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Voluntary disclosure, mandatory disclosure, and cost of capital*

Jing He Assistant Professor of Accounting Department of Accounting & MIS

University of Delaware Jinghe@udel.edu

Marlene A. Plumlee1

Professor of Accounting David Eccles School of Business

University of Utah Marlene.plumlee@utah.edu

Jennifer (He) Wen

Assistant Professor of Accounting Department of Accounting

University of Missouri-St. Louis Wenhe@umsl.edu

May 2018

Abstract We examine the association between firms’ cost of capital and their voluntary and mandatory disclosures. We incorporate two mandatory disclosure types that arise within financial reporting environments: mandatory disclosure of periodic reports that are realizations of ex-ante reporting systems (periodic mandatory disclosures) and mandatory disclosure of specific corporate events (event-driven mandatory disclosures). To capture firms’ voluntary and event-driven mandatory disclosure quality, we employ information firms provide via 8Ks filings. To capture period mandatory disclosures, we employ earnings quality measures derived from the literature. Consistent with endogenous relations predicted by theory, we document that voluntary disclosure and both types of mandatory disclosure are correlated, although only event-driven mandatory disclosures are significant in models that explain voluntary disclosure. We find that the cost of capital is influenced by these disclosures, in separate regressions as well as when they are incorporated in a single model. These inferences are largely unchanged when all three aspects of firms’ disclosures are included in a single model. We find that voluntary disclosure is related to both types of mandatory disclosure, although controlling for them does not impact the association between voluntary disclosure and cost of capital. Also, event-driven mandatory disclosures are significant aspects of firms’ reporting environment. KEYWORDS Voluntary disclosure, mandatory disclosure, event-driven mandatory disclosure, periodic mandatory disclosure, earnings quality, cost of capital

1 Corresponding author. * We appreciate very helpful discussions with Rachel Hayes, Beatrice Michaeli, Xiaoxia Peng, and Jordan Schoenfeld and comments from Michael Neel (AAA discussant), David Plumlee, Peter Pope (the editor), three anonymous reviewers, and participants at the 2017 AAA annual meeting.

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1. INTRODUCTION We examine the association between voluntary and mandatory disclosure, and the conditional and

unconditional pricing effects of voluntary disclosure, given these associations. Our analysis includes

voluntary disclosure and two types of mandatory disclosure: periodic mandatory reports produced by

firms’ ex-ante reporting system and event-driven mandatory filings required when specific corporate or

firm events occur. Our proxy for periodic mandatory reports is the common factor generated by factor

analysis performed on three commonly used earnings quality measures. We follow Francis et al. (2008) in

constructing this measure and concur with their view of earnings quality as capturing “the precision of the

earnings signal emanating from the firm’s financial reporting system” (pg. 54) Our primary proxies for

voluntary disclosure and event-driven mandatory disclosure are based on a technique developed and

validated in Cooper, He, & Plumlee (2018) (CHP hereafter), that uniquely partitions information from

firms’ 8-K filings into information required by the SEC to be disclosed and voluntary information. This

finer partitioning of firms’ disclosures allows a deeper examination of the relationships among earnings

quality, cost of capital and financial disclosure.

Whether mandatory and voluntary disclosure are complements or substitutes represents an

ongoing issue in the analytical disclosure literature. For example, some analyses show that the link

between voluntary and mandatory disclosure is conditional on whether the disclosure has either financial

or real externalities (Dye, 1990), the purpose of the mandatory disclosure (Gigler & Hemmer, 1998), and

the features of the mandatory disclosure environment (Einhorn, 2005). Other studies analyze settings

where the structure of the mandatory disclosure system is endogenously determined, based on

expectations around subsequent receipt of a private signal (e.g., Stocken & Verrecchia, 2004; Freidman,

Hughes, & Michaeli, 2018a). As we discuss in Section 2, conclusions from this literature lead to

conflicting expectations about the expected relationship between voluntary and mandatory disclosure, due

in part to assumptions regarding endogeneity of the mandated disclosures. This body of work motivates

our first two hypotheses. A related issue is the link between voluntary disclosure and a firm’s cost of

capital. Many theoretical and empirical studies examine the association between a firm’s cost of capital

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and the quality of its disclosure.2 The findings from these literatures are also somewhat mixed, though

they are generally consistent with voluntary disclosure being inversely related to the cost of capital. These

findings motivate our third hypothesis.

An unresolved issue in the literature is whether studies that provide evidence of the link between

voluntary disclosure and the cost of capital capture the impact of voluntary disclosure or whether the

voluntary disclosure measures capture more primitive aspects of firms’ reporting environment. The

concern is that, while an empirical link between voluntary disclosure and cost of capital is established, the

true relationship is between the underlying aspects of the reporting environment that are correlated with

voluntary disclosure. For example, several empirical studies explore how the inclusion of earnings quality

in the analysis impacts the association between voluntary disclosure and the cost of capital (e.g., Francis

et al., 2008; Bhattacharya et al., 2012; Heflin, Moon, & Wallace, 2016).3 We expand this research by

explicitly examining how firms’ mandatory disclosure is related to voluntary disclosure and how

controlling for mandatory disclosure affects our understanding of the voluntary disclosure/cost of capital

link. A unique aspect of our analysis is the inclusion of two types of mandatory disclosure.

Our empirical analysis begins by examining the relationships between voluntary disclosure and

periodic mandatory disclosure, and event-driven mandatory disclosure. We document positive

correlations between voluntary disclosure and both types of mandatory disclosure. Once we control for

firm characteristics expected to predict voluntary disclosure, however, the link between periodic

mandatory disclosure and voluntary disclosure becomes insignificant. Event-driven mandatory disclosure

continues to provide significant explanatory power. In contrast to the findings in Francis et al. (2008), our

results suggest that voluntary disclosures are unrelated to the quality of a firm’s mandatory reporting of

periodic events, instead they appear to be responses to firm specific corporate events that trigger

mandatory disclosure. We also find that greater voluntary disclosure is associated with the cost of capital, 2 For example, the theoretical literature includes Diamond & Verrecchia (1991), Kim & Verrecchia (1994), Zhang (2001), Bertomeu, Beyer, & Dye (2011), Cheynel (2013), Clinch & Verrecchia (2015). Empirical studies in this literature include Botosan & Plumlee (2002), Gietzmann & Ireland (2005), Hail & Leuz (2006), Francis, Nanda, & Olsson (2008), Baginski & Rakow (2012), Bhattacharya, Ecker, Olsson, & Schipper (2012). This is only a partial list. 3 In these studies, earnings quality proxies for the information environment.

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although the sign of the association varies by the cost of capital measure (implied cost of equity, bid-ask

spread, or cost of debt) and voluntary disclosure measure (8-K-based measure or management forecasts)

employed. When we use the implied cost of equity or cost of debt to capture the cost of capital and our

primary measure of voluntary disclosure, the unconditional association with the cost of capital is positive

or insignificant. 4 When we use bid-ask spreads to proxy for cost of capital or rely on management

forecasts to capture voluntary disclosure, the unconditional relation between voluntary disclosure and the

cost of capital is significantly negative. We also document that firms with a greater number of event-

driven mandatory filings have higher costs of capital, and that firms with higher quality periodic

mandatory disclosure have lower cost of capital, regardless of the cost of capital proxy employed.

When we include all three disclosure variables in the model, we find that doing so does not affect

the unconditional relation between the mandatory disclosure proxies and the various measures of the cost

of capital. In most cases, we also find that the inclusion of the either or both of the mandatory disclosure

proxies does not impact the unconditional relation between voluntary disclosure and the cost of capital.

These findings are consistent with voluntary and mandatory disclosures having an impact on firms’ cost

of capital, but do not support the contention that earnings quality subsumes the voluntary disclosure

quality and that excluding earnings quality in the analysis leads erroneous conclusions. Ultimately, our

findings extend those in Francis et al. (2008) and Heflin et al. (2016). We show that even though periodic

mandatory disclosures and event-driven mandatory disclosures are correlated with voluntary disclosures

and with the cost of capital, the documented association between voluntary disclosure and the cost of

capital are unaffected by the inclusion of the mandatory disclosure variables.

Our study contributes to the literature in several distinct ways. First, we link earnings quality and

periodic mandatory disclosure, which forms a more comprehensive framework within which to examine

the associations between firm disclosure and firms’ cost of capital. Second, we increase our understanding 4 Documenting a positive association between cost of capital and voluntary disclosures is inconsistent with many theoretical models, which generally predict a negative relation between voluntary disclosure and cost of equity capital due to a reduction in information asymmetry (e.g., Amihud & Mendelson, 1986; Diamond & Verrecchia, 1991) or in estimation risk (e.g., Barry & Brown, 1985). If, however, public information is a complement rather than a substitute for private-information gathering, more public information will reduce market liquidity as it intensifies adverse selection some models (e.g., Kim & Verrecchia, 1994).

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of the link between voluntary and mandatory disclosure by providing empirical evidence of these

relationships. Third, we expand the literature around the conditional impact of voluntary disclosure on the

cost capital by explicitly examining how firms’ mandatory disclosure, partitioned into periodic mandatory

disclosure and event-driven mandatory disclosure, is related to voluntary disclosure and how controlling

for both types of mandatory disclosure affect our understanding of the voluntary disclosure/cost of capital

link. Fourth, we demonstrate that the relation between voluntary disclosure and the cost of capital is

robust to the inclusion of both types of mandatory disclosure. Ultimately, even though mandatory

disclosure is associated with both voluntary disclosure and the cost of capital, it does not subsume the

relation between voluntary disclosure and the cost of capital. Finally, we illustrate how CHP’s 8K-based

voluntary and mandatory disclosure measures can be used to enhance our understanding of firms’

disclosure environments.

The rest of the paper proceeds as follows. Section 2 discusses the analytical literature that

investigates the relation between voluntary and mandatory disclosure and the relation between disclosure

and a firm’s cost of capital. Based on these models, we generate testable hypotheses. Sections 3 and 4

describe the primary empirical proxies (disclosure measures and the cost of capital measures) and

describe the sample and presents descriptive statistics. Section 5 presents our empirical results. Section 6

presents robustness tests and section 7 concludes.

2. HYPOTHESES ON THE RELATIONS AMONG VOLUNTARY DISCLOSURE,

MANDATORY DISCLOSURE, AND THE COST OF CAPITAL The purpose of this study is to provide empirical evidence that enhances our understanding of the role of

voluntary disclosure in a firm’s cost of equity capital, conditional on the endogenous nature of voluntary

disclosure. Our examination is motivated by and builds on the discussion and analysis provided by

Francis et al. (2008). We begin with a review of papers that examine the interdependencies between a

firm’s voluntary and mandatory disclosures. Our analysis departs from prior work by explicitly

considering two mandatory disclosure types that arise within the financial reporting environment: periodic

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reports that are a realization of ex-ante reporting systems and event-driven mandatory filings. We rely on

several theoretical studies (e.g., Gietzmann & Trombetta, 2003; Jorgensen & Kirschenheiter, 2003;

Bertomeu & Magee, 2015; Friedman, Hughes, & Michaeli, 2018a) as a basis for our arguments. After we

describe the expected relations between voluntary disclosure and the two aspects of mandatory disclosure,

we detail our predictions of the association between cost of capital and the quality of firms’ disclosures,

conditional on the endogenous nature of the voluntary disclosure.

(i) Voluntary and Mandatory Disclosures

A broad theoretical literature explores the expected association between the quality of voluntary

disclosures and the quality of a firm’s information environment. Early work, which views voluntary

disclosure quality as exogenous, finds that voluntary disclosure is used to mitigate information

asymmetry (e.g., Grossman 1981). Treating voluntary disclosure quality as exogenous, however, runs

counter to intuition and evidence that suggests that the quality of voluntary disclosure is likely to be

related to the underlying information environment (e.g., Coller & Yohn, 1997; Chen, Defond, & Park,

2002) A number of theoretical studies take various approaches to modeling the interdependencies

between voluntary disclosure quality and the quality of the underlying information environment. In some

settings, the models demonstrate that an increase in the quality of the information environment leads to an

increase in the probability of voluntary disclosure.5 The complementary relationship is driven by the

market’s inference when a manager does not disclose private information; when the manager is more

likely to be informed or has higher quality information, the market is more likely to interpret

nondisclosure as bad news (e.g., Dye, 1985; Verrecchia, 1990). Thus, a firm’s disclosure threshold—i.e.,

the lowest realization the manager is willing to disclose—decreases as the quality of the information

environment increases, and firms with higher (lower) quality information disclose more (less). This

complementary association between disclosure and the quality of the information system is not universal,

5 This literature includes Verrecchia (1983, 1990), Dye (1985), Einhorn (2005).

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however. Penno (1997) shows that if the probability a manager is informed is lower when the information

quality is higher, disclosure and information quality may serve as substitutes. The paper considers several

variations of the relation between information quality and the probability of being informed and shows

that under multiple scenarios there are regions where voluntary disclosure and information quality will be

substitutes.

Several theoretical studies endogenize the process by which mandatory disclosures are generated,

with a focus on the determination of the disclosure rules and of the reporting systems that produce

disclosures (e.g., Stocken & Verrecchia, 2004; Bertomeu & Magee, 2015; Friedman et al. 2018a, 2018b).

Stocken & Verrecchia (2004) show that when managers anticipate they will have private information that

is not captured within the firms’ financial reporting system and can manipulate the financial reports, they

may not choose the most precise reporting system. Bertomeu & Magee (2015) first model a setting where

mandatory disclosure policies are formed in response to managers’ preferences and political

compromise.6 They demonstrate that the resulting policies mandate asymmetric reporting and that

“certain adverse (below the median) news” be disclosed. Next they expand the model to include the

presence of a voluntary disclosure channel and explore the impact it might have on both the endogenously

determined mandatory disclosure policies and voluntary disclosure. The expanded model predicts the (1)

survival of the asymmetric nature of the mandatory disclosure rules and (2) substitutive nature of

voluntary and mandatory disclosure. Similarly, Friedman et al. (2018a) model the endogenous

determination of firms’ reporting systems and voluntary disclosures. Their study focuses on the ex ante

design of the reporting system when managers have expectations about the receipt of private information

and its disclosure subsequent to the release of realized values from the reporting system. In this setting,

the likelihood a firm provides voluntary disclosure is linked to the properties of its reporting system,

which is in turn linked to the probability that the firm receives private information. Specifically, they find

that firms with a higher (lower) probability of receiving and disclosing private information could select a

6 As noted by the authors, the assumptions that underlie this model – including the managers’ influence on the policies obtained – are consistent with “stylized facts of the rule-setting process in accounting.”

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less (more) informative reporting system. These studies provide analysis that link mandatory disclosure

policies and reporting systems, which we contend are related to a specific type of mandatory disclosures.

We draw from these models and current financial reporting outcomes to motivate the examination

of two types of mandatory disclosure—disclosure based on periodic reports that are a realization of an ex-

ante reporting system and disclosure of specific events. Bertomeu & Magee (2015) and Friedman et al.

(2018a) analyze links with mandatory disclosure policies and reporting systems, which we contend are

more closely related to the first type of mandatory disclosure. Empirically, we provide independent

proxies for the quality of each mandatory disclosure type, and investigate the relations between voluntary

and mandatory disclosure, conditional on the mandatory disclosure type.7

We present hypotheses on the expected relation between voluntary disclosure and both types of

mandatory disclosure below. The first hypothesis draws on the findings from Bertomeu & Magee (2015)

and Friedman et al. (2018a) and predicts a substitutive association between a firm’s periodic mandatory

disclosure and its voluntary disclosure:

H1a: Voluntary disclosures are decreasing in the quality of the periodic mandatory reports provided by a firm’s accounting system.

The second hypothesis predicts an association between mandatory disclosures triggered by to

specific corporate events and voluntary disclosure. It is less clear from the literature whether this type of

7 This perspective is supported both theoretically and empirically. For example, Gigler & Hemmer (1998) emphasize the “confirmatory” role of mandatory reports, where mandatory disclosures are “verifiable but noisy and, possibly, late signals” of managers’ private information. Friedman et al. (2018a) describe a setting where voluntary disclosure is influenced by the realized reports generated by the reporting system. These descriptions comport well with reported earnings. Empirical analysis that documents a link between mandatory and voluntary disclosures centers on earnings-quality-related mandatory disclosures (e.g., Francis et al., 2008; Ball, Jayaraman, & Shivakumar, 2012; Li & Yang, 2016). Francis et al. (2008) examine the link between an accruals-based earnings quality measure and voluntary disclosure; their primary findings are consistent with a complementary relation between mandatory and voluntary disclosure. Ball et al. (2012) frame their empirical analysis as an examination of the “confirmation hypothesis”, where audited financial statements increase the credibility of voluntary disclosures. Consistent with the predications provided by Gigler & Hemmer (1998), Ball et al. predict and find a complementary association between mandatory and voluntary disclosures. Finally, Li & Yang (2016) use changes in the reporting system due to the adoption of IFRS to explore the impact of mandatory disclosure on voluntary disclosures. They examine the channels by which IFRS adoption might impact voluntary disclosures, including reduced pressure to provide voluntary disclosure when more is known a priori about a firm due to higher quality mandatory reporting (Verrecchia, 1990). They document an increase in voluntary disclosure after the adoption of IFRS, inconsistent with the substitutive relation predicted by Verrecchia (1990). Each of these studies employs mandatory disclosure proxies that are based on the outcome of a reporting system: earnings quality, audited financial statements, and the adoption of IFRS.

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mandatory disclosure will have a complementary or substitutive association with voluntary, however.

Thus, we predict an association although we do not provide an expectation of the sign of the relationship.

H1b: Voluntary disclosures are associated with the quality of the event-driven mandatory reports.

(ii) Voluntary Disclosure and the Cost of Capital

A sizable body of theoretical research supports a link between the cost of capital and disclosure. One

stream of this literature suggests that, in a market with differentially-informed investors, adverse selection

impacts the cost of capital. To reduce this influence firms are willing to commit to higher levels of

disclosure to reduce information asymmetry between investors and firms’ cost of capital (e.g., Grossman,

1981; Verrecchia, 1983; Diamond & Verrecchia, 1991; Easley & O’Hara, 2004). Another stream of

literature suggests that estimation risk drives the link between cost of capital and disclosure. In this

setting, information improves investors’ assessments of the parameters of the distribution of the future

payoffs, which reduces the uncertainty of those payoffs and will lead to a decrease in the cost of capital

(Barry & Brown, 1985; Coles & Lowenstein, 1988; Coles, Lowenstein, & Suay, 1995). Cheynel (2013)

shows that firms that voluntary disclose have lower costs of capital than firms that do not. In contrast with

much of the theoretical literature, this negative association exists in the cross-section of expected returns,

consistent with empirical models frequently employed.

Regardless of the mechanism that links disclosure and the cost of capital, many of the prior

findings demonstrate that firms with more disclosure will face a lower cost of capital than firms with less

disclosure. This prediction is not universal, however. For example, Kim & Verrecchia (1991) show that

the opposite relation might exist: firms with more disclosure have a higher cost of capital. In their setting,

increased disclosure motivates investors to seek additional private information, which leads to an increase

in information asymmetry between informed and uninformed investors and a higher cost of capital.

Another analytical study (Zhang, 2001) demonstrates that, conditional on the setting, the link between the

cost of capital and disclosure could be positive or negative, depending on the factors that cause variation

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in disclosure levels. His model suggests that a negative association between disclosure level and the cost

of capital exists only when variation in disclosure is due to disclosure costs. When the primary drivers of

variation in disclosure are other factors (e.g., variability in earnings or variability in liquidity shocks) a

positive association between disclosure levels and the cost of capital will obtain.8

Prior empirical studies that examine the relation between voluntary disclosures and the cost of

capital generally support a negative association, although this is not always the case. Several studies

examine the link between voluntary disclosures based on AIMR scores and the cost of capital—both

equity and debt (e.g., Welker, 1995; Sengupta, 1998; Botosan & Plumlee, 2002; Heflin et al. 2016).

Botosan & Plumlee (2002) regress the implied cost of equity on total AIMR scores and three related

AIMR component scores that capture three aspects of a firm’s disclosures (annual report, other

publications, and investor relations). They fail to document a significant relation when total scores are

used to capture voluntary disclosure. When they employ the component scores to capture voluntary

disclosure, however, the quality of the annual reports (other publications) is negatively (positively)

associated with the cost of capital. Heflin et al. (2016) replicate this analysis using a subset of the Botosan

& Plumlee sample and document similar results. Sengupta (1998) and Heflin et al. (2016) document a

negative association between the cost of debt and AIMR scores. In addition, Botosan (1997) and Francis

et al. (2008) use hand-collected data from 10-Ks and document that more forthcoming firms have lower

cost of capital, although Botosan (1997) documents this relation only for firms with low analyst following

and Francis et al.’s findings disappear or are weaker when they control for earnings quality.

We note that when alternative proxies for the cost of capital (e.g., bid-ask spreads, illiquidity

measures) are employed, the findings are generally consistent with a negative association between these

measures and proxies for higher quality disclosure (e.g., Welker, 1995; Coller & Yohn, 1997; Heflin et al.,

2016). The theoretical and empirical findings from prior studies are not clear-cut, with analytical and

8 There also are several theoretical studies that argue that information asymmetry is diversifiable in large economies and should not affect the cost of capital (e.g., Hughes, Liu, & Liu, 2007; Christensen, de la Rosa, & Feltham 2010). The “no link” result is frequently limited to settings with perfectly competitive markets.

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empirical evidence of both negative and positive relations. Our hypothesis on the association between

disclosure and cost of capital is unsigned.

H2: Variation in the level of a firm’s voluntary disclosure is associated with variation in its cost of capital.

In the next section we discuss the expected associations between cost of capital and voluntary disclosure,

conditional on two types of mandatory disclosure.

(iii) Mandatory Disclosure and the Cost of Capital

As discussed in section (i), whether managers decide to provide less, or more, voluntary disclosure when

faced with higher quality or more mandatory disclosure hinges on whether voluntary disclosure serves as

a substitute for or complement to mandatory disclosure. Regardless of the underlying relation, voluntary

disclosure is generally portrayed as a response to information quality, which leads to a prediction that

voluntary disclosure has a second-order effect on the cost of capital. Consistent with this intuition, we

expect voluntary disclosure to have a second-order effect on the cost of capital, although we look to the

periodic mandatory reports provided by a firm’s accounting system as providing the first-order effect. We

rely on the same empirical proxy (earnings quality) as Francis et al. (2008) to capture the quality of

mandatory reports provided by a firm’s accounting system; we argue that earnings quality reflects firms’

underlying information quality that is reflected in firms’ periodic mandatory reports.9 We also expect

event-driven mandatory disclosures have a first-order effect.10 As discussed earlier, we predict that the

quality of periodic mandatory reports provided by a firm’s accounting system and voluntary disclosure

have a substitutive relation. Thus, even if voluntary disclosure is negatively associated with the cost of

capital, an empirical model that fails to control for the quality of periodic mandatory reports could report

a positive relation between voluntary disclosure and the cost of capital due to the negative correlation 9 This view is consistent with prior studies, including Ball et al. (2012) and Li & Yang (2016). In addition, Francis et al. (2008) state that earnings quality is “the precision of the earnings signal emanating from the firm’s financial reporting system”. 10 Consistent with the potential role of event-driven mandatory disclosures in pricing, McMullin, Miller, & Twedt (2018) document that price formation improves subsequent to changes in the SEC’s reporting requirements around firm events.

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between the omitted variable and voluntary disclosure. Additionally, failing to control for the quality of

event-driven mandatory disclosures could influence the documented association between voluntary

disclosure and the cost of capital. In this case, we predict an association between voluntary disclosure and

event-driven mandatory disclosures, although it is unclear whether these disclosures serve as

complements or substitutes. If voluntary disclosure and event-driven mandatory disclosure have a

substitutive relation, the impact described above would again obtain. If, however, voluntary disclosure

and event-driven mandatory disclosure serve as complements then increased mandatory disclosure would

lead to increased voluntary disclosure. In this case, failing to control for the first-order effect of

mandatory disclosure in a setting where voluntary disclosure is negatively associated with the cost of

capital would still lead to a negative relation. Even so, without including the appropriate control for event-

driven mandatory disclosure we are unable to assess whether the documented relation is due to voluntary

disclosure or the omitted variable. Ultimately, we are unable to predict the conditional relation between

voluntary disclosure and the cost of capital. Irrespective of the association between voluntary disclosure

and periodic mandatory disclosure and event-driven mandatory disclosure, however, providing empirical

evidence of the association between voluntary disclosure and the cost of capital requires the inclusion of

both types of mandatory disclosure. We state our formal prediction of the relation between voluntary

disclosure and the cost of capital, conditional on mandatory disclosure as:

H3: Controlling for the level of periodic mandatory disclosure and event-driven mandatory disclosure, variation in a firm’s voluntary disclosure is associated with variation in its cost of capital.

3. EMPIRICAL MEASURES OF DISCLOSURE AND COST OF CAPITAL

In this section we detail the construction of the primary empirical proxies used in our analysis: voluntary

disclosure, both types of mandatory disclosure, and the cost of capital. Section (i) below provides a

discussion of our voluntary and event-driven mandatory disclosure proxies, which are based on data from

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firms’ 8-K filings. Sections (ii) and (iii) describe the proxies for periodic mandatory disclosure and the

cost of capital, respectively.

(i) Voluntary and Event-Driven Mandatory Disclosure

Research that examines voluntary disclosure issues uses a number of proxies for firm-level voluntary

disclosure, including (1) management earnings forecasts, (2) conference calls, (3) non-GAAP earnings,

(4) scores based on analysts’ or researchers’ assessments, and (5) 8-K-based proxies.11 We elect to use an

8-K-based voluntary disclosure proxy developed and validated in CHP in our primary analysis for the

following reasons. First, the CHP measure is available for a broad range of firms (publicly-listed firms

over a twelve-year period) and therefore, the sample is not restricted to firms selected by external data

providers or limited to firms covered by analysts or selected by researchers. It also is based on a broad

range of voluntary disclosures that are reported in 8-K filings instead of relying on disclosures provided

through a single channel (i.e., management earnings forecasts, conference calls, or non-GAAP earnings).

Limiting the analysis to a single disclosure channel ignores other firm disclosures that could be

substitutes, which could lead to potential bias toward firms that choose one channel to communicate

versus another. Third, CHP also develop an 8-K-based measure of mandatory disclosure, which serves as

our proxy form event-driven mandatory disclosure. Unlike the voluntary disclosure literature, there are

fewer obvious candidates to serve as proxies for event-driven mandatory disclosure.

CHP calculate their disclosure measures for all publicly-listed firms that file 8-Ks with the

Securities and Exchange Commission (SEC) between 2005 and 2016. As noted by Carter & Soo (1999)

and Lerman & Livnat (2010) and exploited by CHP, information reported in 8-K filings can be

categorized as (1) mandatory disclosures (disclosures about corporate events the SEC requires firms to

report if the event “happens” to the firm) or (2) voluntary disclosures that reflect information firms choose

11 See (1) Baginski & Rakow, 2012, (2) Tasker, 1998; Brown, Lo, & Hillegeist. 2004; (3) Brown, Christensen, Elliott, & Mergenthaler, 2012, (4) Lang & Lundholm 1993; Botosan & Plumlee, 2002; Francis et al., 2008 and (5) Leuz & Schrand 2009; Schoenfeld 2017; Bourveau, Lou, & Wang 2018.

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to disclose that the SEC then requires them to report in an 8-K. Relying on the SEC’s 8-K filing

requirements and the findings in Lerman & Livant (2010), CHP classify 8K item numbers reported within

8-K filings into those that are related to events that the SEC specifies be disclosed via an 8K (mandatory

disclosures) and other disclosures where the manager has greater discretion (voluntary disclosures).

Specifically, CHP classify items 2.02 (Results of Operations and Financial Condition), 7.01 (Regulation

FD Disclosure), and 8.01 (Other Important Events) as voluntary. All other item numbers are classified as

mandatory.12 We use their variable VolDisc_Ct, the total number of items and exhibits classified as

voluntary that are reported in a firm’s 8Ks issued during a fiscal year, as our proxy for voluntary

disclosure (VD_8K) and their variable ManDisc_Ct, as our proxy for event-driven mandatory disclosure

(MD_Event). Appendix A provides detailed descriptions of these measures.

We perform additional analysis using management forecasts (VD_MF) to proxy for voluntary

disclosure, a proxy that is widely used in the literature. VD_MF equals the log of the sum of the values

assigned to each management forecast for all forecasts issued during the fiscal year. Each forecast was

assigned a value based on the forecast form: one for qualitative forecasts, two for range forecasts and

open-ended forecasts, and three for point forecasts (Francis et al., 2008).

(ii) Periodic Mandatory Disclosure

A number of empirical studies use earnings quality as a proxy for information quality or

information risk, which links information quality to the quality of the firm’s reporting system.13 We take

this notion a step further and assert that information quality is a function of information generated by the

financial reporting system, such that earnings quality proxies for mandatory disclosure related to the

reporting system used to generate periodic reports.

12 See Cooper, He, and Plumlee (2018) available at https://ssrn.com/abstract=2640722 for additional details. 13 Francis et al. (2008) state, “we view our earnings quality variable as a proxy for the information quality of the firm’s financial information system” (footnote 1, pg. 54). Bhattacharya, Ecker, Olsson, & Schipper (2012) examine the direct and mediated paths by which earnings quality impacts the cost equity, where earnings quality serves “as a proxy for information risk.”

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Consistent with the literature and Francis et al. (2008), we calculate three related measures of

earnings quality (accrual quality (EQAQ), earnings variability (EQEV), and absolute abnormal accruals

(EQAbsAA)). We then calculate EQCF, a common factor score obtained from a factor analysis of these three

measures.14 EQCF is our proxy for the quality of the mandatory disclosures generated from the reporting

system—MD_EQ.

(iii) Cost of Capital

We include three implied cost of equity measures (rMPEG, rDIV, rSTPEG) for use in our analyses, consistent

with prior studies in this literature. rMPEG is based on the modified price-earnings-growth ratio method,

following the procedure detailed in Easton (2004). rDIV is estimated based on the target price method

identified in several studies (e.g., Botosan, Plumlee, & Wen 2011; Botosan & Plumlee, 2013). We use the

I/B/E/S 12-month ahead target price forecast, which mirrors the process employed in Francis et al. (2008)

and Heflin et al. (2016).15

We rely on the findings in Botosan et al. (2011) to support the use of the implied cost of capital

measures above. Nonetheless, we recognize that the literature has failed to reach consensus on the best

measure of the cost of capital. Consequently, we include additional analysis using alternative cost of

capital measures: bid-ask spread and the cost of debt. Bid-ask spread (AvgBAS) is calculated as the

average daily quoted bid-ask spread over a fiscal year. Our proxy for the cost of debt (SPRating) is the

14 Specifically, EQAQ, is the standard deviation of residuals from a regression of total current accruals on cash flow from operations, change in revenues, and gross value of property, plant and equipment using data from year t-10 to year t (e.g., the data from 1995-2005 is used to calculate the 2005 EQAQ). EQEV, is the standard deviation of earnings before extraordinary items scaled by total assets. Data from year t-9 to year t is used to estimate the measure for year t. EQAbsAA, is based on modified Jones (1991) approach. We follow Francis et al. (2008) and use a ten-year rolling data (from year t-9 to year t) to generate average absolute abnormal accruals, |AAj|. Thus, EQAbsAA for 2005 is the firm-level average |AAj,t| over the estimation period (1996-2005). EQCF, is the common factor score obtained from a factor analysis of EQAQ, EQEV, and EQAbsAA. We multiply this value by a negative one, so a higher value of earnings quality indicates higher quality mandatory disclosure. See Appendix B for a detailed description of these measures. 15 Prior studies use Value Line three-to-five-year-ahead target price forecasts to capture the firm terminal value, while we use I/B/E/S analysts’ target price one-year-ahead forecasts, due to data availability. These values are qualitatively the same as the rDIV measure employed in Francis et al. (2008), which allows for a meaningful comparison between our results and Francis et al. and related studies.

16

Standard and Poor’s domestic long-term issuer credit rating. Details of constructing these variables are

provided in Appendix A.

4. SAMPLE AND VARIABLE DESCRIPTION AND DESCRIPTIVE STATISTICS

We begin our sample in 2005, after firms were required to comply with the SEC’s “Additional Form 8K

disclosure requirements and acceleration of filing date” (August 23, 2004). The new guidance expanded

the items to be reported in 8Ks and shortened the filing deadline, which is likely to affect firms’ measured

disclosure practices.16 To be included in our sample, we first require that a firm-year has data to calculate

the 8K-based disclosure measures and the earnings quality based disclosure measures from 2005 - 2016.

Using firm-years that meet these data requirements, we then form three samples to use in our analysis.

These samples differ based on the alternative cost of capital measure used in the analysis. The implied

cost of capital sample is limited to firm-years for which we can estimate the implied cost of capital

measures and the relevant control variables. The bid-ask spread sample is limited to firm-years for which

we can estimate the bid-ask spread and the relevant control variables, and the cost of debt sample is

limited to firm-years for which we can calculate the cost of debt and the relevant control variables.

Restricting the sample to firm-year observations that have all three measures would result in a small

sample. We obtain firm-level financial accounting information from Compustat, stock-return information

from CRSP, analyst forecast information from I/B/E/S, and the 8-K-based disclosure measures from CHP.

This process generates an implied cost of capital sample (ICC) of 20,228 firm-year observations, a bid-

ask spread sample (BAS) of 27,669 firm-year observations, and a cost of debt sample (COD) of 8,733

firm-year observations.

The control variables we include vary by the sample. The control variables for the ICC sample

are Beta (estimated from a firm-specific CAPM regression using monthly data for the five preceding 16 The new SEC rule generally requires 8Ks to be filed within four business days after the event date. See CHP for a detailed discussion of the timing. Lerman and Livnat (2010) examine the timeliness of 8K filing for a sample from 2005 through 2007 and find that 8Ks are filed in a timely manner after the enforcement of the new rule. Specifically, they document that 95 percent of material events are filed within four business days of the event dates and that mandatory and voluntary items exhibit similar timeliness.

17

fiscal years), MVE (the market value of equity measured at the beginning of the fiscal year), and BTM (the

firm’s book-to-market ratio measured at the beginning of the fiscal year). We log both MVE and BTM

when including it in ICC regressions. The control variables for the BAS sample are AvgVol, (the natural

log of the average daily firm trading volume over the prior fiscal year) AvgPrc (the natural log of the split

–adjusted daily firm price over the prior fiscal year), LnAssets (the natural log of a firm’s total assets at

the end of the prior fiscal year), and LnBTM (defined above). The control variables for the COD sample

include three variables defined above (LnMVE, Beta, and LnBTM) and ROA (return on assets, calculated

as operating income before depreciation divided by the beginning of the year total assets), LEV (financial

leverage, calculated as the beginning of the fiscal year total liabilities divided by total assets), and IntCov

(interest coverage, calculated as pretax income plus interest expense divided by interest expense).

Table 1 presents summary statistics for the disclosure, cost of capital, and control variables

included in the empirical analyses. Panel A (B, C) presents statistics for the ICC (BAS, COD) sample. To

facilitate comparisons across the three samples, we present summary statistics for Beta, MVE, and BTM in

each panel. While we present descriptive statistics for each sample, our discussion focuses on the ICC

sample. Firms in our sample file an average of just over 30 8K items and exhibits a year, 18.17 classified

as voluntary and 12.13 classified as mandatory.17,18 We also document significant variation in the our two

proxies for mandatory disclosure: MD_Event and MD_EQ. Our sample mean (median) cost of equity

capital values range from 12.91 percent (10.29 percent) to 25.59 percent (20.67 percent), depending on

the estimation method. These values are consistent with those reported in prior studies. For example,

Botosan (1997) reports a mean of 20.1 percent (19.0 percent) for 122 manufacturing firms in 1991;

Francis et al. (2008) show a mean (median) value of 16.6 percent (15.6 percent) for 677 firms in their

sample during the year of 2001. The mean (median) firm in our sample has a market value of equity of

$6.56 billion ($1.09 billion), suggesting that our sample includes smaller firms than in Francis et al.

17 The number of 8K items and exhibits exceed the number of 8K filings since firms frequently report both items and exhibits and/or report multiple items in a single 8K. 18 These values are larger than the mean values reported in CHP. They report mean values of 27.04 8K items and exhibits a year, 15.12 classified as voluntary and 11.92 classified as mandatory.

18

(2008) and firms that are closer to the Compustat firms during the period. Finally, VD_MF, a proxy for

voluntary disclosure based on management forecasts, is available for only about half of the firms in our

full sample. For the set of firm years for which we have the data, VD_MF has a mean (median) value of

12.28 (10). Again, we document meaningful variation in this measure.

Panel B of Table 1 presents similar statistics for the BAS sample. Generally, the mean/median

values of the disclosure variables are consistent with the ICC sample. When we compare firm size,

riskiness, and growth (MVE, Beta, and BTM), we see that the BAS sample includes slightly smaller (mean

MVE of 4883.72 versus 6560.37), equally risky (mean Beta of 1.29 versus 1.30), and slightly higher

growth (mean BTM of 0.56 versus 0.52) firm-years than the ICC sample. Average daily price (AvgPrc)

has a mean of 26.96 and median of 17.47. These values are generally consistent with those reported in

prior research. For example, Amiram, Owens, & Rozenbaum (2016) report daily average price of 30.244

(median of 24.91).

Panel C of Table 1 presents analogous statistics for the COD sample. Generally, the mean/median

values of the disclosure variables are higher than in the ICC sample. When we compare firm size,

riskiness, and growth (MVE, Beta, and BTM), we see that the COD sample includes much larger (mean

MVE of 13,011.33 versus 6,560.37), less risky (mean Beta of 1.18 versus 1.30), and slightly higher

growth (mean BTM of 0.55 versus 0.52) firm-years than the ICC sample.

5. EMPIRICAL RESULTS

(i) Tests of the Relation between Voluntary Disclosures and Periodic and Event-Driven Mandatory Disclosures: H1a and H1b. We begin by examining the associations between the three aspects of corporate information flow:

voluntary disclosure, periodic mandatory disclosure, and event-driven mandatory disclosure. H1a predicts

a substitutive relation between voluntary disclosure and periodic mandatory disclosure (higher-quality

periodic mandatory disclosure will lead to lower-quality voluntary disclosure). H1b predicts that

19

voluntary disclosure will be associated with event-driven mandatory disclosure, although we do not

predict a sign. We test these hypotheses using both a univariate and multivariate model, which includes

firm characteristics drawn from prior studies. Specifically, we include firm size (lnMVE) to control for

firm-specific factors prior studies suggest influence disclosure policy (e.g., Bamber & Cheon, 1998). We

also include the natural log of BTM (lnBTM) to control for the association between growth and voluntary

disclosure (Bamber & Cheon, 1998; Nagar, Nanda, & Wysocki, 2003). Lang & Lundholm (1993) show

that voluntary disclosure (measured with AIMR scores) is associated with the number of analysts that

follow the firm. Based on their finding, we include #Analyst (the number of analysts that follow the firm

during the prior fiscal year) and ROA (return on assets calculated as operating income before depreciation

divided by total assets) in the model. We include #Segment (the number of segments the firm reports at

the end of the prior fiscal year) to control for firm complexity (Nagar et al. 2003). Finally, we include

Issue (an indicator variable that equals one if the number of shares outstanding, adjusted for stock

dividends/splits, increases by more than twenty percent from year t-1 to year t, and zero otherwise), as

Frankel, McNichols, & Wilson (1995) and Lang & Lundholm (2000) document links between equity

issuance and a firm’s voluntary disclosure.

Panel A of Table 2 presents pairwise correlations between VD_8K, MD_Event, MD_EQ and the

firm characteristics included in the multivariate model. We show that both types of mandatory

disclosure—MD_Event and MD_EQ—are consistently positively associated with voluntary disclosure,

although the association with MD_Event is much larger. The average correlation between VD_8K and

MD_Event is 0.393, compared to the average correlation with MD_EQ (0.103). Panel B provides further

evidence using multivariate analysis.19 We present results when MD_EQ is included in the model (column

(2)), when MD_Event is included in the model (column (3)), and when both are included (column (4)).

We fail to document a significant association between VD_8K and MD_EQ in the multivariate setting

(columns (2) and (4)), inconsistent with the univariate setting. In contrast, VD_8K and MD_Event are

19 We estimate our model using the largest of our three samples—the BAS sample.

20

consistently positively associated (columns (3) and (4)). Our results are inconsistent with H1a, where we

predict a substitutive relation between VD_8K and MD_EQ, although they are consistent with our

unsigned expectations in H1b.20 In sum, our results suggest that the quality of a firm’s voluntary

disclosure is (1) unrelated to the quality of the reports produced from the firm’s mandatory reporting

system and (2) a complement to event-driven mandatory disclosures.21 We also find that voluntary

disclosure is positively associated with firm size and the number of segments a firm reports and

negatively associated with the book-to-market ratio and the number of analyst that follow it.22

(ii) Test of H2 and H3: Conditional and Unconditional Associations Between Cost of Capital and Voluntary and Mandatory Disclosure

The empirical associations between VD_8K and MD_Event and MD_EQ we document are

important in interpreting prior work that investigates how voluntary disclosure influences the cost of

capital. Furthermore, while we fail to document that MD_EQ is related to VD_8K, there is theoretical

support for the endogenous nature of the mandatory reporting system and voluntary disclosures. We

examine the empirical association between a firm’s cost of capital and its disclosures using a series of

regression models. We first provide evidence of the unconditional relation between the cost of capital and

voluntary disclosure (H2) and then expand the model to provide evidence of the relation between the cost

of capital and voluntary disclosure, conditional on mandatory disclosures. We estimate a series of

20 Failing to document a significant association between voluntary disclosure and earnings quality measures is also inconsistent with Francis et al. 2008, who document a significant complementary relation between EQ and voluntary disclosure based on information provided within 10-K filings. 21 In untabulated analysis, we estimate the Table 2 model without fixed effects and compare the adjusted R2’s of the base model with and without the two mandatory disclosure proxies: periodic mandatory disclosures (MD_EQ) and event-driven mandatory disclosures (MD_Event). This allows to evaluate the relative importance of these measures in explaining voluntary disclosure. When we add MD_EQ to the base model, the adjusted R2 of the model increases from 10.8% to 10.9%. In contrast, when MD_Event is added to the base model, the adjusted R2 increased from 10.8% to 23.1%. 22 Our analysis mirrors the analysis in Francis et al. (2008) (their Table 3), extended by the addition of MD_Event. Our analyses and results differ from theirs, however, in two significant ways. First, while our univariate analysis is consistent with theirs (VD_8K and MD_EQ have a complementary relation), we fail to document a significant association between VD_8K and MD_EQ in the multivariate analysis. Second, we find that our proxy for voluntary disclosure is significantly associated with additional firm controls, relative to their study. We believe that differences in our proxies for voluntary disclosure likely impact these results: our primary proxy for voluntary disclosure is based on data drawn from 8-K filings, while they construct a measure based on disclosures within 10-K filings.

21

regressions, where we include a measure of cost capital as the dependent measure, voluntary disclosure,

mandatory disclosures, and control variables. The general model is below:

COC = α +β1VD_8K +β2MD_Event + β3MD_EQ + Controls + Firm/Year Fixed Effects

We provide results where the proxy for the dependent measure (COC) is (1) the implied cost of equity,

(2) bid-ask spread, and (3) the cost of debt. We include firm fixed effect to control for time-invariant

unobservable firm characteristics that potentially drive both firm disclosure and riskiness. Including firm

fixed effects is one way to mitigate endogeneity concerns in empirical analyses (Wooldridge 2010).

Including year fixed effects controls for unobservable year effects such as changes in regulations and

accounting standards that cause changes in disclosure. We also calculate t-statistics based on standard

errors clustered by firm to address the potential correlation in the residuals across years for a given firm

(Petersen 2009).

Table 3 presents Spearman correlations for each of our samples and the appropriate variables.

Panel A includes the implied cost of capital variables, Panel B includes the bid-ask spread variables, and

Panel C includes the cost of debt variables. We focus our discussion on the ICC sample (Panel A) and

highlight differences across the other panels. As documented in Table 2, Panel A, the three disclosure

measures are significantly correlated. The largest average correlation is between VD_8K and MD_Event

(ρ = 0.364). Both mandatory disclosure proxies are positively correlated with VD_8K, but are weakly

negatively correlated with each other (average ρ = -0.079). The three implied cost of capital measures are

not statistically associated with voluntary disclosure, are positively associated with MD_Event and are

strongly negatively associated with MD_EQ. In terms of our additional measures of cost of capital

measures, panel B reports that AvgBAS is significantly negatively associated with both VD_8K and

MD_EQ and panel C reports that SPRating is positively associated with MD_Event and negatively

associated with MD_EQ.

Table 4 presents the regression results for the ICC sample. Panel A (B, C) includes results when

the implied cost of capital is based on rMPEG (rDIV, rSTPEG). Within each panel, Columns (1) – (3) present

22

the results of the unconditional tests. Columns (4) – (6) present the results when we include two of the

three proxies for disclosure. Column (7) reports the results based on the complete model. The results

across all three panels (all three measures of implied cost of capital) are relatively consistent. Our

discussion focuses on Panel A. When we include each of the disclosure measures in the model

individually, we find that VD_8K and MD_Event are both positively associated with the implied cost of

capital, and that MD_EQ is negatively associated with the implied cost of capital. (When we employ rDIV

as our implied cost of capital proxy, VD_8K is insignificant). These findings suggest that firms with

higher levels of voluntary disclosure and event-driven mandatory disclosure also have a higher cost of

capital and that firms with higher quality earnings have a lower cost of equity. In columns (4) – (6) we

include two of the three disclosure proxies in the regression and in column (7) we include all three

disclosure proxies. The findings across the models in columns (4) – (7) are relatively consistent. The

inclusion of additional disclosure variables in the regression model has no impact on the associations

between mandatory disclosure and the cost of equity capital. We do find, however, that the unconditional

positive association between VD_8K and implied cost of capital is either subsumed (Panel A and Panel C)

or unaffected.

Table 5 Panel A presents results when we employ bid-ask spread as our proxy for a firm’s cost of

capital (Welker, 1995; Heflin, Shaw, & Wild, 2005; Heflin et al. 2016). Panel B presents results when the

cost of debt is the proxy for cost of capital (Sengupta, 1998; Heflin et al. 2016). Consistent with Table 4,

within each panel, Columns (1) – (3) present the results of the unconditional tests. Columns (4) – (6)

present the results when we include two of the three disclosure proxies. Column (7) reports results based

on the complete model. The results in Panel A support our unconditional expectation between voluntary

disclosure and the cost of capital: firms that provide more expansive voluntary disclosures have a lower

cost of capital. This finding is consistent with our H2. We also continue to document significant positive

(negative) relations between MD_Event (MD_EQ) and bid-ask spread, consistent with the analysis that

relies on the implied cost of capital (Table 4). These findings suggest that firms with higher levels event-

driven mandatory disclosure also have a higher cost of capital, and that firms with higher quality

23

voluntary disclosure and higher quality periodic mandatory reports have a lower cost of equity. In

columns (4) – (6) we include two of the three disclosure proxies in the regression and in column (7) we

include all three disclosure proxies. The findings across the models in columns (4) – (7) are consistent.

The inclusion of additional disclosure variables in the regression model has no measurable impact on the

associations between the disclosure proxies and firms’ bid-ask spreads. Thus, the analysis that relies on

bid-ask spread to proxy for the cost of capital provides evidence that mandatory disclosures are related to

a firm’s cost of capital, although they do not impact the relation between voluntary disclosure and bid-ask

spread.

Table 5, Panel B presents results where the cost of debt is the dependent measure. The tenor of

these results are similar to the results when we employ the implied cost of capital. First, we find that the

unconditional relation between voluntary disclosure and the cost of debt is positive, consistent with H2.

When we add our proxies for the two types of mandatory disclosure to the analysis, we document a

positive (negative) relation between mandatory event-driven disclosures (periodic mandatory disclosures)

and the cost of capital. We do not find, however, that including these additional variables to the analysis

impacts the relation between cost of debt and voluntary disclosure.

The results in Tables 4 and 5 are generally consistent. First, the evidence provides support for our

H2, which predicts an association between cost of capital and voluntary disclosure. Even so, across the

models we document significant associations between voluntary disclosure and the cost of capital. The

sign of that association differs based on the model we examine. Our regression models also provide

support for our H3, which predicts that the link between a firm’s voluntary disclosure and the cost of

capital will maintain even when we control for mandatory disclosure. Our findings are consistent with our

expectations.

6. ADDITIONAL TESTS

(i) Disclosure Commitment

In Table 6 we present results of additional analysis where we sort the sample observations into two

24

groups based on our proxy for mandatory periodic disclosure and re-estimate our primary models. As

noted Francis et al. (2008), Hail & Leuz (2006), and others, the theoretical research used to motivate the

link between voluntary disclosure and the cost of capital is predicated on a firm’s commitment to a

disclosure policy. Gigler & Hemmer (1998), Beyer et al. (2010), and Ball et al. (2012) suggest that

mandatory disclosure policies and regulation may serve as a commitment mechanism. We use these

findings to motivate the examination of the role that voluntary disclosure plays in explaining a firm’s cost

of capital, conditional on the level of MD_EQ. To perform our analysis, we sort firm-year observations

into three groups based on the within industry-year rank of MD_EQ and re-estimate the models on the

subsamples.

Table 6 presents results of this analysis; we report on the observations in the top and bottom

terciles. Panel A (B) presents results when the implied cost of capital measures proxy for cost of capital

(bid-ask spread and cost of debt proxy for cost of capital). We include the same set of firm-level control

variables in the regressions, but omit them from the table for brevity. We also include firm and year fixed

effects in the implied cost of equity and bid-ask spreads models and industry and year fixed effects in the

cost of debt model. We present robust t-statistics, based on standard errors clustered at a firm level.

When implied cost of equity proxies for cost of capital, we find that the signs of the coefficients

on MD_Event and MD_EQ across the partitions are generally consistent.23 The coefficient on VD_8K is

statistically positive in the High MD_EQ partition only, however. When the proxy for cost of capital is

the cost of debt (in Panel B) we report similar results: the coefficients on MD_Event and MD_EQ are

consistently positive and negative and the positive coefficient on VD_8K in the High MD_EQ partition is

larger than in the Low MD_EQ partition (0.0265 versus 0.0086). When our proxy for cost of capital is

bid-ask spread, however, the results reverse. We find that VD_8K is significantly negative in the Low

MD_EQ partition and is insignificant in the High MD_EQ partition.

Our results provide preliminary evidence that mandatory periodic disclosure might serve as a

23 The signs of the coefficients when rDIV is the dependent measure in the high MD_EQ partition are as expected, but are not statistically significant.

25

commitment mechanism. We find that when firms exhibit greater commitment (High MD_EQ partition)

voluntary disclosures that are linked to increases in firms’ cost of capital are informative and when firms

exhibit lower commitment (Low MD_EQ partition) voluntary disclosures that are linked to decreases in

firms’ cost of capital are informative. The intuition that underlies these relations is unclear; we leave

additional analysis of this issue to future studies.

(ii) Controlling for Reportable Events

Firms’ voluntary disclosures are frequently responses to event-driven mandatory disclosure, as evidenced

by the findings in Table 2. We contend that “responsive” voluntary disclosures differ from “other”

voluntary disclosures and examine whether they are differentially related to firms’ cost of capital. To

perform our analysis, we sort firm-year observations into three groups based on the within industry-year

rank of MD_Event and re-estimate the models on the subsamples. Table 7 presents results of this analysis;

we report results based on the observations in the top and bottom MD_Event terciles. Panel A (B) presents

results when the implied cost of capital measures proxy for cost of capital (bid-ask spread and cost of debt

proxy for cost of capital). We include the same set of firm-level control variables in the regressions, but

omit them from the table for brevity. In this table, we include industry and year fixed effects and present

robust t-statistics, based on standard errors clustered at a firm level.

When implied cost of equity proxies for cost of capital, we find that the signs of the coefficients

on MD_Event and MD_EQ across the partitions are generally consistent.24 The coefficient on VD_8K is

statistically positive across the partitions. When the proxy for cost of capital is the cost of debt (in Panel

B) we report similar results: the coefficients on MD_Event are positive, on MD_EQ are statistically

negative, and on VD_8K are statistically positive. When our proxy for cost of capital is bid-ask spread,

however, we find that MD_Event is statistically positive (only in the High MD_Event partition) and that

VD_8K is statistically negative (only in the Low MD_Event partition).

24 When rDIV is the dependent measure MD_Event is not significant in the low MD_Event partition .

26

These results suggest the following. First, partitioning the sample by MD_Event allows us to

control for the impact that reportable events have on firms’ measured disclosures, reducing concerns

around the endogenous nature of voluntary disclosures. Across most of our cost of capital proxies, we

find that the general tenor of our results is unchanged. Including MD_EQ and MD_Event in the regression

model does not subsume the association between VD_8K and the cost of capital. Second, when we

employ bid-ask spread as our proxy for cost of capital (and document that firms with more forthcoming

voluntary disclosures have lower cost of capital in the full sample), we find that partitioning the sample

does matter. Specifically, we find that VD_8K is statistically negative only in the Low MD_Event

partition. This finding suggests that when VD_8K includes more “responsive” voluntary disclosures (the

High MD_Event partition) voluntary disclosures do not impact cost of capital. When VD_8K includes

fewer “responsive” voluntary disclosures (the Low MD_Event partition), however, voluntary disclosures

are associated with a lower cost of capital. These results support our predictions on two levels. First, they

reduce concerns that event-driven mandatory disclosures are driving the results across all our models.

Second, they provide some evidence that responsive voluntary disclosures are not driving the negative

relation between voluntary disclosures and the cost of capital. We leave additional analysis of this issue to

future studies.

(iii) Management Forecasts as Voluntary Disclosure

Management forecasts have been used as a proxy for voluntary disclosure in a number of studies (e.g.,

Coller & Yohn, 1997; Francis et al., 2008; Baginski & Rakow, 2012; Ball et al., 2012; Li & Yang, 2016).

Despite the shortcomings of this proxy in terms of capturing voluntary disclosure (e.g., relying on a single

channel to capture voluntary disclosure, limitations in terms of firms that provide management forecasts

and concerns with how data providers collect these data), we re-estimate our models where we replace

VD_8K with a voluntary disclosure variable based on management forecasts (VD_MF). Table 8 replicates

the analysis provided in Tables 4 and 5, when the number of management forecasts serves as our proxy

for voluntary disclosure instead of VD_8K. Specifically, VD_MF equals the log of the sum of the values

27

assigned to each management forecast for all forecasts issued during the fiscal year. Each forecast was

assigned a value based on the forecast form: one for qualitative forecasts, two for range forecasts and

open-ended forecasts, and three for point forecasts (Francis et al., 2008). Panel A reports results using

implied cost of equity to proxy for cost of capital, Panel B reports results when bid-ask spread proxies for

cost of capital, and Panel C reports results using cost of debt.25 As in our earlier analyses, we have three

samples (an ICC, BAS, and COD sample) that are constrained by the availability of the relevant cost of

capital proxy. The sample sizes are smaller than in earlier tables, however, as our management forecast

data ends in 2015 instead of 2016.

Across all three panels we document significantly negative coefficients on VD_MF, whether it is

included in the models by itself or along with MD_Event and MD_EQ. The consistent negative

association with all of the cost of capital proxies is in stark contrast with the findings in Table 4 and Panel

B of Table 5. Nonetheless, consistent with the prior analysis, we fail to find that the inclusion of

mandatory disclosure in the analysis subsumes the impact of voluntary disclosure. In sum, these findings

support our H2 and H3.26

7. CONCLUSIONS

We examine the association between firms’ cost of capital and their voluntary and mandatory disclosures,

using a unique set of empirical proxies to capture voluntary and mandatory disclosures. Our empirical

analysis considers two types mandatory disclosure that arise within the financial reporting environment:

mandatory periodic disclosure and event-driven mandatory disclosure. Consistent with endogenous

relations predicted by theory, we document that voluntary disclosure is associated with both types of

mandatory disclosure, although only event-driven mandatory disclosure is significant in explaining 25 We table results based on rMPEG. Results based on rDIV and rPEG are substantively similar. 26 We recognize the potential shortcomings of VD_MF—it is limited to a single channel of voluntary disclosure, while the analytical research considers voluntary disclosure more generally—but believe the analysis is informative and facilitates comparison with prior studies. Another issue of note is that management forecasts are reported via voluntary 8-K items, so are incorporated in VD_8K. CHP report a correlation of 0.507 between VolDisc_Ct (our VD_8K) and the frequency of management guidance reported within the voluntary 8-K items (identified via a keyword search), which may explain differences in our reported results. See their Tables 3 and 7.

28

voluntary disclosure in a multivariate model that includes relevant firm characteristics. We also find that

the cost of capital is influenced by each of these disclosure, in separate regressions as well as when they

are incorporated in a single model. These inferences are largely unchanged when we include all three

aspects of a firm’s disclosures in a single model.

Our study makes several important contributions to the literature. First, we extend the literature

around mandatory disclosures by introducing the notion that these disclosures can be sorted into two

types: periodic mandatory disclosure and event-driven mandatory disclosure. We use this framework and

exploit findings from the analytical literature to form predictions of the expected relations with voluntary

disclosure. Second, we provide evidence of the associations between voluntary disclosure and mandatory

periodic disclosure and mandatory event-driven disclosure. Finally, we expand the research into the

conditional impact of voluntary disclosure on the cost capital by providing evidence of the link between

each type of disclosure (voluntary and mandatory) and the cost of capital. Ultimately, we show that, even

though mandatory disclosure is associated with both voluntary disclosure and the cost of capital, it does

not subsume the relation between voluntary disclosure and the cost of capital.

29

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34

APPENDIX A Variable Definitions

Variable Variable Descriptions Voluntary and Mandatory Disclosure Proxies VD_8K The number of voluntary 8K items and related exhibits reported in a firm’s 8Ks within its fiscal year.

8K item numbers 2.02 (Results of Operations and Financial Condition), 7.01 (Regulation FD), and 8.01 (Other Events) are classified as voluntary (Lerman & Livant, 2010, Cooper, He, & Plumlee, 2018).

MD_Event The number of mandatory 8K items and related exhibits reported in a firm’s 8Ks within its fiscal year. 8K items other than 2.02, 7.01, and 8.01 are classified as mandatory (Lerman & Livant, 2010, Cooper et al. 2018).

MD_EQ The common factor score obtained from a factor analysis of EQAQ, EQEV, and EQAbsAA. We multiply the value by (-1) so that higher values of MD_EQ indicate poorer quality of mandatory periodic disclosure.

VD_MF The sum of values assigned to management guidance (1 for qualitative forecasts, 2 for range forecasts and open-ended forecasts, and 3 for point forecasts) for all forecasts issued during the fiscal year, following Francis et al. (2008).

Cost of capital/liquidity/cost of debt measures rMPEG Calculated following Easton (2004).

𝑟!"#$ = 𝐴 + 𝐴! +𝐹𝑒𝑝𝑠! − 𝐹𝑒𝑝𝑠!

𝑃! ,𝐴 =

𝐹𝑑𝑝𝑠!2𝑃!

𝑃! = stock price (from I/B/E/S) the day before I/B/E/S releases monthly forecasts 𝐹𝑒𝑝𝑠! = the I/B/E/S forecasted earnings per share for year t+1 𝐹𝑒𝑝𝑠! = the I/B/E/S forecasted earnings per share for year t+2 𝐹𝑑𝑝𝑠! = the I/B/E/S forecasted dividend per share for year t+1. When a dividend per share forecast is not available, we estimate it based on the current dividend payout ratio (k): dps1 = k×𝑒𝑝𝑠!,. If earnings are negative, we divide the dividends paid by (0.06*total assets) to estimate the payout ratio. We winsorize k to be between 0 and 1, following Guay et al. (2011). We estimate the implied cost of capital measure based on monthly mean forecasts from IBES summary file and use the average of the monthly estimates of a fiscal year.

rDIV Calculated following the method employed in Botosan & Plumlee (2005) and Botosan et al. (2011). Specifically, rDIV is the value that solves P0 = !"#$!!!"!

!!!!"# where:

𝑃! = stock price (from I/B/E/S) the day before I/B/E/S releases monthly forecasts. 𝐹𝑑𝑝𝑠! = the I/B/E/S forecasted dividend per share for year t+1 𝐹𝑃! = the I/B/E/S forecasted stock price for year t+1 We estimate the implied cost of capital measure based on monthly mean forecasts from IBES summary file and use the average of the monthly estimates of a fiscal year.

rSTPEG Calculated following the method employed in Easton (2004) and Botosan et al. (2011)

𝑟!"#$% =𝐹𝑒𝑝𝑠! − 𝐹𝑒𝑝𝑠!

𝑃!

𝑃! = stock price (from I/B/E/S) the day before I/B/E/S releases monthly forecasts. 𝐹𝑒𝑝𝑠! = the I/B/E/S forecasted earnings per share for year t+1 𝐹𝑒𝑝𝑠! = the I/B/E/S forecasted earnings per share for year t+2 We estimate the implied cost of capital measure based on monthly mean forecasts from IBES summary file and use the average of the monthly estimates of a fiscal year.

AvgBAS The average daily quoted bid-ask spread over a fiscal year. Daily quoted bid-ask spread is calculated as the average of all bid-ask spreads, 0.5 × (Ask-Bid) / (Ask+bid). The quote data are from TAQ.

SPRating Standard &Poor Domestic Long Term Issuer Credit Rating (Compustat - SPLTICRM). The value is assigned as follows 1 if between AAA and AA-; 2 if A+ to A-; 3 if BBB+ to BBB-; 4 if BB+ to BB-; 5 if B+ to 'SD'.

Control variables Beta Estimated from a firm-specific CAPM regression using the 60 months’ data preceding fiscal year t.

35

MVE Market value of equity measured at the beginning of fiscal year t, which is the end of fiscal year t-1. lnMVE The natural logarithm of MVE. #Analyst The number of analysts issuing at least one annual earnings forecast during fiscal year t-1. BTM A firm’s book-to-market ratio measured at the beginning of fiscal year t. lnBTM The natural logarithm of (BTM). #Segment The number of segments at the beginning of fiscal year t. ROA Return on assets = operating income before depreciation divided by beginning of the year total assets

(Compustat OIBDP/AT) Issue An indicator variable that equals one if a firm’s split-adjusted number of outstanding common shares

increased by 20 percent or more in year t relative to year t-1, and zero otherwise. lnAssets The natural log of a firm’s total assets at the beginning of the fiscal year t. AvgPrc The natural log of the average of the daily firm (split adjusted) price over fiscal year. AvgVol The natural log of the average of the daily firm trading volume over fiscal year. LEV Financial leverage calculated as total liabilities/total assets at the beginning of year. IntCov Interest coverage calculated as (pretax income + interest expense)/ interest expense at the beginning

of year.

36

Appendix B Calculation of MD_EQ

MD_EQ measures the quality of a firm’s mandatory periodic filings. It is calculated as (-1) multiplied by the common factor score obtained from a factor analysis of EQAQ, EQEV, and EQAbsAA. The calculating process of EQAQ, EQEV, and EQAbsAA are detailed as follows. • Accrual quality (EQAQ) is the standard deviation of the residuals from the following regressing model using data

over years t-10 to t (i.e., we use data from 1995–2005 to calculate the measure of 2005): 𝑇𝐶𝐴!,!𝐴𝑠𝑠𝑒𝑡𝑠!,!

= ∅!,! + ∅!,!𝐶𝐹𝑂!,!!!𝐴𝑠𝑠𝑒𝑡𝑠!,!

+ ∅!,!𝐶𝐹𝑂!,!𝐴𝑠𝑠𝑒𝑡𝑠!,!

+ ∅!,!𝐶𝐹𝑂!,!!!𝐴𝑠𝑠𝑒𝑡𝑠!,!

+ ∅!,!∆𝑅𝑒𝑣!,!𝐴𝑠𝑠𝑒𝑡𝑠!,!

+ ∅!,!𝑃𝑃𝐸!,!𝐴𝑠𝑠𝑒𝑡𝑠!,!

+ 𝑣!,!

AQj = ϭ(𝑣!,!). Where: Assetsj,t= firm j’s average total assets (Compustat AT) in year t and t-1 TCAj,t = firm j’s total current accruals in year t = (∆𝐶𝐴!,! − ∆𝐶𝐿!,! − ∆𝐶𝑎𝑠ℎ!,! + ∆𝑆𝑇𝐷𝐸𝐵𝑇!,!)

∆𝐶𝐴!,! = firm j’s change in current assets (Compustat ACT) between year t-1 and year t ∆𝐶𝐿!,! = firm j’s change in current assets (Compustat LCT) between year t-1 and year t ∆𝐶𝑎𝑠ℎ!,! = firm j’s change in cash (Compustat CHE) between year t-1 and year t ∆𝑆𝑇𝐷𝐸𝐵𝑇!,! = firm j’s change in current debt (Compustat DLC) between year t-1 and year t ∆𝑅𝑒𝑣!,! = firm j’s change in revenues (Compustat SALE) between year t-1 and year t 𝑃𝑃𝐸!,! = firm j’s gross value of property, plant and equipment (Compustat PPEGT) in year t 𝐶𝐹𝑂!,! = firm j’s cash flow from operation in year t, measured as 𝐶𝐹𝑂!,! = 𝑁𝐼𝐵𝐸!,! − 𝑇𝐴!,! 𝑁𝐼𝐵𝐸!,! =

firm j’s net income before extraordinary items (Compustat IB) in year t 𝑇𝐴!,! = firm j’s total accruals in year t, (∆𝐶𝐴!,! − ∆𝐶𝐿!,! − ∆𝐶𝑎𝑠ℎ!,! + ∆𝑆𝑇𝐷𝐸𝐵𝑇!,! − 𝐷𝐸𝑃𝑁!,!)

DEPNj,t = firm j’s depreciation and amortization expense (Compustat - DP) in year t. • EQEV is the standard deviation of earnings before extraordinary items (Compustat IB), scaled by total assets

(Compustat AT). We use firm data from year t-9 to year t to estimate EQEV for year t (i.e., for year 2005, we use the data over 1996 – 2005).

• EQAbsAA is the absolute value of abnormal accruals is generated using the modified Jones (1991) model. We

estimate the cross-sectional model for each of the 49Fama-French (1997) industries with a minimum of 20 firms in year t.

𝑇𝐴!,!𝐴𝑠𝑠𝑒𝑡!,!!!

= к!1

𝐴𝑠𝑠𝑒𝑡!,!!!+ к!

∆𝑅𝑒𝑣!,!𝐴𝑠𝑠𝑒𝑡!,!!!

+ к!𝑃𝑃𝐸!,!

𝐴𝑠𝑠𝑒𝑡!,!!!+ ԑ!,!

Compustat definitions are the same as in the estimations described above. The industry- and year- specific parameter estimates (к!, к!, к!) from the above equation are used to estimate firm-specific normal accruals (NA) as a percentage of lagged total assets:

𝑁𝐴!,! = к!1

𝐴𝑠𝑠𝑒𝑡!,!!!+ к!

(∆𝑅𝑒𝑣!,! − ∆𝐴𝑅!,!)𝐴𝑠𝑠𝑒𝑡!,!!!

+ к!𝑃𝑃𝐸!,!

𝐴𝑠𝑠𝑒𝑡!,!!!

where ∆𝐴𝑅!,! = firm j’s change in accounts receivable (Compustat RECT) between year t-1 and year t and

calculate |AAj,t| = !"!,!

!""#$!,!!!− 𝑁𝐴!,! . 𝑁𝐴!,! is from fitting firm information into the above equation.

Following Francis et al. (2008), we use rolling ten-year windows, t-9,…, t to generate average |AAj| which yields a firm-specific measure (i.e., for year 2005, we take the average of the firm-level |AAj,t| from 1996-2005 (t=2005).

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Table 1 Descriptive Data for Sample and Variables

Panel A: Distributional statistics – implied cost of capital sample

Variable N Mean Std. Dev. 10% 25% Med 75% 90% VD_8K 20228 18.17 14.17 8 10 14 22 33 MD_Event 20228 12.13 10.00 3 6 10 16 24 MD_EQ 20228 0.00 1.00 -0.14 0.00 0.09 0.13 0.15 rMPEG 20228 13.08 8.12 7.32 8.66 10.58 14.47 21.67 rDIV 20228 25.59 17.46 8.39 13.03 20.67 33.57 50.30 rSTPEG 18308 12.91 8.44 6.74 8.25 10.29 14.53 22.12 Beta 20228 1.30 0.85 0.43 0.76 1.18 1.67 2.28 MVE 20228 6560.37 23608.69 126.22 327.59 1091.54 3725.86 12922.74 BTM 20228 0.52 0.48 0.16 0.27 0.43 0.66 0.94 VD_MF 19132 6.19 8.22 0 0 0 10 18

Panel B: Distributional statistics – bid-ask spread sample

Variable N Mean Std. Dev. 10% 25% Med 75% 90% VD_8K 27669 16.91 13.63 7 9 14 21 31 MD_Event 27669 11.92 10.31 2 5 10 16 24 MD_EQ 27669 0.00 1.00 -0.18 -0.01 0.10 0.15 0.17 Beta 27666 1.29 0.83 0.40 0.73 1.16 1.68 2.31 MVE 27669 4883.72 20292.04 37.30 133.84 589.11 2438.54 9018.08 BTM 27669 0.56 0.77 0.13 0.26 0.46 0.73 1.09 AvgBAS 27669 0.01 0.02 0.00 0.00 0.00 0.01 0.02 AvgVol 27669 4152.42 12609.66 55.09 250.98 979.60 3174.77 9217.44 AvgPrc 27669 26.96 55.79 2.62 6.39 17.47 35.79 57.01 LnAssets 27669 4434.32 16063.83 34.19 121.23 555.39 2356.43 8607.00 VD_MF 25841 4.77 7.59 0 0 0 8 16

Panel C: Distributional statistics – cost of debt sample

Variable N Mean Std. Dev. 10% 25% Med 75% 90% VD_8K 8733 21.88 16.60 9 12 18 27 38 MD_Event 8733 14.16 11.45 4 7 12 18 26 MD_EQ 8733 0.00 1 -0.43 -0.08 0.14 0.27 0.34 Beta 8733 1.18 0.74 0.38 0.66 1.07 1.57 2.12 MVE 8733 13011.33 32869.14 512.23 1312.20 3469.46 10591.06 28432.61

38

BTM 8733 0.55 0.60 0.18 0.28 0.46 0.68 0.97 SPRating 8733 3.48 1.05 2 3 3 4 5 ROA 8733 0.13 0.08 0.07 0.09 0.12 0.17 0.22 LEV 8733 0.61 0.16 0.42 0.51 0.62 0.72 0.81 IntCov 8733 17.69 333.53 0.19 2.16 4.63 10.46 21.66 VD_MF 8063 7.65 9.10 0 0 6 12 20

Note: See Appendix A for detailed definitions of all variables. This table reports descriptive data for the three samples we include in our analyses. Panel A reports summary statistics for the implied cost of capital sample and includes voluntary and mandatory disclosure variables, implied cost of capital variables, and firm characteristics. Panel B reports summary statistics for the bid-ask spread sample and includes voluntary and mandatory disclosure variables, bid-ask spread variable, and firm characteristics. Panel C presents summary statistics for the cost of debt sample and includes voluntary and mandatory disclosure variables, S&P credit rating, and control variables.

39

Table 2 Test of the Relation between Voluntary Disclosure and Mandatory Disclosures

Panel A: Spearman correlation coefficients for the bid-ask spread sample VD_8K MD_Event MD_EQ lnMVE lnBTM #Analyst ROA #Segment

MD_Event 0.393 (12/0) MD_EQ 0.103 -0.086 (12/0) (0/10) lnMVE 0.327 0.115 0.486 (12/0) (11/0) (12/0) lnBTM -0.072 0.002 0.040 -0.358 (0/9) (1/0) (5/1) (0/12) #Analyst 0.318 0.161 0.331 0.821 -0.271 (12/0) (12/0) (12/0) (12/0) (0/12) ROA 0.056 -0.150 0.357 0.435 -0.309 0.306 (7/0) (0/12) (12/0) (12/0) (0/12) (12/0) #Segment -0.019 0.000 0.041 0.211 -0.013 0.160 0.105 (0/2) (0/0) (3/0) (12/0) (0/1) (12/0) (11/0) Issue 0.116 0.158 -0.108 -0.072 -0.081 -0.044 -0.117 -0.076 (12/0) (12/0) (0/10) (0/9) (0/9) (0/4) (0/10) (0/8) Panel B: Regressions of VD_8K on MD_Event, MD_EQ, and control variables. Variable (1) (2) (3) (4) MD_Event 0.317*** 0.317*** (12.09) (12.09) MD_EQ -0.040 -0.025 (-0.60) (-0.63) lnMVE 0.814*** 0.816*** 1.008*** 1.010*** (4.56) (4.57) (5.93) (5.93) lnBTM -0.274 -0.274 -0.251* -0.250*

(-1.64) (-1.64) (-1.67) (-1.66) #Analyst -0.046 -0.046 -0.059** -0.059**

(-1.50) (-1.50) (-1.99) (-1.99) ROA -0.572 -0.572 0.584 0.584

(-1.02) (-1.02) (0.94) (0.94) #Segment 3.576*** 3.576*** 2.477*** 2.477***

(13.65) (13.65) (9.29) (9.29) Issue 0.042 0.042 0.039 0.039 (0.80) (0.80) (0.77) (0.77) N 26,289 26,289 26,289 26,289 Adj R2 0.632 0.632 0.663 0.663 Note: See Appendix A for detailed definitions of all variables. This table reports correlations and regression results for the relation between voluntary disclosure and mandatory disclosure. Panel A presents Spearman correlation coefficients between disclosure variables and firm control variables. Each cell provides the average yearly correlation coefficient; the number of years out of the twelve-year sample period the correlation coefficient is significantly positive/negative with a p-value less than 0.01 (in parentheses). The correlations are based on the sample to conduct bid-ask spread analysis in order to keep more firm-year observations. Panel B presents the results of regressing voluntary disclosure on mandatory disclosure and control variables. All regressions include firm and year fixed effects. We exclude firms appearing only once in the sample to avoid overstating statistical significance when firm fix effect is included. Robust t-statistics are presented in parentheses and are based on standard errors clustered at a firm level. ***, **, and * denote statistical significance at the 0.01, 0.05, and 0.10 levels, respectively.

40

Table 3 Spearman Correlation Matrices

Panel A: Spearman correlation coefficients—cost of capital sample (N = 20,228 firm years) VD_8K MD_Event MD_EQ rMPEG rDIV rSTPEG Beta lnMVE

MD_Event 0.364 (12/0)

MD_EQ 0.080 -0.079 (9/0) (0/7)

rMPEG -0.040 0.018 -0.286 (0/2) (1/0) (0/12)

rDIV -0.031 0.092 -0.466 0.391 (0/3) (9/0) (0/12) (12/0)

rSTPEG -0.103 0.212 -0.339 0.461 0.415 (0/1) (12/0) (1/11) (12/0) (11/1)

Beta -0.033 0.061 -0.376 0.273 0.298 0.422 (0/2) (6/0) (0/12) (12/0) (12/0) (12/0)

lnMVE 0.224 0.083 0.456 -0.258 -0.498 -0.545 -0.224 (12/0) (8/0) (12/0) (0/12) (0/12) (0/12) (0/12)

BTM 0.006 0.050 0.058 -0.039 0.100 0.293 0.106 -0.305 (1/0) (4/0) (6/0) (0/5) (9/0) (12/0) (8/0) (0/12) Panel B: Spearman correlation coefficients—bid-ask spread sample (N = 27,669 firm years)

VD_8K MD_Event MD_EQ AvgBAS AvgVol AvgPrc BTM MD_Event 0.393 (12/0)

MD_EQ 0.103 -0.086 (12/0) (0/10)

AvgBAS -0.307 -0.094 -0.484 (0/12) (0/8) (0/12)

AvgVol 0.363 0.249 0.153 -0.726 (12/0) (12/0) (11/0) (0/12)

AvgPrc 0.182 -0.033 0.571 -0.795 0.375 (12/0) (1/4) (12/0) (0/12) (12/0)

BTM -0.087 -0.027 0.071 0.279 -0.277 -0.238 (0/11) (0/2) (7/0) (12/0) (0/12) (0/12) lnAssets 0.361 0.170 0.548 -0.847 0.729 0.707 -0.059 (12/0) (12/0) (12/0) (0/12) (12/0) (12/0) (0/7)

41

Panel C: Spearman correlation coefficients—cost of debt sample (N = 8,733 firm years) VD MD_Event MD_EQ SPRating LnMVE ROA LEV IntCov Beta

MD_Event 0.339

(12/0)

MD_EQ 0.025 -0.145

(0/0) (0/11)

SPRating 0.059 0.187 -0.441

(1/0) (12/0) (0/12)

LnMVE 0.078 -0.038 0.243 -0.709

(4/0) (0/3) (12/0) (0/12)

ROA -0.094 -0.143 -0.023 -0.313 0.302

(0/6) (0/12) (1/0) (0/12) (12/0)

LEV 0.074 0.071 0.064 0.210 -0.169 -0.195

(2/0) (3/0) (2/0) (12/0) (0/11) (0/11)

IntCov. -0.098 -0.159 0.164 -0.575 0.497 0.614 -0.449

(0/6) (0/10) (9/0) (0/12) (12/0) (12/0) (0/12)

Beta -0.043 0.071 -0.431 0.418 -0.306 -0.116 -0.023 -0.205

(0/1) (4/0) (0/12) (12/0) (0/11) (0/7) (0/2) (0/10)

BTM 0.065 0.064 -0.002 0.280 -0.395 -0.528 -0.191 -0.375 0.128

(2/0) (4/0) (2/3) (12/0) (0/12) (0/12) (0/11) (0/12) (7/0) Note: See Appendix A for detailed definitions of all variables. This table reports Spearman correlations. Panel A, B, and C presents Spearman correlation matrices for the cost of capital sample, bid-ask spread sample, and cost of debt sample respectively. Each cell provides the average yearly correlation coefficient; the number of years out of the twelve-year sample period the correlation coefficient is significantly positive/negative with a p-value less than 0.01 (in parentheses).

Table 4 Test of the Relation between Implied Cost of Capital and Voluntary and Mandatory Disclosure

Panel A: Dependent variable is implied cost of equity rMPEG (1) (2) (3) (4) (5) (6) (7)

VD_8K 0.018*** -0.0003 0.018*** -0.0003 (2.80) (-0.05) (2.80) (-0.06) MD_Event 0.071*** 0.070*** 0.070*** 0.070*** (8.45) (8.45) (8.52) (8.53) MD_EQ -0.462*** -0.465*** -0.462*** -0.465*** (-5.29) (-4.73) (-5.23) (-4.73) Beta 0.103 0.102 0.103 0.102 0.102 0.103 0.102 (0.88) (0.87) (0.89) (0.88) (0.87) (0.88) (0.88) lnMVE -2.781*** -2.742*** -2.761*** -2.732*** -2.742*** -2.771*** -2.732*** (-14.03) (-14.03) (-13.99) (-14.03) (-14.02) (-14.03) (-14.02) lnBTM 0.760*** 0.752*** 0.757*** 0.759*** 0.752*** 0.766*** 0.758*** (4.27) (4.24) (4.26) (4.27) (4.23) (4.31) (4.27) N 19847 19847 19847 19847 19847 19847 19847 Adj. R2 0.502 0.506 0.502 0.507 0.506 0.503 0.507 Panel B: Dependent variable is implied cost of equity rDIV

(1) (2) (3) (4) (5) (6) (7) VD_8K 0.015 0.003 0.015 0.002 (1.27) (0.23) (1.27) (0.23) MD_Event 0.048*** 0.048*** 0.047*** 0.047*** (3.87) (3.87) (3.70) (3.71) MD_EQ -0.290*** -0.292*** -0.290*** -0.292*** (-3.63) (-3.42) (-3.60) (-3.42) Beta 0.662*** 0.662*** 0.663*** 0.662*** 0.662*** 0.662*** 0.662*** (2.74) (2.74) (2.74) (2.74) (2.74) (2.74) (2.74) lnMVE -0.259 -0.231 -0.244 -0.225 -0.233 -0.253 -0.226 (-0.80) (-0.72) (-0.76) (-0.70) (-0.72) (-0.78) (-0.71) lnBTM -1.290*** -1.297*** -1.294*** -1.293*** -1.296*** -1.287*** -1.292*** (-4.52) (-4.55) (-4.53) (-4.54) (-4.54) (-4.50) (-4.53) N 19847 19847 19847 19847 19847 19847 19847 Adj. R2 0.619 0.620 0.620 0.620 0.620 0.620 0.620

43

Panel C: Dependent variable is implied cost of equity rSTPEG (1) (2) (3) (4) (5) (6) (7)

VD_8K 0.022*** 0.003 0.022*** 0.003 (3.08) (0.50) (3.08) (0.49) MD_Event 0.073*** 0.073*** 0.072*** 0.072*** (8.57) (8.58) (8.51) (8.53) MD_EQ -0.448*** -0.451*** -0.448*** -0.451*** (-4.29) (-3.89) (-4.27) (-3.89) Beta 0.089 0.088 0.091 0.088 0.088 0.090 0.088 (0.74) (0.72) (0.75) (0.73) (0.72) (0.75) (0.73) lnMVE -3.059*** -3.016*** -3.040*** -3.008*** -3.018*** -3.050*** -3.009*** (-14.60) (-14.61) (-14.56) (-14.62) (-14.59) (-14.61) (-14.60) lnBTM 0.763*** 0.748*** 0.755*** 0.754*** 0.750*** 0.768*** 0.755*** (4.18) (4.11) (4.14) (4.15) (4.12) (4.21) (4.15) N 17926 17926 17926 17926 17926 17926 17926 Adj. R2 0.550 0.554 0.550 0.555 0.554 0.551 0.555 Note: See Appendix A for detailed definitions of all variables. This table reports main regression results of cost of capital tests. All regressions include firm and year fixed effects. We exclude firms appearing only once in the sample to avoid overstating statistical significance when firm fix effect is included. Robust t-statistics, based on standard errors clustered at a firm level, are presented in parentheses. The coefficient estimates have been multiplied by 1000 for ease of exposition. ***, **, and * denote statistical significance at the 0.01, 0.05, and 0.10 levels, respectively.

44

Table 5 Test of the Relation between Bid-Ask Spread/Cost of Debt and Voluntary and Mandatory Disclosure

Panel A: Dependent variable is bid-ask spread (1) (2) (3) (4) (5) (6) (7)

VD_8K -0.022*** -0.041*** -0.022*** -0.041*** (-3.07) (-4.02) (-3.09) (-4.03)

MD_Event 0.056*** 0.055*** 0.069*** 0.068*** (6.13) (6.10) (6.94) (6.91)

MD_EQ -0.550*** -0.545*** -0.551*** -0.546*** (-4.48) (-4.24) (-4.53) (-4.27)

AvgVol@ -0.040*** -0.040*** -0.040*** -0.040*** -0.040*** -0.040*** -0.040*** (-4.21) (-4.31) (-4.28) (-4.36) (-4.27) (-4.26) (-4.32)

AvgPrc 0.003* 0.003* 0.003* 0.003* 0.003* 0.003* 0.003* (1.91) (1.90) (1.88) (1.87) (1.91) (1.89) (1.88)

lnBTM 2.152*** 2.157*** 2.153*** 2.150*** 2.142*** 2.146*** 2.136*** (5.78) (5.80) (5.78) (5.79) (5.79) (5.78) (5.78)

LnAssets -4.061*** -4.011*** -4.034*** -3.981*** -3.992*** -4.030*** -3.962*** (-17.46) (-17.34) (-17.30) (-17.19) (-17.32) (-17.31) (-17.18) N 27262 27262 27262 27262 27262 27262 27262 Adj. R2 0.662 0.663 0.662 0.663 0.663 0.662 0.663 Panel B: Dependent variable is cost of debt

(1) (2) (3) (4) (5) (6) (7) VD_8K 0.022*** 0.0161*** 0.0221*** 0.0166*** (10.78) (9.08) (11.27) (9.68)

MD_Event 0.0308*** 0.0289*** 0.0230*** 0.0210*** (9.97) (9.24) (7.54) (6.58)

MD_EQ -1.146*** -1.086*** -1.137*** -1.098*** (-12.22) (-11.62) (-11.65) (-11.41)

LnMVE -1.410*** -1.378*** -1.319*** -1.345*** -1.414*** -1.377*** -1.382*** (-35.40) (-36.72) (-39.00) (-37.52) (-35.00) (-36.05) (-35.73)

ROA -4.954*** -4.623*** -5.311*** -4.804*** -4.625*** -5.130*** -4.814*** (-5.05) (-4.68) (-5.05) (-4.59) (-4.75) (-4.98) (-4.67)

LEV 2.756*** 2.847*** 2.979*** 2.927*** 2.756*** 2.823*** 2.827*** (13.08) (12.44) (13.24) (12.72) (12.66) (13.20) (12.87)

IntCov 0.082*** 0.086*** 0.094*** 0. 101*** 0.088*** 0.098*** 0.102*** (2.62) (2.68) (2.73) (2.98) (2.82) (2.94) (3.10)

Beta 0.805*** 0.783*** 0.658*** 0.653*** 0.792*** 0.669*** 0.660*** (23.65) (20.40) (22.31) (18.00) (21.21) (21.16) (18.40)

LnBTM -0.0291 0.00277 0.0533 0.0504 -0.0209 0.0191 0.0260 (-0.38) (0.04) (0.63) (0.62) (-0.27) (0.23) (0.32) N 8733 8733 8733 8733 8733 8733 8733 Pseudo R2 0.356 0.355 0.359 0.367 0.360 0.368 0.371 Note: See Appendix A for definitions of all variables. This table reports main regression results. Panel A and B present the results for the bid-ask spread test and cost of debt test respectively. All regressions (Ordinary Least Square) in Panel A include firm and year fixed effects. All regressions (Ordered Logistic) in Panel B include year and industry fixed effects. Robust t-statistics, based on standard errors clustered at a firm level, are presented in parentheses. Coefficient estimates in Panel A are multiplied by 1000 for ease of exposition. @ Coefficient estimates for this variable are multiplied by 1000 for ease of exposition. ***, **, and * denote statistical significance at the 0.01, 0.05, and 0.10 levels, respectively.

45

Table 6 Test of the Relation between Cost of Capital or Bid-Ask Spread and Voluntary and

Disclosure: Sample Partitioned by MD_EQ

Panel A: Dependent variable is implied cost of equity Dependent Variable = rMPEG Dependent Variable = rDIV Dependent Variable = rSTPEG

Low MD_EQ

High MD_EQ

Low MD_EQ

High MD_EQ

Low MD_EQ

High MD_EQ

VD_8K -0.012 0.012* 0.015 -0.008 0.011 0.011* (-0.70) (1.84) (0.52) (-0.50) (0.62) (1.73)

MD_Event 0.123*** 0.021*** 0.077*** 0.018 0.120*** 0.021*** (6.57) (3.49) (2.85) (1.06) (6.18) (3.59)

MD_EQ -0.398*** -7.818** -0.219** -11.000 -0.387*** -9.869*** (-3.16) (-2.39) (-2.21) (-1.17) (-2.67) (-2.67)

Controls Included Included Included Included Included Included N 6227 6404 6227 6404 5601 5831 Adj. R2 0.465 0.502 0.570 0.606 0.500 0.543 Panel B: Dependent variable is bid-ask spread/cost of debt

Dependent Variable = AvgBAS Dependent Variable = SPRating Low MD_EQ High MD_EQ Low MD_EQ High MD_EQ

VD_8K -0.027*** -0.001 0.009** 0.026*** (-3.54) (-0.59) (2.40) (8.84)

MD_Event 0.037*** -0.004** 0.019*** 0.018** (4.21) (-1.99) (4.98) (2.43)

MD_EQ -0.703*** -3.058** -0.409*** -3.675*** (-3.19) (-2.31) (-5.33) (-10.10)

Controls Included Included Included Included N 6065 6317 2855 2910 Adj. R2 0.650 0.700 0.349 0.357 Note: See Appendix A for detailed definitions of all variables. This table reports the regression results when we split the sample into terciles based on the value of MD_EQ. Panel A (B) presents the results for cost of capital test (bid-ask spread test and cost of debt test). We include the same set of firm-level control variables in regressions; the coefficient estimates on control variables are not reported for brevity. All regressions in cost of capital and bid-ask spread tests include firm and year fixed effects and regressions in cost of debt tests include year and industry fixed effects. Robust t-statistics, based on standard errors clustered at a firm level, are presented in parentheses. ***, **, and * denote statistical significance at the 0.01, 0.05, and 0.10 levels, respectively.

46

Table 7 Test of the Relation between Cost of Capital and Voluntary and Mandatory Disclosure:

Sample Partitioned by MD_Event Panel A: Dependent variable is implied cost of equity

Dependent Variable = rMPEG

Dependent Variable = rDIV Dependent Variable = rSTPEG

Low MD_Event

High MD_Event

Low MD_Event

High MD_Event

Low MD_Event

High MD_Event

VD_8K 0.022** 0.016** 0.098*** 0.024* 0.015 0.014* (2.32) (2.12) (3.72) (1.83) (1.52) (1.69)

MD_Event 0.147*** 0.0818*** 0.1617 0.053** 0.188*** 0.077*** (3.74) (3.98) (1.63) (2.45) (4.42) (3.45)

MD_EQ -0.773** -0.682* -2.533** -2.162** -0.788** -0.678* (-2.00) (-1.85) (-2.56) (-2.30) (-2.02) (-1.80)

Controls Included Included Included Included Included Included N 6100 7145 6100 7145 5568 6401 Adj. R2 0.233 0.254 0.373 0.402 0.272 0.301 Panel B: Dependent variable is bid-ask spread/cost of debt

Dependent Variable = AvgBAS Dependent Variable = SPRating Low MD_Event High MD_Event Low MD_Event High MD_Event

VD_8K -0.020*** -0.000 0.029*** 0.010*** (-3.01) (-0.09) (7.23) (7.59)

MD_Event 0.013 0.023*** 0.027 0.005 (0.41) (3.70) (1.25) (1.28)

MD_EQ -0.069 -0.166 -0.769*** -1.398*** (-0.88) (-1.06) (-5.82) (-7.09)

Controls Included Included Included Included N 6000 6993 2649 3069 Adj.R2 0.316 0.274 0.385 0.366 Note: See Appendix A for detailed definitions of all variables. This table reports the regression results when we split the sample into terciles based on the value of MD_Event. Panel A (B) presents the results for the cost of capital test (bid-ask spread test and cost of debt test). We include the same set of firm-level control variables in regressions; the coefficient estimates on control variables are not reported for brevity. All regressions in cost of capital and bid ask spread tests includes firm and year fixed effects and regressions in cost of debt tests include year and industry fixed effects. Robust t-statistics, based on standard errors clustered at a firm level, are presented in parentheses. ***, **, and * denote statistical significance at the 0.01, 0.05, and 0.10 levels, respectively.

47

Table 8 Test of the Relation between Cost of Capital and Voluntary and Mandatory Disclosure:

Management Forecasts as Alternative Proxy for Voluntary Disclosure Panel A: Dependent variable is implied cost of equity – rMPEG

(1) (2) (3) (4) (5) (6) (7) VD_MF -0.759*** -0.74*** -0.758*** -0.746*** (-7.67) (-7.63) (-7.66) (-7.62) MD_Event 0.070*** 0.070*** 0.070*** 0.070*** (8.27) (8.27) (8.20) (8.20) MD_EQ -0.465*** -0.468*** -0.464*** -0.467*** (-5.38) (-4.81) (-5.55) (-4.96) Controls Included Included Included Included Included Included Included N 18766 18766 18766 18766 18766 18766 18766 Adj. R2 0.508 0.509 0.506 0.510 0.512 0.509 0.513 Panel B: Dependent variable is bid-ask spread

(1) (2) (3) (4) (5) (6) (7) VD_MF -0.180** -0.169* -0.181** -0.170** (-2.07) (-1.95) (-2.08) (-1.96) MD_Event 0.0586*** 0.058*** 0.058*** 0.058*** (6.03) (6.00) (6.02) (5.99) MD_EQ -0.559*** -0.555*** -0.559*** -0.555*** (-4.42) (-4.19) (-4.44) (-4.20) Controls Included Included Included Included Included Included Included N 25459 25459 25459 25459 25459 25459 25459 Adj. R2 0.658 0.659 0.658 0.659 0.659 0.658 0.659 Panel C: Dependent variable is cost of debt

(1) (2) (3) (4) (5) (6) (7) VD_MF -0.240*** -0.222*** -0.215*** -0.200*** (-5.04) (-4.67) (-4.47) (-4.16) MD_Event 0.029*** 0.027*** 0.028*** 0.0258*** (4.67) (4.37) (4.26) (4.03) MD_EQ -1.173*** -1.109*** -1.099*** -1.046*** (-11.36) (-3.98) (-3.95) (-3.92) Controls Included Included Included Included Included Included Included N 8063 8063 8063 8063 8063 8063 8063 Pseudo R2 0.354 0.355 0.360 0.367 0.361 0.366 0.372 Note: See Appendix A for definitions of all variables. This table reports main regression results. Panel A reports the results for the implied cost of equity tests. We present the results with rMPEG only; results using alternative implied cost of equity are substantively similar. Panel B and C present results using bid-ask spread and cost of debt, respectively. All regressions in Panel A and B include firm and year fixed effects and regressions in Panel C include year and industry fixed effects. Robust t-statistics, based on standard errors clustered at a firm level, are presented in parentheses. Coefficient estimates in Panel A are multiplied by 1000 for ease of exposition. ***, **, and * denote statistical significance at the 0.01, 0.05, and 0.10 levels, respectively.