporate bond market. Panel A of Table 13 contains
the related results. In column 1, I test how TRACE increased firms’ litigation costs, as proxied by
the total realized settlements paid to plaintiffs. I estimate that TRACE significantly increases
firms’ litigation costs. To better capture the link between litigation and the corporate bond market,
I also run tests that focus specifically on the probability of bondholder litigation.43 Consistent with
the idea that TRACE is particularly important for bondholder litigation, I estimate that TRACE
has a significantly positive effect on the probability of litigation with bondholder participation (in
column 2) but not on the probability of shareholder litigation (in column 3). Overall, the evidence
is consistent with the idea that TRACE affected firms’ litigious environments, in particular by
increasing the threat of bondholder litigation, and thereby lends support to the litigation channel.
I also test cross-sectional predictions (in Panel B) under the litigation, proprietary cost, and
uncertainty channels. First, under the litigation channel, I predict TRACE to have a larger effect
on firms in high-litigation industries because litigation costs are a first-order determinant of these
firms’ disclosure decisions and additional information revelation might increase these firms’
litigation costs more. Measuring litigation risk based on Kim and Skinner (2012), I find that
TRACE increases managers’ disclosure more for firms in high-litigation risk industries (in
columns 1 and 3).
43 My data allows me to infer whether the class action involves bondholders as eligible participants. However, it does not what the settlement amounts to bondholders are.
46
Second, under the proprietary cost channel, I predict TRACE to have a larger effect in
industries in which proprietary costs play a more important role and thus impact firms’ disclosure
decisions more. Proprietary costs represent a loss of economic rents and likely play a larger role
in more concentrated industries, which tend to have larger economic rents.44 Consistent with the
proprietary cost channel, I find that disclosure increases more in highly concentrated industries,
where I use the Herfindahl-Hirschman Index to measure an industry’s market concentration. In
column 3, I estimate that TRACE has a larger effect on disclosure in both high-litigation and high-
proprietary-cost industries, suggesting that both the litigation and proprietary cost channels
contribute to the TRACE effect and neither of them alone explains the increase in disclosure.
Finally, under the uncertainty channel, reduced uncertainty about whether material events
occurred in the firm leads to increased demand for disclosure from managers with material
information. While this channel is not clearly distinct from the other informational channels (e.g.,
demand could be expressed through the threat of litigation), the order of events is such that
informed traders incorporate their information into prices, which reveals information about
whether managers possess material information and affects informed managers’ disclosure
accordingly. I thus predict that increased disclosure follows increased observable trading activity
(uncertainty reduction). In column 4, I find that increased trading tends to be followed by increased
disclosure, but only for firms with observable trading through TRACE. Because some TRACE
firms may have both observable and unobservable bonds, I examine TRACE’s trade records and
decompose their volume into its disseminated and non-disseminated components. In column 6, I
find that disclosure increases following increased trading only if the trading is observable.
44 Consistent with the argument, the importance of proprietary costs is generally proxied by some measure of product market competition and hypothesized to be higher when product market competition is lower (Berger, 2011).
47
An alternative explanation for the increase in disclosure is that TRACE improved firms’
access to corporate bond markets, which led to increased bond issuance, and relatedly, to increased
disclosure (the “increased bond financing channel”). To test the plausibility of this argument, I
examine the impact of TRACE on firms’ bond financing decisions in Panel C. I find no evidence
that TRACE increases the probability of issuing bonds, the total dollar amount of bonds issued, or
the leverage ratio. These results suggest it is implausible that the increase in disclosure is driven
by increased bond issuance and the associated public disclosure.
Together, the evidence in this section lends plausibility to three non-exclusive informational
channels – the litigation, proprietary cost, and uncertainty channels – but not to the bond financing
channel. None of the three information channels alone appear to be fully responsible for the
increase in disclosure, but instead, each of them plausibly contributes to the TRACE effect. This
finding is consistent with market prices and trading incorporating, and thus reflecting, diverse
information that is useful in different decision contexts.
10 Earnings Conference Call Q&A
The economic channels proposed thus far suggest that TRACE changes managers’ economic
incentives to disclose, thereby leading to increased disclosure. For example, under the Skinner
(1994, 1997) argument, managers disclose more due to the increased threat of investor litigation.
Under the unraveling arguments (e.g., Verrecchia, 1983, Dye, 1985), managers disclose more due
to investors’ price pressure (in the sense that investors value the firm at a lower price in the absence
of disclosure). In this sub-section, I test an alternative mechanism, namely whether TRACE affects
the degree to which investors (or rather analysts) ask management explicitly for information.45
45 Note that none of these channels necessarily reflect investors’ intrinsic desire to possess the disclosure information.
48
To do so, I use data from the Q&A sections of earnings conference calls and attempt to
measure the number and length of questions/statements by firm outsiders. Unfortunately, I cannot
obtain standardized and easy-to-obtain data for my sample period (e.g., from Capital IQ).46 I thus
use a hand-collected sample of transcripts that Eric So kindly shared with me. Unfortunately, my
sample of transcripts has limited sample coverage and, more importantly, does not always follow
the same format, which imposes some difficulties for my analysis. For example, I cannot always
identify whether the talking person is a firm outsider. I proxy for the amount of
questions/statements by firm outsiders in two ways. First, I use the total number of statements
made in a Q&A session (with the exception of statements by the moderator), capturing both
statements by company representatives and outsiders. More questions/statements by firm outsiders
increase this proxy (i) directly and (ii) also indirectly by increasing the number of management
responses. As a second proxy for the number of statements made by outsiders, I use the total
number of statements which the transcript indicates as being made by analysts. While this measure
misses many statements made by firm-outsiders (even some made by analysts), it has the
advantage that it does not capture statements made by the firm’s management.
I test whether TRACE affects these proxies for the amount of statements/questions by
conference call participants (mostly analysts). Table A4 contains the results. In columns 1 and 2,
I use all statements in the Q&A transcript and find no significant effect on either the number of
statements or the number of words in a Q&A session. I then repeat this exercise using only
46 For more recent periods, there are arguably better and easier-to-obtain datasets available for more recent periods. For example, Capital IQ offers standardized and summarized transcript data. However, proper coverage in this dataset begins only after 2008, and coverage over the TRACE period is almost non-existent (with less than 100 transcripts before 2006).
In contrast, my transcript data covers much of my sample period, with reasonably good coverage starting from May 2002. I start my sample period in May 2002, and have 5,472 firm-quarter observations with matched earnings call Q&A data (which constitutes 52% of the post-May 2002 observations).
49
statements identified to come from analysts. In columns 3 and 4, I find no significant effect on
both the number of sentences and words. Together, the results suggest that TRACE had no
significant impact on the average degree to which firm outsiders (mostly analysts) make
statements/questions during earnings conference calls. This result is consistent with managers
responding to economic pressures for disclosure, as described in my channels, as opposed to
intrinsic interest or questions by shareholders. However, I want to emphasize two reasons why I
cannot rule out that managers’ information disclosure increases also because of direct questions in
Q&A sessions. First, if analysts/investors adjust their questions based on what they learned from
returns/trading, I find it plausible that informed managers tend to receive more questions and
uninformed managers receive fewer questions. 47 This is badly captured when I estimate the
average effect. Second, the tests in Table A4 have admittedly low power due to the small sample
size and the noisy measurement of variables.
11 Conclusion
Observable market prices and trading summarize and broadly reveal investors’ private
information and beliefs. Despite their importance as information constructs, it has been an open
question how observable market prices and trading affect managers’ disclosure incentives. To
answer this question, I study the introduction of TRACE between 2002 and 2005, which made
previously unobservable bond prices and trading observable. The TRACE setting has multiple
desirable properties that plausibly allow me to quantify effects and make causal inferences.
47 Note that the logic underlying this argument is related to that underlying the “uncertainty about managers’ information endowment” channel, where TRACE coordinates investors’ demand for disclosure (price pressure) such that informed managers tend to face increased demand for disclosure and uninformed managers tend to face decreased demand for disclosure.
50
I find that firms increase their voluntary disclosure in response to increased transparency.
This increase is stronger for firms whose bond prices and trading tend to contain more incremental
information, namely firms without exchange-listed bonds and firms with higher credit risk, and it
is more pronounced for the disclosure of bad news. I provide preliminary evidence that three non-
exclusive informational channels contribute to the TRACE effect: the litigation, proprietary cost,
and uncertainty channels. Overall, these findings are consistent with the notion that market
participants’ access to information limits managers’ withholding incentives.
I use three ways to mitigate concerns that the documented increase in disclosure is
attributable to confounding effects rather than the TRACE effect. First, I study the timing of the
disclosure increase and find that the effect on disclosure is insignificant during the entire pre-
period (consistent with parallel trends) and that it jumps precisely as a firm’s trading and prices
become observable. Second, I exploit a small controlled experiment in which prices and
transactions were revealed for randomly selected bonds. Finally, I exploit threshold rules in bonds’
treatment assignments that lead to differential treatment assignments of otherwise similar firms.
My treatment effect estimates are very similar across methodologies and regression specifications,
ranging from 0.3 to 0.36 more forecasts per quarter, which lends credibility to their accuracy.
I make two contributions. First, my findings are relevant for regulators concerned with
investors’ access to information and have implications for the debate about mandated transparency
of financial markets. My study suggests that observable market data inform investors not only
directly, by aggregating and revealing the information contained in prices/returns/trading, but also
indirectly, by increasing the information that firms themselves provide. Second, I contribute to the
disclosure literature, which already documents that managers often have incentives to withhold
51
information (e.g., Kothari et al., 2009). My study suggests that market participants’ access to
market information limits the extent to which managers withhold information.
52
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Figure 1: Timeline of TRACE Bond Dissemination
This figure illustrates the introduction of TRACE in multiple phases over time and the requirements to be included in a given
Phase. TRACE was introduced in four main phases: Phase 1 (on 1-Jul-02), Phase 2 (on 3-Mar-03), Phase 3A (on 1-Oct-04),
and Phase 3B (on 7-Feb-05). In addition, the timeline includes the “TRACE 120” experiment, in which NASD randomly
selected 120 BBB-bonds and revealed their transaction data through TRACE (starting from 14-Apr-03).
Figure 2: Requirements for Bonds to be Included in a Phase in Issue-Size-vs-Rating Space
This figure illustrates the requirements bonds must satisfy with regard to their (i) initial issue size and (ii) rating to be
disseminated as part of a given phase.
Figure 3: Timing of the Disclosure Effect Relative to the TRACE Introduction Dates
This figure displays the estimated effect of TRACE on a firm’s management forecast frequency in the quarters surrounding
the firm’s TRACE phase introduction. The red bar indicates when the firm’s price and transaction information becomes
observable through TRACE. The displayed effects are the coefficient estimates from column 1 in Table 4 and come from the
regression equation Count_Guidanceit � γ1Pre_Q3it � γ2Pre_Q2it � γ3Pre_Q1it � β1Post_Q1it � β2Post_Q2it �
β3Post_Q3it � β4Post_Q4_Plusit � αi � αt � θ�Controlsi,t�1 � εit. The benchmark period (omitted in the regression)
consists of all quarters up to the fourth quarter before the firm’s TRACE introduction. Errors are clustered at the firm and
year-quarter level. See Table A1 for variable definitions.
-.20
.2.4
.6.8
≤-Q4(omitted)
-Q3 -Q2 -Q1 +Q1 +Q2 +Q3 ≥+Q4
Time
Table 1: TRACE Phase Introduction Dates
This table summarizes the introduction dates and selection criteria for the four main phases (Phases 1, 2, 3A, and 3B) and for
the TRACE 120 bonds. See Section 2 for a more detailed description of the TRACE introduction.
Phase Group Date Bond Characteristics
Phase 1 1-Jul-02 Investment grade bonds with an initial issue size of at least $1 billion.
Phase 2 3-Mar-03 Adds bonds with a rating of at least A and an initial issue size
of at least $100 million.
Phase 3A 1-Oct-04 Adds all remaining bonds that are “not eligible for delayed dis-
semination,” which practically corresponds to the remaining
bonds with a rating of at least BBB.
Phase 3B 7-Feb-05 Adds the bonds that are eligible for delayed dissemination,
which practically corresponds to bonds with a rating of at most
BB.
TRACE 120 14-Apr-03 Adds 120 randomly selected BBB bonds with issue sizes be-
tween $10 million and $1 billion.
Table 2: Descriptive Statistics for the Main Sample
Panel A includes descriptive statistics for the sample, and Panel B includes variable means for each phase group.
Count_Guidance is the quarterly forecast frequency and defined as the count of management forecasts made in the firm-
quarter. D_Guidance is a dummy variable indicating whether at least one management forecast was made in the firm-quarter.
TRACE_Dissemination is a dummy variable indicating if the firm has a bond for which TRACE dissemination has begun.
See Table A1 for definitions of the other variables.
(a) Univariate Descriptive Statistics
N mean sd p1 p25 p50 p75 p99
Count_Guidance 13,224 1.61 2.05 0 0 1 2 9
D_Guidance 13,224 .602 .49 0 0 1 1 1
N_Topic 13,224 .881 .881 0 0 1 1 3
N_Date 13,224 .901 .959 0 0 1 1 4
N_Target 13,224 .945 .949 0 0 1 2 3
Count_Guidance_Bad 13,224 .268 .81 0 0 0 0 4
Count_Guidance_Good 13,224 .262 .827 0 0 0 0 4
TRACE_Dissemination 13,224 .387 .487 0 0 0 1 1
Size 13,224 10,789 24,439 28.1 874 2,621 8,801 148,095
BTM 13,224 .523 .859 -2.23 .283 .484 .721 3.05
Leverage 13,224 .68 .189 .308 .556 .661 .775 1.37
Rating 12,918 4.68 1.4 2 4 4.5 6 8
D_Junk 13,224 .51 .5 0 0 1 1 1
(b) Means by TRACE Phase
(1) (2) (3) (4) (5)
All Phase1 Phase2 Phase3a Phase3b
Count_Guidance 1.61 2.20 2.19 1.61 1.14
D_Guidance 0.60 0.73 0.75 0.62 0.47
N_Topic 0.88 1.07 1.12 0.90 0.68
N_Date 0.90 1.27 1.23 0.92 0.62
N_Target 0.94 1.16 1.21 0.98 0.71
Count_Guidance_Bad 0.27 0.46 0.38 0.24 0.19
Count_Guidance_Good 0.26 0.42 0.39 0.26 0.14
TRACE_Dissemination 0.39 0.79 0.63 0.31 0.23
Size 10789.1 41122.7 29259.3 5293.3 1434.3
BTM 0.52 0.52 0.37 0.55 0.58
Leverage 0.68 0.68 0.61 0.70 0.70
Rating 4.68 3.77 2.88 4.78 5.80
D_Junk 0.51 0.19 0.011 0.50 0.90
Observations 13224 1020 2104 4970 4395
Table 3: Effect of TRACE Dissemination on Disclosure
This table contains estimates from the generalized difference-in-differences regression Count_Guidanceit � β �
TRACE_Disseminationit�αi�αt�γ�Controlsi,t�1�εit. In Panel A, the dependent variable is the quarterly frequency
of management forecasts, Count_Guidance. In Panel B, the dependent variable is a dummy variable indicating whether
at least one management forecast was made in the quarter, D_Guidance. TRACE_Dissemination is a dummy variable
indicating if the firm has a bond for which TRACE dissemination has begun, i.e., if the firm’s bond trading and prices are
observable. Errors are clustered at the firm and year-quarter level. See Table A1 for variable definitions.
(a) Frequency of Management Forecasts as the Dependent Variable
(1) (2) (3) (4)
Count_Guidance Count_Guidance Count_Guidance Count_GuidanceTRACE_Dissemination 0.320*** 0.322*** 0.337*** 0.354***
(4.31) (4.38) (3.37) (3.76)
log�Size� 0.174** 0.228*** 0.263*** 0.258***
(2.63) (3.07) (6.71) (6.92)
log�Leverage� -1.543*** -0.939* -0.803 -0.138
(-3.10) (-1.78) (-1.42) (-0.27)
BTM 0.032 0.039 -0.056* -0.023
(1.07) (1.26) (-1.73) (-0.77)
Fixed Effects Firm Qtr Firm Qtr�Ind PhaseGroup Qtr PhaseGroup Qtr�Ind
Cluster Firm Qtr Firm Qtr Firm Qtr Firm Qtr
Observations 13,214 13,023 13,224 13,032
R2 0.54 0.59 0.15 0.29
(b) Results using alternative control variable specifications(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14)
Count_Guidance Count_Guidance Count_Guidance Count_Guidance Count_Guidance Count_Guidance Count_Guidance Count_Guidance Count_Guidance Count_Guidance Count_Guidance Count_Guidance Count_Guidance Count_GuidanceTRACE_Dissemination 0.318*** 0.320*** 0.322*** 0.315*** 0.320*** 0.319*** 0.316*** 0.316*** 0.306*** 0.280*** 0.311*** 0.296*** 0.283*** 0.268**
(4.21) (4.31) (4.31) (4.34) (4.30) (4.28) (4.32) (3.23) (4.10) (3.70) (4.08) (2.97) (4.29) (2.63)
log�Size� 0.205*** 0.170** 0.169** 0.162** 0.168** 0.178** 0.182** 0.226** 0.076 0.136* 0.141** 0.126 0.214*** 0.149
(2.95) (2.57) (2.53) (2.44) (2.52) (2.70) (2.71) (2.17) (1.22) (1.72) (2.11) (1.14) (3.34) (1.33)
log�Leverage� -1.396** -1.574*** -1.457** -1.476*** -1.554*** -1.515*** -1.533*** -1.629** -1.301** -1.607*** -1.281** -1.212* -1.561*** -1.230*
(-2.81) (-3.17) (-2.83) (-3.01) (-3.12) (-3.05) (-3.10) (-2.53) (-2.63) (-3.02) (-2.58) (-1.91) (-3.15) (-1.93)
BTM 0.027 0.034 0.031 0.028 0.031 0.031 0.028 0.093 0.015 0.035 0.030 0.054 0.024 0.049
(0.91) (1.10) (1.04) (0.96) (1.02) (1.02) (0.91) (1.52) (0.55) (0.86) (1.05) (0.96) (0.83) (0.87)
ret_fisc_lag -0.172*** -0.065 -0.081
(-3.04) (-0.69) (-0.85)
niq_scaled2 0.323
(0.64)
Common/Ordinary Equity - Total 0.000
(0.87)
Assets - Total 0.000*
(1.92)
std_ret_lag -0.566 -1.208 -1.714
(-0.72) (-0.39) (-0.55)
skewness_lag -0.028* -0.025 -0.024
(-1.87) (-1.01) (-0.98)
turnover_lag -0.201*** -0.115 -0.094
(-2.92) (-1.34) (-1.09)
dispersion_lag 0.021 0.001 0.002
(1.36) (0.02) (0.09)
ln_n_analysts 0.463*** 0.457*** 0.471***
(5.88) (3.33) (3.42)
analyst_forecast_n 0.061***
(7.83)
Rating -0.202*** -0.156* -0.143
(-3.26) (-1.75) (-1.63)
niq_scaled2_lag 0.072 0.066
(0.10) (0.09)
time_trend_p1 0.000 -0.003
(0.00) (-0.13)
time_trend_p2 0.014 0.021
(0.72) (0.92)
time_trend_p3 -0.010 0.008
(-0.56) (0.36)
time_trend_p4 -0.033* -0.013
(-1.74) (-0.54)
time_trend_p120 -0.023 0.000
(-0.97) (.)
Fixed Effects Firm Qtr Firm Qtr Firm Qtr Firm Qtr Firm Qtr Firm Qtr Firm Qtr Firm Qtr Firm Qtr Firm Qtr Firm Qtr Firm Qtr Firm Qtr Firm Qtr
Cluster Firm Qtr Firm Qtr Firm Qtr Firm Qtr Firm Qtr Firm Qtr Firm Qtr Firm Qtr Firm Qtr Firm Qtr Firm Qtr Firm Qtr Firm Qtr Firm Qtr
Observations 13,193 13,214 13,196 13,214 13,207 13,207 13,207 7,997 13,214 10,658 12,909 7,857 13,214 7,857
R2 0.54 0.54 0.54 0.54 0.54 0.54 0.54 0.55 0.55 0.54 0.54 0.56 0.54 0.56
(c) Disclosure Dummy as the Dependent Variable
(1) (2) (3) (4)
D_Guidance D_Guidance D_Guidance D_GuidanceTRACE_Dissemination 0.032** 0.026 0.032* 0.031*
(2.20) (1.66) (1.97) (1.78)
log�Size� 0.079*** 0.083*** 0.088*** 0.086***
(5.36) (5.18) (10.27) (10.12)
log�Leverage� 0.011 -0.003 0.015 0.051
(0.09) (-0.02) (0.13) (0.45)
BTM -0.002 -0.002 -0.006 -0.004
(-0.28) (-0.27) (-0.65) (-0.48)
Fixed Effects Firm Qtr Firm Qtr�Ind PhaseGroup Qtr PhaseGroup Qtr�Ind
Cluster Firm Qtr Firm Qtr Firm Qtr Firm Qtr
Observations 13,214 13,023 13,224 13,032
R2 0.53 0.57 0.15 0.27
Table 4: Parallel Trends – Timing of the Effect of TRACE Dissemination on Disclosure
This table estimates the timing of the TRACE effect on disclosure. It contains difference-in-differences estimates of the effect
of TRACE dissemination on the guidance frequency in individual quarters surrounding the firm’s TRACE introduction date.
The estimated model is Count_Guidanceit � γ1Pre_Q3it�γ2Pre_Q2it�γ3Pre_Q1it�β1Post_Q1it�β2Post_Q2it�
β3Post_Q3it � β4Post_Q4_Plusit � αi � αt � γ�Controlsi,t�1 � εit. The explanatory variables are dummy variables
indicating the quarter relative to the introduction of the firm’s TRACE dissemination. For example, Post_Q1 refers to the
first quarter after the Phase introduction, Post_Q2 refers to the quarter thereafter, and so on. Likewise, Pre_Q1 refers to the
last quarter before the introduction of TRACE, and so on. The benchmark period (omitted in the regression) consists of all
quarters up to the fourth quarter before the firm’s TRACE introduction. Errors are clustered at the firm and year-quarter level.
See Table A1 for variable definitions.
(1) (2) (3) (4)
Count_Guidance Count_Guidance Count_Guidance Count_GuidancePre_Q3 -0.070 -0.036 -0.057 -0.009
(-0.77) (-0.40) (-0.32) (-0.06)
Pre_Q2 0.014 0.037 0.025 0.058
(0.16) (0.32) (0.16) (0.34)
Pre_Q1 -0.005 0.051 -0.013 0.073
(-0.04) (0.33) (-0.06) (0.34)
Post_Q1 0.234* 0.246* 0.240 0.268
(2.01) (1.97) (1.26) (1.47)
Post_Q2 0.324** 0.363** 0.323 0.384*
(2.41) (2.35) (1.56) (1.91)
Post_Q3 0.250** 0.327** 0.269* 0.379***
(2.37) (2.74) (1.93) (3.04)
Post_Q4_Plus 0.482*** 0.505*** 0.525** 0.601***
(2.90) (2.84) (2.68) (2.97)
log�Size� 0.190*** 0.240*** 0.265*** 0.259***
(3.02) (3.38) (6.78) (7.00)
log�Leverage� -1.533*** -0.930* -0.817 -0.153
(-3.09) (-1.77) (-1.44) (-0.30)
BTM 0.029 0.036 -0.058* -0.025
(1.00) (1.20) (-1.81) (-0.87)
Fixed Effects Firm Qtr Firm Qtr�Ind PhaseGroup Qtr PhaseGroup Qtr�Ind
Cluster Firm Qtr Firm Qtr Firm Qtr Firm Qtr
Observations 13,214 13,023 13,224 13,032
R2 0.54 0.59 0.15 0.29
Table 5: Effect of TRACE Dissemination on Three “Dimensions” of Managerial Forecast Fre-quency
This table studies the effect of market transparency on the management forecast frequency along three “dimensions”. First,
firms can increase the forecast frequency by providing forecasts about more topics (‘Earnings’, ‘Sales’, ‘CapEx’, and ‘Other’).
N_Topic reflects this dimension and is defined as the number of distinct topics forecasted in the quarter. Second, firms can
increase the forecast frequency by forecasting on more dates. N_Dates reflects this dimension and is defined as the number
of distinct dates on which the firm provides forecasts. Third, firms can increase the forecast frequency by forecasting different
horizons (e.g., for this and for the next fiscal year). N_Target reflects this dimension and is defined as the number of distinct
target months (in which the forecasted period ends). TRACE_Dissemination is a dummy variable indicating if the firm
has a bond for which TRACE dissemination has begun, i.e., if the firm’s bond trading and prices are observable. Errors are
clustered at the firm and year-quarter level. See Table A1 for variable definitions.
(1) (2) (3)
N_Topic N_Date N_TargetTRACE_Dissemination 0.140*** 0.101*** 0.082***
(4.39) (3.62) (2.88)
log�Size� 0.042 0.119*** 0.136***
(1.57) (4.37) (4.53)
log�Leverage� -0.910*** -0.314 -0.186
(-3.84) (-1.45) (-0.84)
BTM 0.016 0.002 -0.002
(0.91) (0.15) (-0.16)
Fixed Effects Firm Qtr Firm Qtr Firm Qtr
Cluster Firm Qtr Firm Qtr Firm Qtr
Observations 13,214 13,214 13,214
R2 0.58 0.52 0.52
Table 6: How does Pre-Existing Price Transparency Affect the Relation between TRACE Dissem-ination and Disclosure?
This table contains subsample analyses, in which the subsamples are created based on whether a firm has a NYSE-listed
bond. In columns 1 and 2, the dependent variable is Count_Guidance, the count of management forecasts in the quarter.
In columns 3 and 4, the dependent variable is D_Guidance, a dummy variable indicating whether at least one management
forecast was made in the quarter. Errors are clustered at the firm and year-quarter level. TRACE_Dissemination is a
dummy variable indicating if the firm has a bond for which TRACE dissemination has begun, i.e., if the firm’s bond trading
and prices are observable. See Table A1 for variable definitions.
Y=Count_Guidance Y=D_Guidance
(1) (2) (3) (4)
Exchange-Listed Not Exchange-Listed Exchange-Listed Not Exchange-Listed
TRACE_Dissemination 0.194** 0.356*** -0.002 0.047**
(2.43) (3.78) (-0.08) (2.68)
log�Size� 0.318* 0.145** 0.053* 0.078***
(2.00) (2.18) (1.85) (4.41)
log�Leverage� -1.388 -1.643*** -0.380* 0.070
(-1.46) (-2.84) (-1.91) (0.48)
BTM 0.005 0.034 -0.052*** 0.004
(0.06) (1.20) (-4.26) (0.48)
Fixed Effects Firm Qtr Firm Qtr Firm Qtr Firm Qtr
Cluster Firm Qtr Firm Qtr Firm Qtr Firm Qtr
Observations 4,269 8,937 4,269 8,937
R2 0.56 0.55 0.54 0.53
χ2-statistic for differences 1.69 2.84
p-Value for differences 0.19 0.09
Difference -0.16 -0.05
t-value from interacted model -1.54 -1.67
Table 7: How Does Credit Risk Affect the Relation between TRACE Dissemination and Disclo-sure?
This table contains subsample analyses, in which the subsamples are based on the firm’s junk bond status. In columns 1 and
2, the dependent variable is Count_Guidance, the count of management forecasts in the quarter. In columns 3 and 4, the
dependent variable is D_Guidance, a dummy indicating whether at least one management forecast was made in the quarter.
TRACE_Dissemination is a dummy variable indicating if the firm has a bond for which TRACE dissemination has begun,
i.e., if the firm’s bond trading and prices are observable. Errors are clustered at the firm and year-quarter level. See Table A1
for variable definitions.
Y=Count_Guidance Y=D_Guidance
(1) (2) (3) (4)
Junk Status Non-Junk Status Junk Status Non-Junk Status
TRACE_Dissemination 0.392*** 0.166* 0.075*** -0.004
(4.55) (1.83) (3.55) (-0.25)
log�Size� 0.178** 0.192 0.069*** 0.087***
(2.67) (1.33) (3.83) (3.27)
log�Leverage� -1.788*** 0.684 -0.011 0.070
(-3.20) (0.65) (-0.07) (0.37)
BTM 0.010 0.224 -0.001 -0.011
(0.37) (1.24) (-0.14) (-0.34)
Fixed Effects Firm Qtr Firm Qtr Firm Qtr Firm Qtr
Cluster Firm Qtr Firm Qtr Firm Qtr Firm Qtr
Observations 6,727 6,476 6,727 6,476
R2 0.53 0.55 0.51 0.55
χ2-statistic for differences 3.00 6.04
p-Value for differences 0.08 0.01
Difference 0.23 0.08
t-value from interacted
model
1.71 2.77
Table 8: Effect of TRACE Dissemination on the Content of Disclosures (Good vs. Bad News)
In Panel A, I estimate the effect of TRACE dissemination on the number of bad-news forecasts (Count_Guidance_Bad)
or good-news forecasts (Count_Guidance_Good), where the news content is classified based on announcement returns.
Only unbundled management forecasts are used, so the announcement return captures the content of the management forecast
rather than that of the contemporaneous earnings announcement. In Panel B earnings forecasts are classified into good
and bad news relative to the analyst consensus forecast. I focus on earnings forecasts because for earnings, a higher-than-
expected value corresponds unambiguously to a positive market surprise, which allows me to measure the market’s surprise
to the degree that I correctly measure (i) the manager’s revealed expectation and (i) the market’s expectation for each forecast.
I classify a management forecast as good if the manager’s forecast (to proxy for the manager’s revealed expectation) exceeds
the analyst consensus (to proxy for the market’s expectation). Most earnings forecasts are range forecasts, and benchmarking
them requires strong assumptions about the manager’s revealed expectations. Ciconte et al. (20xx) demonstrate that the
range midpoint is a bad proxy for the manager’s revealed expectation, even on average, and suggest that the lower-bound of
the range forecast better reflects the manager’s revealed expectation. Based on Ciconte et al.’s evidence, I assume for range
forecasts that the range’s lower bound reflects the manager’s revealed expectation. Count_Ana1_Bad is the count of bad-
news forecasts based on this assumption. Because the classification of each announcement is sensitive to the classification
assumption, I also construct an alternative measure, Count_Ana2_Bad, which assumes that a range’s midpoint reflects the
manager’s revealed expectations. TRACE_Dissemination is a dummy variable indicating if the firm has a bond for which
TRACE dissemination has begun, i.e., if the firm’s bond trading and prices are observable. Errors are clustered at the firm
and year-quarter level. See Table A1 for variable definitions.
(a) Effect on the Frequency of Bad-News and Good-News Disclosures
(1) (2)
Count_Guidance_Bad Count_Guidance_GoodTRACE_Dissemination 0.070** 0.003
(2.82) (0.10)
log�Size� 0.065** 0.008
(2.73) (0.42)
log�Leverage� -0.336 -0.020
(-1.71) (-0.10)
BTM 0.002 0.001
(0.27) (0.14)
Fixed Effects Firm Qtr Firm Qtr
Cluster Firm Qtr Firm Qtr
Observations 13,214 13,214
R2 0.19 0.23
(b) Using Analyst vs midpoint or Ciconte measure
Measure 1: Measure 2:
(1) (2) (3) (4)
Count_Ana_Bad Count_Ana_Good Count_Ana2_Good n_date_target_topic_m_good
TRACE_Dissemination 0.133** 0.012 0.072* 0.053**
(2.28) (0.74) (1.80) (2.11)
log�Size� 0.091*** 0.029* 0.090*** 0.035*
(3.13) (1.96) (3.71) (1.81)
log�Leverage� -0.938*** -0.108 -0.839*** -0.185
(-3.05) (-0.89) (-3.30) (-0.88)
BTM 0.014 0.005 0.005 0.016**
(1.19) (1.25) (0.64) (2.19)
Fixed Effects Firm Qtr Firm Qtr Firm Qtr Firm Qtr
Cluster Firm Qtr Firm Qtr Firm Qtr Firm Qtr
Observations 13,214 13,214 13,214 13,214
R2 0.47 0.26 0.37 0.34
61
Table 9: Effect of TRACE on Absolute and Signed Return Reactions to Management Forecasts
This Table estimates how TRACE affects the absolute and signed return reactions to the management forecasts (conditional on
a forecast being made). In Panel A, I estimate the effect on the absolute return reaction to the announcement. Abs�RetAbn� is
the absolute value of the 3-day market-adjusted return centered around the announcement. Abs�RetRaw� is the corresponding
raw return reaction. In Panel B, I estimate the impact on the signed return reactions, RetAbn and RetRaw.
(a) Effect on Absolute Return Reaction to Management Forecast
(1) (2)
Abs�RetAbn� Abs�RetRaw�TRACE 0.008** 0.008*
(2.27) (2.03)
Fixed Effects Firm Qtr Firm Qtr
Cluster Firm Qtr Firm Qtr
Observations 4,487 4,487
R2 0.38 0.37
(b) Effect on Signed Return Reaction to Management Forecast
(1) (2)
RetAbn RetRaw
TRACE -0.010* -0.010*
(-1.85) (-1.78)
Fixed Effects Firm Qtr Firm Qtr
Cluster Firm Qtr Firm Qtr
Observations 4,487 4,487
R2 0.21 0.21
Table 10: Descriptive Statistics for the TRACE 120 Experiment
Panel A includes univariate descriptive statistics for the sample, and Panel B includes variable means for the treatment and
control groups. See Table A1 for variable definitions.
(a) Univariate Descriptive Statistics
N mean sd p1 p25 p50 p75 p99
Count_Guidance 1,533 1.58 1.95 0 0 1 2 8
D_Guidance 1,533 .637 .481 0 0 1 1 1
N_Topic 1,533 .837 .771 0 0 1 1 3
N_Target 1,533 1.01 .949 0 0 1 2 3
N_Date 1,533 .933 .947 0 0 1 1 4
Count_Guidance_Bad 1,533 .302 .876 0 0 0 0 4
Count_Guidance_Good 1,533 .288 .937 0 0 0 0 4
log�Size� 1,533 8.15 1.13 5.25 7.39 8.16 8.98 10.3
BTM 1,533 .674 .682 -.00733 .377 .566 .786 3.42
log�Leverage� 1,533 .502 .0898 .286 .444 .505 .565 .697
(b) Pre-Period Means and Differences for Firms (collapsed)
Treat Control Difference t-Statistic
(mean) count_guidance 1.433 1.284 0.150 0.65
(mean) d_guidance 0.563 0.605 -0.042 -0.60
(mean) n_topic 0.669 0.694 -0.026 -0.28
(mean) n_target 0.990 0.935 0.055 0.40
(mean) n_date 0.860 0.843 0.018 0.14
(mean) n_date_target_topic_r_bad_b0 0.269 0.331 -0.062 -0.70
(mean) n_date_target_topic_r_good_b0 0.338 0.257 0.080 0.85
(mean) ln_size_lag 8.555 7.938 0.617 3.23
(mean) btm_lag 0.501 0.820 -0.319 -2.39
(mean) ln_lev_lag 0.494 0.507 -0.013 -0.87
N_Firms 40.000 159.000 . .
Table 11: Estimates from the TRACE 120 Experiment: Effect of TRACE Dissemination on Dis-closure
This table contains difference-in-differences estimates based on the TRACE 120 experiment. In Panel A, I estimate how
TRACE dissemination affects the guidance frequency, Count_Guidance. In Panel B, I estimate how TRACE dissemination
affects the frequency of bad-news and good-news disclosures, where the news content is classified based on announcement
returns. I focus on unbundled management forecasts to ensure that the announcement return is attributable to the content
of the management forecasts rather than to the content of the earnings announcement. Errors are clustered at the firm and
year-quarter level. See Table A1 for variable definitions.
(a) Effect on the Management Forecast Frequency
(1) (2) (3) (4)
Count_Guidance Count_Guidance Count_Guidance Count_GuidancePost � Treat 0.385** 0.347* 0.377** 0.305
(2.55) (1.97) (2.78) (1.66)
Post 0.234 0.209 -0.055 -0.515**
(1.63) (1.44) (-0.34) (-2.56)
Treat -0.124 0.000 -0.094 0.000
(-0.58) (0.00) (-0.44) (.)
log�Size� 0.350*** 0.928*** 0.333** 0.433
(3.22) (3.84) (3.09) (1.75)
log�Leverage� -1.344 -3.736** -1.309 -3.830**
(-1.06) (-2.39) (-1.00) (-2.23)
BTM -0.127 0.078 -0.104 0.025
(-1.16) (1.41) (-1.04) (0.35)
Fixed Effects X Firm Qtr Firm Qtr
Cluster Firm Qtr Firm Qtr Firm Qtr Firm Qtr
Observations 1,533 1,532 1,533 1,532
R2 0.07 0.58 0.10 0.60
(b) Effect on the Management Forecast Frequency
(1) (2)
Count_Guidance_Bad Count_Guidance_GoodPost � Treat 0.239** 0.089
(2.42) (0.65)
Post -0.102* -0.017
(-1.88) (-0.42)
Treat 0.000 0.000
(0.00) (.)
log�Size� 0.158* 0.134
(1.81) (0.76)
log�Leverage� -0.909*** 0.930
(-4.81) (0.79)
BTM -0.032 0.008
(-0.69) (0.16)
Fixed Effects Firm Firm
Cluster Firm Qtr Firm Qtr
Observations 1,532 1,532
R2 0.310.26
Table 12: Exploiting Discrepancies in Firms’ TRACE Dissemination Related to the ThresholdRules Used to Determine Bonds’ Phase Membership: Effect of TRACE Dissemination on Disclo-sure
This table exploits discrepancies in firms’ treatment (TRACE dissemination) related to the threshold rules used to assign
bonds to phase groups. For illustration, for Phase 1, an investment-grade bond’s issue size triggers the treatment assignment.
A firm with one $1.2 billion investment-grade bond receives treatment while a firm with two $600 million investment-grade
bonds does not. Comparing these two firms holds constant the treatment-assigning variable (issue size conditional on rating)
at the firm level while yielding variation in the treatment status. Phases 2 and 3 have similar cutoff rules. See Section 7 for a
description of the estimation. Errors are clustered at the firm and year-quarter level. See Table A1 for variable definitions.
Stacked 1: no firm or qtr FE Stacked 2: firm FE Stacked 3: Qtr FE
(1) (2) (3)
Count_Guidance Count_Guidance Count_GuidancePost � Treat 0.403*** 0.414** 0.363**
(3.47) (2.40) (2.50)
Fixed Effects _post�cohort�bin_issue_size _treat�cohort�bin_issue_size Firm�cohort _post�cohort�bin_issue_size Firm�cohort Qtr�cohort�bin_issue_size
Cluster Firm Qtr Firm Qtr Firm Qtr
Observations 5,446 5,443 4,870
R2 0.29 0.64 0.69
Table 13: Effect of TRACE Dissemination on the Fineness of Reporting (Disaggregation Quality)
This table contains difference-in-differences estimates of the effect of market transparency on disaggregation quality. The
DQ measure is from Chen, Miao and Shevlin (2015) and measures the “fineness” of reporting as the modified proportion of
non-missing Compustat line items. The panel is defined at the firm-year level. Errors are clustered at the firm and year level.
See Table A1 for variable definitions.
(1) (2)
DQ DQ0.00
( )
TRACE_Dissemination 0.00 *
(2. )
log� Size� 0.005 0.003
(1.60) (0.86)
log�Leverage� -0.014 -0.036
(-0.46) (-1.80)
BTM -0.002 -0.005
(-0.45) (-1.14)
Fixed Effects Firm Year
Cluster Firm Year
Observations
R2 0.88
Firm Year_Ind
Firm Year
0.90
Table 14: Effect of TRACE Dissemination on the Number of All Forward-Looking Statements(FLS) from Bozanic et al. (2018)
This table contains difference-in-difference estimates of the effect of market transparency on the amount of forward-looking
statements, based on Bozanic et al.’s (2018) textual analysis to classify whether sentences contain forward-looking statements.
I thank the authors for sharing their data with me. Because the FLS data starts only in August 2004, the only phase introduction
date that is sufficiently covered is that of Phase 3B (in February, 2005). My difference-in-differences regression thus focuses
on the change in disclosure around the introduction of Phase 3B. The treatment group consists of firms whose prices/trading
become observable as part of Phase 3B, and the control group consists of firms whose price/trading observability remains
unchanged during the studied time window. As for the time window, in columns 1 and 2, I use symmetric one year-pre and
post-periods. However, the pre-period is implicitly truncated because the data availability for the pre-period begins only in
August 2004 (which is 6 months before the introduction date). In columns 3 and 4, I use symmetric 6-month windows instead.
One Year Windows (Truncated) Symmetric 6 Month Windows
(1) (2) (3) (4)
Count_FLS Count_All_Sentences Count_FLS Count_All_Sentences_post_treat 2.014** 10.327 2.571* 16.748*
(2.01) (1.45) (1.94) (1.67)
_post -0.658 -6.782 -2.199** -20.778**
(-0.88) (-1.21) (-2.02) (-2.34)
_treat 2.144* 9.215 1.553 3.412
(1.69) (1.08) (1.25) (0.41)
log�Size� 1.556** 15.042*** 1.366** 13.280***
(2.42) (3.12) (2.32) (3.15)
log�Leverage� 2.920 81.681* 1.222 63.197
(0.41) (1.93) (0.18) (1.61)
BTM 2.524* 20.814* 2.348* 19.790*
(1.76) (1.93) (1.67) (1.95)
Fixed Effects X X X X
Cluster Firm Firm Firm Firm
Observations 1,822 1,822 1,197 1,197
R2 0.02 0.04 0.02 0.04
Table 15: Tests of Specific Information Channels
In this table, I test the plausibility of potential channels as contributors to the TRACE effect. In Panel A, I test whether
TRACE affected firms’ litigious environment. In Panel B, I test cross-sectional predictions under the litigation, proprietary
cost, and uncertainty channels. In Panel C, I test the plausibility of the "increased bond financing channel" by estimating the
effect of TRACE on firms’ bond issuance. Variable Definitions: TRACE_Dissemination is a dummy variable indicating
if the firm has a bond for which TRACE dissemination has begun, i.e., if the firm’s bond trading and prices are observable.
log_Settlements is the log of one plus the dollar amount of settlements. D_Litigation_Bond is a dummy equal to one if
there is at least one case with bondholder involvement filed against the firm. D_Litigation_All equals one if there is at
least one case (with shareholder involvement) filed against the firm. D_High_Litigation_Risk is a dummy that indicates
above-median litigation risk using Kim and Skinner’s (2012) litigation risk model. D_High_Herfindahl is a dummy
that indicates above-median industry market concentration. log_V olumet�1 is the log of one plus the firm’s lagged bond
trading volume. log_V olume_Observablet�1 is defined similarly but uses only transactions that are disseminated through
TRACE. log_V olume_Unobservablet�1 uses only transactions that are not disseminated. Errors are clustered at the firm
and year-quarter level.
(a) Effect of TRACE on Realized Litigation
(1) (2) (3)
log_Settlements D_Litigation_Bond D_Litigation_AllTRACE_Dissemination 0.201* 0.004** 0.005
(2.07) (2.40) (0.74)
log�Size� 0.149** 0.000 0.007
(2.27) (0.06) (1.44)
log�Leverage� 0.469 0.002 0.039
(0.80) (0.16) (0.81)
BTM 0.007 -0.001 0.001
(0.27) (-0.37) (0.49)
Fixed Effects Firm Qtr Firm Qtr Firm Qtr
Cluster Firm Qtr Firm Qtr Firm Qtr
Observations 13,214 13,214 13,214
R2 0.09 0.10 0.09
(b) Cross-sectional Evidence for the Informational Channels
Litigation and Proprietary Costs Ex Post Trading Volume
(1) (2) (3) (4) (5)
Count_Guidance Count_Guidance Count_Guidance Count_Guidance Count_GuidanceTRACE_Dissemination � D_High_Litigation_Risk 0.166* 0.182*
(1.79) (1.97)
TRACE_Dissemination � D_High_Herfindahl 0.216* 0.233**
(2.03) (2.18)
TRACE_Dissemination � log_V olumet�1 0.015**
(2.14)
TRACE_Dissemination � log_V olume_Observablet�1 0.009*
(1.98)
TRACE_Dissemination � log_V olume_Unobservablet�1 0.005
(1.18)
TRACE_Dissemination 0.234*** 0.212*** 0.108 0.043 0.115
(2.87) (2.84) (1.27) (0.34) (1.13)
D_High_Litigation_Risk -0.093** -0.095*
(-2.11) (-2.07)
D_High_Herfindahl -0.056 -0.070
(-0.56) (-0.70)
log_V olumet�1 -0.004 -0.001
(-0.92) (-0.22)
Fixed Effects Firm Qtr Firm Qtr Firm Qtr Firm Qtr Firm Qtr
Cluster Firm Qtr Firm Qtr Firm Qtr Firm Qtr Firm Qtr
Observations 13,093 12,907 12,792 9,601 9,601
R2 0.54 0.55 0.54 0.59 0.59
(c) Effect of TRACE on Firms’ Bond Issuance
(1) (2) (3) (4)
d_bond_issuance ln_bond_issuance_raw offering_yield_issue_mean lev
TRACE_Dissemination 0.004 0.044 -0.002 0.001
(0.36) (0.28) (-0.02) (0.27)
log�Size� 0.039*** 0.501*** -1.606*** -0.031***
(5.65) (5.69) (-5.64) (-3.85)
log�Leverage� 0.244*** 3.130*** 1.913 1.585***
(4.04) (3.89) (1.05) (10.79)
BTM 0.006** 0.080** -0.081 -0.012
(2.64) (2.81) (-0.15) (-1.42)
Fixed Effects Firm Qtr Firm Qtr Firm Qtr Firm Qtr
Cluster Firm Qtr Firm Qtr Firm Qtr Firm Qtr
Observations 13,214 13,214 563 13,214
R2 0.13 0.13 0.75 0.90
Table A1: Variable Descriptions
Main Dependent VariableCount_Guidance Count_Guidance is the forecast frequency, i.e., the count of management forecasts in a
given quarter. The variable is computed as the number of distinct (Topic)x(Date)x(TargetMonth) combinations. Topic refers to the content-category of the management forecast
(“Earnings”, “Sales”, “CapEx”, or “Other”). Date refers to the date on which the forecast
was made. Target Month refers to the forecasted month (e.g., the fiscal period ending in
December 2004 and December 2005).
Treatment VariableTRACE_Dissemination TRACE_Dissemination indicates whether the firm’s bond price/trading is observable
through TRACE. TRACE_Dissemination is defined as a dummy variable equal to one
if the firm has a bond for which TRACE dissemination has begun, and to zero otherwise.
Additional Disclosure VariablesD_Guidance D_Guidance is a dummy variable indicating whether the firm-quarter has at least one
management forecast.
N_Topic N_Topic is the number of distinct topics forecasted in the quarter. For example, if there
are management forecasts about “Earnings” and about “CapEx”, then N_Topic takes the
value two.
N_Date N_Date is the number of distinct forecast dates in the quarter. For example, if the firm
provides forecasts on February 28 and March 31, then N_Date takes the value two.
N_Target N_Target is the number of distinct target months to which the forecasts refer. For ex-
ample, if management provides forecasts for the quarters ending in March 2005 and June
2005, then N_Target takes the value two.
DQ DQ is the disaggregation quality (or disclosure quality) measure from Chen, Miao and
Shevlin (2015). The measure is constructed as the modified proportion of non-missing
Compustat line items in the firm’s financial reports (income statement and balance sheet).
The measure captures the “fineness” at which firms report their financial information.
Table A1: Variable Descriptions (Continued)
Other VariablesSize Size is the firm’s lagged market capitalization. Size is inflation-adjusted and expressed in
2014-2016 US dollars.
BTM BTM is the lagged book-to-market ratio, defined as the book value of common equity
divided by the market equity of the firm’s common shares.
Leverage Leverage is the lagged book leverage, defined as the ratio of total liabilities to total assets.
D_Junk D_Junk is a dummy variable that equals one if the firm has a bond rated BB or below and
zero otherwise. I use ratings from Moody’s, Standard and Poor’s, and Fitch (in that order
of preference) to maximize the rating coverage of firms.
Rating Rating is the firm’s bond rating. I use ratings from Moody’s, Standard and Poor’s, and
Fitch (in that order of preference) to maximize the rating coverage of firms. The rating is
expressed as a number: 1 refers to a rating of AAA, 2 to a rating of AA, 3 to a rating of A,
and so on. If a firm has multiple bonds with different ratings, the median rating is used.
Ret_Announcement Ret_Announcement is the portion of the firm-quarter’s return that accrues around unbun-
dled announcement days. I use (-1,+1) windows around the management forecast date. If
there are multiple forecast dates, I compound the announcement returns such that no day
is double-counted.
Ret_Fisc Ret_Fisc is the firm-quarter’s total stock return.
Ret_NonAnnouncement Ret_NonAnnouncement is the portion of the firm-quarter’s return that accrues on non-
announcement days. It is the difference between the total fiscal quarter return, Ret_Fiscand Ret_Nonannouncement.
log_Settlements log_Settlements is defined as the log of 1 plus the dollar amount of settlements paid for
cases filed in the quarter.
D_Litigation_Bond D_Litigation_Bond is a dummy variable equal to one if a case with bondholder partic-
ipation was filed in the quarter. A limitation of my litigation data is that it contains only
cases with shareholder participation (which applies to most bondholder cases). I plan to
collect additional data on securities litigation involving only bondholders in the future.
D_Litigation_All D_Litigation_All is a dummy variable equal to one if at least one case (with shareholder
participation) was filed in the quarter.
D_Bond_Issuance D_Bond_Issuance is a dummy variable equal to one if the firm issues at least one bond
during the quarter.
log_Bond_Issuance log_Bond_Issuance is defined as the log of 1 plus the total dollar amount raised from
standard bond offerings during the quarter.
