the determinants and consequences of disclosure committee

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Theses and Dissertations

7-2015

The Determinants and Consequences ofDisclosure Committee AdoptionLyle Roy SchmardebeckUniversity of Arkansas, Fayetteville

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The Determinants and Consequences of Disclosure Committee Adoption

The Determinants and Consequences of Disclosure Committee Adoption

A dissertation submitted in partial fulfillment

of the requirements for the degree of

Doctor of Philosophy in Business Administration

by

Lyle Roy Schmardebeck

Brigham Young University

Bachelor of Science in Accounting, 2009

Brigham Young University

Master of Accountancy, 2009

July 2015

University of Arkansas

This dissertation is approved for recommendation to the Graduate Council.

_________________________________

Dr. Linda A. Myers

Dissertation Director

_________________________________ _________________________________

Dr. Cory A. Cassell Dr. James N. Myers

Committee Member Committee Member

_________________________________

Dr. Kangzhen Xie

Committee Member

Abstract

After the passage of the Sarbanes-Oxley Act of 2002, the Securities and Exchange

Commission recommended that companies voluntarily adopt disclosure committees to aid in

preparing company disclosures. In this paper, I investigate the determinants and consequences

of disclosure committee adoption. I find that companies with material weaknesses in internal

controls over financial reporting and less readable 10-K filings are more likely to adopt

disclosure committees. In consequences analyses, using a propensity score matched control

sample and a difference-in-differences research design, I find that 10-K filings are longer and

less readable after disclosure committee adoption. However, consistent with institutional theory,

I do not find evidence of a reduction in information asymmetry or an increase in the

informativeness of earnings following disclosure committee adoption.

Acknowledgments

I am grateful for the guidance and instruction of my dissertation chair, Linda Myers, and

the members of my dissertation committee, Cory Cassell, James Myers, and Kenneth Xie. I also

thank Ben Anderson, T. J. Atwood, Cari Burke, Lauren Cunningham, Andrew Doucet, Taylor

Joo, Stacey Kaden, Gary Peters, Michael Stuart, and seminar participants at the University of

Arkansas, the University of Missouri, and the Miami Rookie Camp for providing helpful

comments and suggestions. I express gratitude to my family for their support and

encouragement. Finally, I express gratitude to my wife, Brenna, because I wouldn’t have been

able to accomplish my goals without her love, friendship, and dedication.

Table of Contents

I. Introduction ....................................................................................................................1

II. Background, Prior Literature, and Hypothesis Development ........................................6

A. Disclosure Committee ....................................................................................................6

B. Annual Report Readability ............................................................................................7

C. Information Asymmetry.................................................................................................9

D. Earnings Informativeness.............................................................................................10

III. Research Design...........................................................................................................12

A. Determinants of Disclosure Committee Adoption .......................................................12

B. Propensity Score Matching and Difference in Differences .........................................15

C. Readability Tests ..........................................................................................................16

D. Information Asymmetry Tests .....................................................................................18

E. Earnings Informativeness Tests ...................................................................................20

IV. Sample Selection and Data ..........................................................................................22

V. Results ..........................................................................................................................24

A. Determinants Test and Propensity Score Matching .....................................................24

B. Readability Tests ..........................................................................................................27

C. Information Asymmetry Tests .....................................................................................29

D. Earnings Informativeness Tests ...................................................................................29

VI. Additional Analyses .....................................................................................................30

A. Specific Word Types....................................................................................................30

B. Disclosure Committee Composition Tests...................................................................32

C. Internal Control over Financial Reporting ...................................................................35

VII. Robustness Tests ..........................................................................................................36

A. Timing of Disclosure Committee Adoption ................................................................36

B. Measurement Window of Bid-Ask Spreads and Market Illiquidity ............................37

C. Alternative Measure of Information Asymmetry ........................................................37

VIII. Conclusion ...................................................................................................................38

IX. References ....................................................................................................................41

X. Appendix A: Sample Disclosure Committee Charter ..................................................46

XI. Appendix B: Variable Definitions ...............................................................................48

List of Tables

1. Disclosure Committee Sample Summary ....................................................................52

Panel A: Disclosure Committee and Control Observations by Year ...........................52

Panel B: Disclosure Committee and Control Observations by Fama and French

(1997) Industry.............................................................................................................53

2. Descriptive Statistics for Determinants Model ............................................................55

3. Determinants Model with Fama and French (1997) Industry Classification ...............56

4. Determinants Model with One Digit SIC Codes .........................................................57

5. Propensity Score Matching ..........................................................................................58

Panel A: Sample Selection ...........................................................................................58

Panel B: Covariate Balance .........................................................................................58

6. Difference in Governance Controls .............................................................................59

Panel A: Before Adoption ......................................................................................59

Panel B: After Adoption ........................................................................................59

7. Difference in Differences Sample Selection ................................................................60

Panel A: Readability Tests .....................................................................................60

Panel B: Information Asymmetry Tests ................................................................60

Panel C: Earnings Informativeness Tests ..............................................................60

8. Number of Words Test.................................................................................................61

9. Gross File Size Test .....................................................................................................62

10. Net File Size Test ........................................................................................................63

11. Bid Ask Spread Test ....................................................................................................64

12. Market Illiquidity Test .................................................................................................65

13. ERC Test ......................................................................................................................66

14. FERC Test ...................................................................................................................67

15. Number of Positive Words Test ...................................................................................68

16. Number of Negative Words Test .................................................................................69

17. Number of Neutral Words Test....................................................................................70

18. Percentage of Positive Words Test ..............................................................................71

19. Percentage of Negative Words Test .............................................................................72

20. Percentage of Neutral Words Test ...............................................................................73

21. Disclosure Committee Composition Descriptive Statistics .........................................74

22. Disclosure Committee Composition Number of Words Test ......................................75

23. Disclosure Committee Composition Gross File Size Test ...........................................76

24. Disclosure Committee Composition Net File Size Test ..............................................77

25. Disclosure Committee Composition Percentage of Positive Words Test ....................78

26. Disclosure Committee Composition Percentage of Negative Words test ...................79

27. Disclosure Committee Composition Bid-Ask Spread Test .........................................80

28. Disclosure Committee Composition Illiquidity Test ...................................................81

29. Disclosure Committee Composition ERC Test ...........................................................82

30. Disclosure Committee Composition FERC Test .........................................................83

31. Disclosure Committee Adoption and Current Material Weaknesses ..........................84

32. Disclosure Committee Adoption and Future Material Weaknesses ............................85

33. Number of Words Test Excluding Unclear Adoption Timing Observations ..............86

34. Gross File Size Test Excluding Unclear Adoption Timing Observations ...................87

35. Net File Size Test Excluding Unclear Adoption Timing Observations .......................88

36. Bid-Ask Spread Test Excluding Unclear Adoption Timing Observations ..................89

37. Market Illiquidity Test Excluding Unclear Adoption Timing Observations ...............90

38. ERC Test Excluding Unclear Adoption Timing Observations ....................................91

39. FERC Test Excluding Unclear Adoption Timing Observations .................................92

40. Bid-Ask Spread Test Measuring BA_SPREAD over the fiscal year ............................93

41. Market Illiquidity Test Measuring ILLIQUIDITY over the fiscal year ........................94

42. Residual Volatility Test ...............................................................................................95

1

I. Introduction

In an effort to assist companies in dealing with additional disclosure requirements and

increased scrutiny over financial reporting practices following the adoption of the Sarbanes-

Oxley Act of 2002 (SOX), the Securities and Exchange Commission (SEC) recommended that

companies adopt disclosure committees “with the responsibility of determining the materiality of

information and determining disclosure obligations on a timely basis” (SEC 2002; Bevilacqua

2004). Although disclosure committee adoption is voluntary, regulators and practitioners believe

that a disclosure committee is an essential part of a company’s communication process (SEC

2002; Deloitte and Touche 2003; Deloitte 2013).

Disclosure committee responsibilities can include reviewing the design and operation of

disclosure controls, determining whether periodic filings (e.g., 8-K or Form 4 filings) are

necessary, and drafting and reviewing quarterly and annual filings (i.e., 10-Qs and 10-Ks).

Although the composition of disclosure committees is not regulated, most are comprised of

management (e.g., the Chief Executive Officer (CEO), Chief Financial Officer (CFO), Chief

Accounting Officer, General Counsel, and Chief Investor Relations Officer) and board members

(Bocchio and Daly 2007; McCarthy 2008). Most disclosure committees meet at least quarterly

(KPMG 2011a) and report directly to the CEO or CFO (Deloitte 2013; WSJ 2013). Therefore,

committee meetings can require a significant investment of management time and company

resources. Although regulators, practitioners, and the business press highlight the potential

benefits of disclosure committee adoption, to my knowledge, no prior studies examine why

companies form disclosure committees and whether disclosure committee adoption influences

disclosure quality and/or market outcomes.

2

In this study, I examine the determinants and consequences of disclosure committee

adoption. To perform my analyses, I hand collect a sample of disclosure committee adopters by

performing keyword searches of SEC filings on EDGAR. Using a sample of 764 disclosure

committee adoptions, I find that companies are more likely to adopt disclosure committees when

there are prior year material weaknesses in internal controls over financial reporting or when

prior year 10-K filings are less readable. I also find that disclosure committee adoption is more

likely among larger companies and companies engaging a Big N auditor.

Next, I investigate how disclosure committee adoption influences corporate disclosures.

Companies might adopt disclosure committees to facilitate the transfer of information across the

entity or to signal improved disclosure quality and reduce agency costs.1 However, under

institutional theory, corporate governance mechanisms may be “primarily ceremonial and serve

as symbols of effective oversight” (Beasley et al. 2009, p. 69). Thus, disclosure committee

adoption may not have an effect on disclosure quality.

I first examine the impact of adoption on the length and readability of 10-K filings. The

SEC’s Plain English Rule mandates clear language that avoids “boilerplate” disclosure (SEC

1998). More recently, the SEC has undertaken projects to reduce disclosure overload and

improve the effectiveness of disclosures.2 Prior research suggests that shorter and more readable

10-K filings are more informative to investors and analysts (Li 2008; Miller 2010; Lehavy et al.

1 Suggested benefits of disclosure committees include improving disclosure quality by

formalizing the disclosure process and aiding in the accumulation of financial information,

determining the materiality of transactions, and communicating this information to management

in a timely manner (Bocchio and Daly 2007; McCarthy 2008). 2 For example, the SEC is currently working to help companies improve disclosure quality by

suggesting that they remove duplicate, redundant, or unnecessary disclosure from 10-K filings

and by identifying areas where additional disclosure may be useful or necessary (see SEC

Changes Tack on Disclosure Overload Project, available at:

http://blogs.wsj.com/cfo/2014/05/01/sec-changes-tack-on-disclosure-overload-project/).

3

2011; Loughran and McDonald 2014). If disclosure committee adoption represents a

commitment to improved disclosure quality, then I expect 10-K filings to be shorter and more

readable post-adoption. However, if disclosure committee adoption is primarily ceremonial, then

the length and readability of 10-K filings should be unaffected or 10-K filings should become

longer and less readable post-adoption.

I also examine whether the adoption of a disclosure committee influences the level of

information asymmetry and the informativeness of earnings. Prior research suggests that

increased disclosure quality reduces the likelihood that investors discover and trade on private

information and therefore decreases information asymmetry (Brown and Hillegeist 2007). Thus,

if disclosure committee adoption improves disclosure quality, then I expect that the level of

information asymmetry will decrease post-adoption. However, if disclosure committee adoption

is primarily ceremonial in nature or decreases disclosure quality, then I expect no change or an

increase in information asymmetry after disclosure committee adoption. Prior research also

suggests that more informative disclosure can improve the informativeness of earnings (Healy et

al. 1999) and can increase the amount of future earnings news reflected in current returns

(Lundholm and Myers 2002). If disclosure committee adoption improves disclosure quality,

then I expect the informativeness of earnings to increase post-adoption. However, if disclosure

committees do not change or reduce disclosure quality, then I expect no change or a decrease in

the informativeness of earnings post-adoption.

In my consequences tests, I use a difference-in-differences research design and select a

control sample of companies that did not adopt a disclosure committee using a propensity score

matching method that controls for observable company characteristics (Rosenbaum and Rubin

1983). Specifically, I match each company in the treatment sample (i.e., each of the disclosure

4

committee adopters) with a company that does not adopt a disclosure committee but is as likely

to adopt given its observable characteristics.

I find that companies adopting disclosure committees file longer and less readable 10-Ks

post-adoption. Specifically, I find that disclosure committee adoption is associated with an 8

percent increase in the number of words in the 10-K filing and an 8 percent increase in gross 10-

K file size, representing an increase in length of 6 pages of single spaced text, on average. Thus,

disclosure committee adoption does not improve the readability of the annual 10-K filing. In

addition, I find no relation between disclosure committee adoption and level of information

asymmetry or between disclosure committee adoption and the informativeness of earnings.

Taken together, these results suggest that disclosure committee adoption is associated with

longer and less readable 10-K filings, but these longer and less readable 10-K filings do not

influence market outcomes.

In additional analyses, I examine the relation between disclosure committee adoption and

specific word types used in the 10-K filing. After disclosure committee adoption, I find an

increase in positive, negative, and neutral words. However, I do not find a change in the

proportions of positive, negative, or neutral words to total words in the 10-K after adoption.

These results suggest disclosure committee adoption is associated with an increase in words in

general and do not change the sentiment or tone of the 10-K filing. Next, I use a subsample of

companies that disclose the composition of their disclosure committee and investigate whether

the composition of the disclosure committee impacts the length and complexity of the 10-K

filing, information asymmetry, and earnings informativeness. In these tests, I find some

evidence that the inclusion of senior management on the disclosure committee is associated with

longer 10-K filings. I do not find robust evidence suggesting that the inclusion of senior

5

management on the disclosure committee influences information asymmetry or the

informativeness of earnings. Last, I examine the relation between disclosure committee adoption

and the quality of internal controls over financial reporting. I find an increase in likelihood of an

internal control weaknesses reported in the year of disclosure committee adoption. However, I

also find a decrease in the likelihood of internal control weaknesses reported in the year after

adoption. Taken together, these results suggest that disclosure committee adoptions are a signal

of an improvement in current and future internal controls over financial reporting.

My findings should be of interest to academic researchers, regulators, investors, and other

stakeholders interested in the determinants and consequences of disclosure committee adoption.

Consistent with institutional theory, my findings suggest that the disclosure committee adoption

is primarily ceremonial in that although it affects the amount of quantity of disclosure, it does not

improve disclosure quality. This suggests that the investment of management time and company

resources may not benefit shareholders. Finally, my finding that disclosure committee adoption

increases the length and decreases the readability of 10-K filings could be problematic given

recent efforts by the SEC to reduce disclosure overload.

The remainder of this paper is organized as follows. In Section 2, I provide background

information about disclosure committees, review prior literature, and develop my hypotheses. In

Section 3, I describe my research design. Section 4 describes my sample selection and data.

Section 5 presents the results of my empirical tests. Section 6 presents additional analyses.

Section 7 describes my robustness tests and Section 8 offers concluding remarks.

6

II. Background, Prior Literature, and Development of Hypotheses

A. Disclosure Committees

Disclosure committee adoption represents a significant investment of management time

and company resources. In general, disclosure committees provide guidelines for disclosure and

determine the appropriateness of public disclosures. Specifically, disclosure committees should

ensure that company filings are fair, accurate, timely, and complete by creating, reviewing, and

disseminating financial disclosures made by the company (Deloitte and Touche 2003).3

Typically, one of the primary responsibilities of the disclosure committee is to draft and

review the annual 10-K filing. The 10-K is one of the most important documents filed by a

public company because it contains both summary information related to prior year performance

and forward-looking information that helps investors anticipate future performance.4 Proponents

suggest that disclosure committees can help managers determine what information to disclose in

the 10-K as well as how and when to disclose this information. For example, Tysiac (2012)

suggests that the disclosure committee can help improve reporting in the Management

Discussion & Analysis section of the 10-K by monitoring such items as financial covenants,

regulatory and legislative changes, problems with customers and suppliers, and litigation

developments. Disclosure committees can take the lead in benchmarking key disclosures against

industry peers and advising management on which disclosures to include or exclude from

company filings (McCarthy and Iannaconi 2010). Moreover, disclosure committees can reduce

3 An example of the responsibilities section of a Disclosure Committee Charter appears in

Appendix A. Among the responsibilities listed, Cardinal Health’s Disclosure Committee has

oversight of the preparation of the Company’s 10-K filing. 4 I follow Griffin (2003) and Loughran and McDonald (2014) and focus on annual 10-K filings

rather than 10-K and 10-Q filings because prior research suggests that 10-K filings are more

informative than 10-Q filings to investors.

7

the complexity of financial disclosures made in 10-K filings (KPMG 2011b). Alternatively,

according to an audit committee member of a company listed on the New York Stock Exchange,

disclosure committees place a burden on management and on the audit committee by adding

another layer of bureaucracy to the organization (Tremblay and Gendron 2011).

Companies might adopt disclosure committees to reduce agency costs by providing a

signal of improved disclosure quality. However, the adoption of a disclosure committee might

also be a ceremonial governance choice that doesn’t improve disclosure quality or provide

perceived benefits to shareholders. As such, disclosure committee adoption could be consistent

with institutional theory, which states that some corporate governance structures are merely

symbolic and do not actually improve corporate governance (Scott 1987). In this paper, I

examine whether disclosure committee adoption provides real or perceived benefits to

shareholders by investigating whether disclosure committee adoption influences the readability

of the annual report, information asymmetry, and informativeness of earnings.

B. Annual Report Readability

Li (2008) was the first to document the effects of complex language in the 10-K filing; he

finds that less readable 10-K filings (i.e., 10-Ks that are less readable) are associated with poor

future performance and lower earnings persistence. You and Zhang (2009) find that investor

underreaction to information in 10-K filings is more pronounced for companies with lengthy 10-

Ks, and Miller (2010) finds lower overall trading volume for companies with longer and less

readable annual reports.5 In addition, when 10-Ks are less readable, analyst forecast dispersion

and analyst forecast uncertainty are greater and analyst forecasts are less accurate (Lehavy et al.

2011). Moreover, experimental research also finds evidence that the readability of disclosure

5 This lower overall trading volume is driven by less frequent trading by small investors.

8

impacts investors. For example, Rennekamp (2012) finds that investors experience processing

disfluency and rely less on disclosures when readability is poor. Taken together, prior research

suggests that investors and analysts find longer and more complex 10-K disclosures less useful

(Libby and Emett 2014).

Because disclosure committees are often responsible for drafting and reviewing 10-K

filings, I examine the impact of disclosure committee adoption on 10-K disclosure quality. On

the one hand, disclosure committees could improve disclosure quality by helping top

management obtain and summarize information, and draft and review disclosures. Having a

formalized disclosure process and including management from multiple areas within a company

(e.g., legal, investor relations, accounting, and operations) could improve the readability of 10-K

filings. In addition, disclosure committees could improve the readability of 10-K filings by

drafting disclosures that are shorter and more informative. On the other hand, disclosure

committee adoption could lead to longer and less informative 10-K filings because committee

members may increase the length or decrease the readability of 10-Ks because of regulatory or

legal concerns or because committee members may feel obligated to increase disclosure

(regardless of content) given that such a committee has been formed. Because the adoption of a

disclosure committee could increase or decrease the length and/or readability of 10-K filings, my

first null hypothesis is as follows:

H0a: Disclosure committee adoption does not influence the length or readability of a

company’s 10-K filing.

To test this hypothesis, I examine the change in 10-K length and readability following disclosure

committee adoption.

9

Many prior studies use the Fog Index, which measures readability as a function of

average sentence length and the number of complex words, to proxy for readability.6 However,

Loughran and McDonald (2014) find that the Fog Index is misspecified when used to measure

the readability of 10-K filings because many of the “complex words” in the 10-K filing are

simple business terms, rather than words that are difficult to read or understand. Loughran and

McDonald (2014) suggest that the file size of the 10-K filing provides a better measure of

readability because it correlates more strongly than the Fog Index with returns volatility, analyst

forecast error, and analyst forecast dispersion. Thus, I use the 10-K gross file size and net file

size, following Loughran and McDonald (2014), to measure the length and readability of the 10-

K filing. The 10-K gross file size variable is the size of the 10-K filing as posted on EDGAR

and the net file size variable is the size of the 10-K filing when only textual content is included.

Loughran and McDonald (2014) use both file size measures to proxy for readability. I also

follow You and Zhang (2009) and Miller (2010) and use the number of words in the 10-K filing

as a simple proxy for 10-K length and complexity.

C. Information Asymmetry

Information asymmetry allows informed investors to benefit by trading based on their

private information. Economic theory suggests that increased disclosure quality reduces

information asymmetry (e.g., between the company and market participants or between informed

and uninformed investors) and many empirical studies provide supporting evidence (Healy et al.

2001; Beyer et al. 2010). In addition, Brown and Hillegeist (2007) suggest that increased

disclosure quality reduces investor incentives for private information search, thereby decreasing

information asymmetry.

6 See, for example, Li (2008) Biddle et al. (2009), Lehavy et al. (2011), and Lawrence (2013).

10

Because disclosure committees are charged with increasing disclosure quality in general

and ensuring that disclosures are complete, accurate, and relevant, changes to disclosures made

by the disclosure committee could reduce information asymmetry by increasing the amount of

information available to investors. However, because disclosure committees could reduce

disclosure or decrease disclosure quality, information asymmetry could increase or be unaffected

by disclosure committee adoption. Stated in the null, my second hypothesis is as follows:

H0b: Disclosure committee adoption does not influence information asymmetry.

To test this hypothesis, I use two common proxies for information asymmetry – bid-ask spreads

and stock illiquidity – and examine the change in information asymmetry after disclosure

committee adoption.

Bid-ask spreads are a common proxy for the level of information asymmetry because

they capture the adverse selection problem that arises from trading in the presence of

asymmetrically informed investors (Healy et al. 1999; Leuz and Verrecchia 2000). Prior

research also uses the level of stock illiquidity to proxy for information asymmetry. This

research suggests that a reduction in information asymmetry resulting from an increase in

disclosure increases the liquidity of a company’s stock (Amihud and Mendelson 1986; Diamond

and Verrecchia 1991). Thus, higher levels of stock illiquidity represents greater information

asymmetry.

D. Earnings Informativeness

Decades of accounting research investigate the returns-earnings relation (Kothari et al.

2001). This research suggests that current stock returns reflect investor beliefs about current and

future earnings. When investors perceive current earnings to be more informative about

company value, the relation between current stock returns and current earnings surprise (i.e., the

earnings response coefficient (ERC)) will be greater. In addition, stock returns reflect investor

11

beliefs about future performance (Beaver et al. 1980) such that future earnings explain a large

amount of the variation in current stock returns (Collins et al. 1994). Recent literature examines

the impact of disclosure on the relation between current returns and current and future earnings.

This research suggests that companies with large increases in disclosure activity experience an

increase in ERCs (Healy et al. 1999), and that companies with more informative disclosures

experience higher future earnings response coefficients (FERCs) (Gelb and Zarowin 2002;

Lundholm and Myers 2002; Choi et al. 2011).

The SEC and practitioners suggest that disclosure committees should improve the

relevance, timeliness, and credibility of reported performance. Audit firms also suggest that

disclosure committees should help to increase the credibility of reported earnings (PwC 2006).

If disclosure committee adoption improves disclosure quality and the relevance of earnings, then

ERCs will increase following disclosure committee adoption. In addition, if disclosure

committees increase the timeliness and credibility of reported earnings, then FERCs should

increase post-disclosure committee adoption. However, because disclosure committee adoption

may be ceremonial, my third and fourth null hypotheses are as follows:

H0c: Disclosure committee adoption does not affect the market’s response to earnings

news.

H0d: Disclosure committee adoption does not affect the amount of future earnings news

reflected in current returns.

To test this hypothesis, I examine the effect of disclosure committee adoption on both ERCs and

FERCs.

12

III. Research Design

A. Determinants of Disclosure Committee Adoption

Examining the consequences of disclosure committee adoption requires controlling for

factors occurring concurrently with the formation of disclosure committees. One way to do this

is to establish a prediction model for disclosure committee adoption and use this to identify a

propensity score matched control sample of companies that do not adopt a disclosure committee.

In developing my prediction model, I acknowledge that some companies may use

disclosure committee adoption to signal improvements in disclosure quality as a means to reduce

agency costs. Therefore, I expect companies with poor quality disclosures in year t-1 to be more

likely to adopt disclosure committees because this can signal to shareholders that they are

making efforts to improve current and future disclosure quality. I also expect larger companies,

higher-growth companies, and companies with more complex operations to be more likely to

adopt disclosure committees because they can assist management in gathering financial

information and drafting and reviewing disclosures. Finally, I include company profitability,

leverage, age, and level of institutional ownership, as well as auditor characteristics in the year

before adoption in my prediction model.7 My prediction model is as follows:

PR(DC_ADOPTt) = α0 + α1 RES_ANNOUNCEit-1 + α2 MWEAKNESS302it-1

+ α3 GROSSFILESIZEit-1 + α4 SIZEit-1 + α5 BTM it-1 + α6 M&Ait-1

+ α7 FINANCINGit-1 + α8 SEGMENTSit-1 + α9 FOREIGNit-1

+ α10 RET_VOLit-1 + α11 STD_SALEit-1 + α12 STD_CFOit-1

+ α13 AR_INit-1 + α14 SPECIAL_ITEMSit-1 + α15 OP_CYCLEit-1 + α16 ROAit-1

+ α17 LEVERAGEit-1 + α18 AGEit-1 + α19 %INST_HOLDit-1 + α20 BIGNit-1

+ α21 AUDITOR_TENUREit-1 + αj INDUSTRYFEit-1 + αk YEARFEit-1 (1)

7 Governance data in machine-readable form is unavailable for half of my sample companies so

include governance variables in my models would result in significant sample attrition.

However, in untabulated analyses, I find no difference in governance characteristics (i.e., audit

committee size, percentage of outsiders on the audit committee, and percentage of outsiders on

the board of directors) between treatment and control companies either before or after disclosure

committee adoption.

13

where:

DC_ADOPTt = an indicator variable set to one if the company adopted a

disclosure committee during year t, zero otherwise;

RES_ANNOUNCEt-1 = an indicator variable set to one if the company announced

a restatement in year t-1 due to misstatements in prior year

financial statements, zero otherwise (Audit Analytics);

MWEAKNESS302t-1 = an indicator variable set to one if the company disclosed

a material weakness in internal control over financial

reporting under SOX Section 302 in year t-1, zero

otherwise (Audit Analytics);

GROSSFILESIZEt-1 = the natural log of the gross file size of the company’s 10-

K filing in year t-1 (available from Bill McDonald’s

website)8;

SIZEt-1 = the natural log of total assets in year t-1 (Compustat: AT);

BTMt-1 = book-to-market in year t-1, calculated as the book value

of common equity (Compustat: CEQ) divided by the

market value of equity (Compustat: CHSO x PRCC_F);

M&At-1 = an indicator variable set to one if the company engaged in

a merger or acquisition in year t-1, and zero otherwise

(Compustat: SALE_FN);

FINANCINGt-1 = an indicator variable set to one if M&A is not equal to

one and the number of shares outstanding (Compustat:

CHSO) increased by at least 10 percent during the year, or

if M&A is not equal to one and long-term debt increased by

at least 20 percent during the year, zero otherwise;

SEGMENTSt-1 = the natural log of the number of business segments in

year t-1 (Compustat Segment File);

FOREIGNt-1 = the natural log of the number of foreign segments in year

t-1 (Compustat Segment File);

RET_VOLt-1 = the standard deviation of daily stock returns in year t-1;

8 The 10-K readability data used in Loughran and McDonald (2014) are available at

http://www3.nd.edu/~mcdonald/Word_Lists.html.

14

STD_SALEt-1 = the standard deviation of sales (Compustat: SALE) for the

prior three years ending in year t-1;

STD_CFOt-1 = the standard deviation of operating cash flows

(Compustat: OANCF) for the prior three years ending in

year t-1;

AR_INt-1 = the proportion of accounts receivable (Compustat: RECT)

and inventory (Compustat: INVT) in total assets

(Compustat: AT) in year t-1;

SPECIAL_ITEMSt-1 = special items (Compustat: SPI) scaled by total assets

(Compustat: AT) in year t-1;

OP_CYCLEt-1 = the company’s operating cycle in year t-1, calculated as

the number of days sales in accounts receivable plus the

number of days sales in inventory;

ROAt-1 = return on assets in year t-1, measured as income before

extraordinary items (Compustat: IB) scaled by total assets

(Compustat: AT);

LEVERAGEt-1 = the debt-to-assets ratio in year t-1 (Compustat: DLTT /

AT);

AGEt-1 = company age in year t-1, calculated as the number of

years during which the company reports total assets on

Compustat (Compustat: AT) greater than zero;

%INST_HOLDt-1 = the percentage of institutional holdings in year t-1

(Thomson Reuters Institutional Holdings);

BIGNt-1 = an indicator variable set to one if the company is audited

by a Big N auditor in year t-1, zero otherwise;

AUDITOR_TENUREt-1 = the length of the company-auditor relationship to date;

FF_INDUSTRYFE = industry indicator variables defined using the Fama and

French (1997) industry classifications; and

YEARFE = fiscal year indicator variables.

15

B. Propensity Score Matching and Difference-in-Differences

Most sample companies disclose the presence of a disclosure committee in their 10-Q,

10-K, or DEF 14A filings. In each instance, I ensure that the disclosure committee was formed

and operating before the fiscal year-end date used to measure my consequences (outcome)

variables. I estimate Equation (1) using the disclosure committee adoption year for my treatment

companies (i.e., I remove the pre-and post-adoption years for companies that adopt a disclosure

committee) and all of the available company-years in the sample period for companies that did

not adopt a disclosure committee.9 I use logit regression with robust standard errors clustered by

company because non-disclosure committee adopters can appear in the sample multiple times. I

use the results from this estimation to propensity score match each treatment company to a

control company in the same year using the caliper method (without replacement). This method

allows me to control for observable company characteristics in my consequences analyses

(Rosenbaum and Rubin 1983). The sets of treatment companies and matched control companies

comprise my sample companies for all remaining analyses.

Because I am interested in the effects of disclosure committee adoption, I use a

difference-in-differences research design. Specifically, my sample includes the year before and

the year after disclosure committee adoption for both treatment companies (i.e., those adopting

disclosure committees) and the propensity score matched control companies (i.e., those not

adopting disclosure committees).10 In all outcome analyses, I also include controls for time-

variant characteristics that could influence the outcome variable.

9 I use include all years for the non-disclosure committee adopters because they could have

adopted the committee in any of the years of my sample. 10 The propensity score matched sample also allows me to overcome concerns about violating the

parallel trends assumption that is important for difference-in-differences research designs

(Roberts and Whited 2011).

16

C. Readability Tests

To examine the relation between disclosure committee adoption and readability of the 10-

K filing, I estimate the following cross-sectional regression using ordinary least squares (OLS):

READ_VARit = β0 + β1 DCit + β2 AFTERit + β3 DCit x AFTERit + β4 SPECIAL_ITEMSit + β5 M&Ait

+ β6 FINANCINGit + β7 RET_VOL3it + β8 LOSSDit + β9 STD_INCOMEit

+ β10 DELAWAREit + β11 %INST_HOLDit + β12 SIZEit + β13 BTMit

+ β14 FOREIGNit + β15 SEGMENTSit + βj YEARFE + βk FF_INDUSTRYFE

+ µit (2)

where:

READ_VAR = readability, defined as the log number of words in the 10-K filing

(#WORDSt), the log of the 10-K’s gross file size

(GROSSFILESIZEt), or the log of the 10-K’s net file size

(NETFILESIZEt);

DCt = an indicator variable set to one for companies that adopted a

disclosure committee, and zero otherwise;

AFTERt = an indicator variable set to one in the post-adoption period, and

zero otherwise;11

SPECIAL_ITEMSt = special items (Compustat: SPI) scaled by total assets

(Compustat: AT);

M&At = an indicator variable set to one if the company was engaged in a

merger or acquisition during the year, and zero otherwise

(Compustat: SALE_FN);

FINANCINGt = an indicator variable set to one if M&A is not equal to one and

the number of shares outstanding (Compustat: CHSO) increased by

at least 10 percent during the year, or if M&A is not equal to one

and long-term debt increased by at least 20 percent during the year,

zero otherwise;

RET_VOL3t = the standard deviation of daily stock returns over the prior three

years (CRSP: RET);

11 For each control group observation, I set AFTER equal to one based on the year in which that

the matched treatment observation adopted a disclosure committee.

17

LOSSDt = an indicator variable set to one if the company reports a loss

during the year (Compustat: NI), zero otherwise;

STD_INCOMEt = the standard deviation of earnings (Compustat: IB) over the prior

three years;

DELAWAREt = an indicator variable set to one if the company is incorporated in

the state of Delaware, zero otherwise;

%INST_HOLDt = the percentage of institutional holdings (Thomson Reuters

Institutional Holdings database);

SIZEt = the natural log of total assets (Compustat: AT);

BTMt = book-to-market, calculated as the book value of common equity

(Compustat: CEQ) divided by the market value of equity

(Compustat: CHSO x PRCC_F);

FOREIGNt = the natural log of the number of foreign segments (Compustat

Segment File);

SEGMENTSt = the natural log of the number of business segments (Compustat

Segment File);

µit = the error term; and

all other variables as previously defined.

β3 is the coefficient of interest. It represents the difference-in-differences in readability, between

adopter and control companies, from before to after disclosure committee adoption.

Equation (2) includes controls from prior research that are associated with the length and

readability of 10-K filings. Specifically, I control for the magnitude of special items

(SPECIAL_ITEMSt), mergers and acquisitions (M&At), equity or debt financing (FINANCINGt),

the prior volatility of stock returns (RET_VOL3t), current period losses (LOSSDt), the prior

volatility of earnings (STD_INCOMEt), incorporation in the state of Delaware (DELAWAREt),

the percentage of institutional holdings (%INST_HOLDt), company size (SIZEt), book-to-market

18

(BTMt), the number of foreign operating segments (FOREIGNt), and the number of business

segments (SEGMENTSt). I also include industry and year fixed effects to control for differences

in 10-K length and readability over time and across industries.

D. Information Asymmetry Tests

Next, I examine the relation between disclosure committee adoption and information

asymmetry. I use the following general model for these tests:

IA_VARit = γ0 + γ1 DCit + γ2 AFTERit + γ3 DCit x AFTERit + γ4 TURNOVERit-1 + γ5 MVEit-1

+ γ6 RET_VOLit-1 + γ7 GROSSFILESIZEit + γj YEARFE + γk FF_INDUSTRYFE

+ νit (3)

where:

IA_VARt = a measure of information asymmetry, defined as either

BA_SPREAD or ILLIQUIDITY;

BA_SPREADt = bid ask spread (following Corwin and Schultz (2012)), measured

as the average high-low estimator over the period from 9 months

before the fiscal year-end through 3 months after the fiscal year-

end;

ILLIQUIDITYt = the daily ratio of absolute stock returns scaled by the dollar

volume of trading (following Amihud (2002)), averaged over the

period starting 9 months before the fiscal year-end through 3

months after the fiscal year-end;

TURNOVERt-1 = the log of annual share turnover in year t-1 (CRSP: VOL);

MVEt-1 = the market value of equity at the end of year t-1;

RET_VOLt-1 = the standard deviation of daily stock returns over the prior fiscal

year (CRSP: RET);

GROSSFILESIZEt = the gross file size of the 10-K filing (from McDonald’s website);

νit = the error term; and


19

γ3 is the coefficient of interest. It represents the difference-in-differences in information

asymmetry, between adopter and control companies, from before to after disclosure committee

adoption.

My tests utilize two common measures of information asymmetry. The first measure is

the bid-ask spread. I use the high-low spread estimator from Corwin and Schultz (2012) because

it is easy to calculate and generally outperforms other bid-ask spread estimators based on the

frequency of zero returns.12 I calculate the average (mean) of the Corwin and Schultz (2012)

daily bid-ask spread measure from three months before the fiscal year-end through nine months

after the fiscal year-end to ensure that information in the 10-K filings is priced

(BA_SPREADt).13 Higher BA_SPREADt values represent greater of information asymmetry.

The second measure of information asymmetry is a measure of illiquidity

(ILLIQUIDITYt) from Amihud (2002). This measure captures an investor’s ability to trade in a

stock without moving its price (Daske et al. 2013). I calculate ILLIQUIDITYt as average daily

ratio of absolute stock return to the dollar value of trading volume from three months before the

fiscal year-end through nine months after the fiscal year-end to capture information in the 10-K

filing. Higher values of ILLIQUIDITYt represent higher stock illiquidity and lower market

depth.14

12 I follow Corwin and Schultz (2012) and estimate the bid-ask spread after backing out the

stock’s fundamental volatility. The high-low spread estimator is a reasonable proxy for

information asymmetry because daily high prices are usually buyer-initiated and daily low prices

are almost always seller initiated. Thus, the ratio of high-to-low prices on a given day represents

both the stock’s fundamental volatility and its bid-ask spread. Another benefit of using the

Corwin and Schultz (2012) estimator is that it avoids need to match individual buys and sells

with TAQ data, which computationally intensive and time consuming. 13 The code used to calculate the high-low spread estimator is available on Shane Corwin and

Paul Schultz’s website at http://www3.nd.edu/~scorwin/HILOW_Estimator_Sample_002.sas. 14 I also calculate ILLIQUIDITYt using the median instead of the mean and find my results

unchanged.

20

In my information asymmetry tests, I include controls for lagged company size

(MVEt-1), return volatility (RET_VOLt-1), and share turnover (TURNOVERt-1), consistent with

prior literature (Cheng et al. 2013; Daske et al. 2013). Because I am interested in how disclosure

committee adoption influences information asymmetry overall, I also control for the readability

of the 10-K filing (GROSSFILESIZEt) filed during the fiscal year, as well as industry and year

indicator variables.

E. Earnings Informativeness Tests

Next, I examine the impact of disclosure committee adoption on the informativeness of

earnings using the following model:

RETURNSit = δ0 + δ1 EARNINGSit-1 + δ2 EARNINGSit + δ3 DCit + δ4 AFTERit + δ5 DCit x AFTERit

+ δ6 DCit x EARNINGSit-1 + δ7 DCit x EARNINGSit

+ δ8 AFTERit x EARNINGSit-1 + δ9 AFTERit x EARNINGSit

+ δ10 DCit x AFTERit x EARNINGSit-1 + δ11 DCit x AFTERit x EARNINGSit

+ δj CONTROLS + δk (CONTROLS x EARNINGSit-1)

+ δl (CONTROLS x EARNINGSit) + δm (CONTROLS x DCit)

+ δn (CONTROLS x AFTERit) + δo (CONTROLS x DCit x AFTERit)

+ δp YEARFE + δq FF_INDUSTRYFE + ηit (4)

where:

RETURNSt = the buy-and-hold return for year t, measured from the beginning

of year t;

EARNINGSt = income available to common shareholders before extraordinary

items (Compustat: IB), scaled by the market value of common

equity (Compustat: PRCC_F x CSHO) at the beginning of year t;

CONTROLS = the standard deviation of earnings (STD_INCOMEt), the

proportion of shares owned by institutions (%INST_HOLDt),

analyst following (NUMBER_ANALYSTSt), asset growth

(ASSET_GROWTHt), leverage (LEVERAGEt), book-to-market

(BTMt), and market value of equity (MVEt);

ηit = the error term; and


21

I estimate Equation (4) using ordinary least squares (OLS) regression with robust standard errors

clustered by company (Petersen 2009).

The coefficient of interest, δ11, represents how disclosure committee adoption affects the

relation between current earnings and current returns. A positive coefficient estimate would

indicate that disclosure committee adoption increases the informativeness of current earnings.

Finally, I examine the impact of disclosure committee adoption on the relevance and

timeliness of reported earnings. Following prior literature (e.g., Lundholm and Myers (2002),

Ettredge et al. (2005), Tucker and Zarowin (2006), and Drake et al. (2014)), I use the following

model for these tests:

RETURNSit = λ0 + λ1 EARNINGSit-1 + λ2 EARNINGSit + λ3 EARNINGS3it+1 to t+3

+ λ4 RETURNS3it+1 to t+3 + λ5 DCit + λ6 AFTERit + λ7 DCit x AFTERit

+ λ8 DCit x EARNINGSit-1 + λ9 DCit x EARNINGSit

+ λ10 DCit x EARNINGS3it+1 to t+3 + λ11 DCit x RETURNS3it+1 to t+3

+ λ12 AFTERit x EARNINGSit-1 + λ12 AFTERit x EARNINGSit

+ λ14 AFTERit x EARNINGS3it+1 to t+3 + λ15 AFTERit x RETURNS3it+1 to t+3

+ λ16 DCit x AFTERit x EARNINGSit-1 + λ17 DCit x AFTERit x EARNINGSit

+ λ18 DCit x AFTERit x EARNINGS3it+1 to t+3

+ λ19 DCit x AFTERit x RETURNS3it+1 to t+3 + λj CONTROLS

+ λk (CONTROLS x EARNINGSit-1) + λl (CONTROLS x EARNINGSit)

+ λm (CONTROLS x EARNINGS3it+1 to it+3)

+ λn (CONTROLS x RETURNSS3it+1 to it+3) + λo (CONTROLS x DCit)

+ λp (CONTROLS x AFTERit) + λq (CONTROLS x DCit x AFTERit)

+ λr YEARFE + λs FF_INDUSTRYFE + εit (5)

where:

EARNINGS3t+1 to t+3 = the sum of income available to common shareholders before

extraordinary items (Compustat: IB) for the years t+1 through t+3,

scaled by the market value of equity (Compustat: PRCC_F x

CSHO) at the beginning of year t;

RETURNS3t+1 to t+3 = the buy-and-hold return for the fiscal years t+1 through t+3,

measured from the beginning of fiscal year t+1;

CONTROLS = the standard deviation of earnings (STD_INCOMEt), the

proportion of losses in the subsequent three years (LOSS3t), the

proportion of shares owned by institutions (%INST_HOLDt),

22

analyst following (NUMBER_ANALYSTSt), asset growth

(ASSET_GROWTHt), leverage (LEVERAGEt), book-to-market

(BTMt), and market value of equity (MVEt);

εit = the error term; and


I estimate Equation (5) using OLS regression with robust standard errors clustered by company

(Petersen 2009).

The coefficient of interest, λ18, represents how disclosure committee adoption influences

the FERC. A positive coefficient estimate would indicate that disclosure committee adoption

increases the amount of future earnings news reflected in current returns. The control variables

follow Lundholm and Myers (2002), Ettredge et al. (2005), and Tucker and Zarowin (2006) and

have been shown to influence the associations between current returns and current and future

earnings.

IV. Sample Selection and Data

My sample consists of a treatment group and a control group. The treatment group is

comprised of all companies that disclose the presence of a disclosure committee in EDGAR

filings and the control group is a propensity score matched sample of companies that do not

adopt a disclosure committee (as described in Section 3). I collect internal control weakness

data, auditor data, and financial statement misstatement data from Audit Analytics, financial data

from the Compustat Fundamental Annual database, returns data from CRSP, analyst data from

I/B/E/S, and institutional holdings data from Thomson Reuters. To minimize the impact of

extreme observations, I winsorize all continuous variables at 1 and 99 percent. Appendix B

provides details about the variable construction.

23

I hand collect disclosure committee adoptions from EDGAR filings (i.e., 10-K, 10-Q,

DEF 14A, DEFA 14A, PRE 14A, PREA 14A, 8-K, S-1, 40-F, 20-F, and 424B filings) from 2002

through 2012.15 I begin in 2002 because this is the year in which the SEC first recommended

their adoption. For my determinants, readability, and information asymmetry tests, my sample

period ends in 2012, but for my earnings informativeness tests, my sample period ends in 2009

because I require three future years of earnings and returns. I perform keyword searches of each

filing for evidence of the presence of a disclosure committee. I then read these filings and

identify companies that adopted a disclosure committee. I set the variable DC_ADOPTt equal to

1 if a disclosure committee is present for a given company-year observation, and 0 otherwise.16

This search yields a sample of 764 unique companies disclosing the presence of a disclosure

committee.17

Table 1 contains the details about my treatment and control samples. Panel A reveals that

many sample companies adopted a disclosure committee immediately after the passage of SOX.

Panel B presents the number of treatment and control observations for by Fama and French

15 I find that the presence of a disclosure committee is most often disclosed in 10-K, 10-Q, and

DEF 14A filings. I also perform searches of EDGAR filings for 1999 through 2001 to

investigate whether companies adopted disclosure committees before the passage of SOX but do

not find evidence of disclosure committee adoptions. 16 Examples of the naming convention for the disclosure committee include the following:

Disclosure and Controls Committee, Financial Reporting Committee, and Financial Reporting

and Disclosure Committee. I perform a keyword search using variations of these examples,

including identifying all observations with the words disclosure and committee within three

words of each other. 17 I also perform Google searches of the Standard and Poor’s (S&P) 1500 for evidence of

disclosure committees on company websites and I find that 390 of the S&P 1500 companies

disclose the presence of a disclosure committee. I find that 333 of these companies were

identified by my EDGAR searches so my EDGAR searches capture about 85 percent of

disclosure committee companies. In all analyses, I exclude the 57 companies identified in

Google searches but not in EDGAR searches as having a disclosure committee because I cannot

reliably determine the disclosure committee adoption date for these companies.

24

(1997) industry classification. My sample companies represent a number of industries, with

companies from the Business Services industry comprising the greatest proportion (at 15.7

percent of the sample) and companies from the Agriculture, Tobacco Products, Shipbuilding and

Railroad Equipment, Defense, Precious Metals, Coal, and Shipping Containers industries

representing the smallest proportion (at 0.10 percent).

[Insert Table 1 Here]

In Table 2, I present descriptive statistics for my treatment and control samples. I find

that, on average, treatment companies have less of their assets in accounts receivable and

inventory (AR_INt-1), are more likely to engage a Big N auditor (BIGNt-1), and have more

readable financial statements (GROSSFILESIZEt-1) in the year before disclosure committee

adoption. I also find that treatment companies are more likely than control companies to report a

material weakness in internal control (MWEAKNESS302t-1) in the year before adoption. Lastly, I

find that disclosure committee adopters have shorter operating cycles (OP_CYCLEt-1), higher

returns volatility (RET_VOLt-1), more segments (SEGMENTSt-1), and are larger (SIZEt-1), in the

year before adoption, relative to control companies.18


V. Results

A. Determinants Test and Propensity Score Matching

Table 3 presents the results of analyses investigating the determinants of disclosure

committee adoption. The area under the receiver-operating characteristic (ROC) curve is greater

than 0.70, suggesting that my model has acceptable discriminatory power (Hosmer and

18 I also investigate whether the proportion of losses differ between disclosure committee

adoption and control group companies in the pre- and post-adoption periods. I do not find

significant differences in losses between these groups in the pre- or post-adoption years.

25

Lemeshow 2002). The results suggest that companies with material weaknesses in internal

control over financial reporting (MWEAKNESS302t-1) in the prior year are more likely to adopt

disclosure committees. Additionally, companies with less readable 10-K filings

(GROSSFILESIZEt-1), larger companies (SIZEt-1), and companies engaging Big N auditors

(BIGNt-1) in the previous year are more likely to adopt disclosure committees.


Prior research suggests that the inclusion of fixed effects in nonlinear models creates an

incidental parameters problem and that the unconditional maximum likelihood estimator is

biased (Neyman and Scott 1948; Lancaster 2000), but studies using simulations find that this

bias becomes smaller as the group size increases and Katz (2001) suggests that this bias becomes

negligible when the group size exceeds 15. To investigate the robustness of my results given the

potential for an incidental parameters problem, in Table 4, I re-estimate Equation (1) using

industry-fixed effects based on one digit Standard Industry Classification (SIC) codes and find

that the coefficient signs and significance are unchanged.


Next, I use the propensity scores from the determinants model to match each treatment

company (i.e., disclosure committee adopter) to a control company that did not adopt a

disclosure committee.19 I match treatment companies to control companies without replacement

on a one-to-one basis with a caliper distance of 0.005,20 matching on year. As indicated in Panel

A of Table 5, I am able to match 732 of the 764 disclosure committee companies. In Panel B, I

19 I performed Google searches to ensure that each control company did not adopt a disclosure

committee during the sample period. 20 Rosenbaum and Rubin (1985) suggest that the appropriate caliper for matching is 25 percent

of the standard deviation of the propensity scores. The standard deviation of the propensity score

from my determinants model is 0.02 so I use a caliper of 0.005.

26

present univariate statistics for the sample of treatment and control companies used in my

determinants test. Columns (1) and (2) contain the means and standard deviations for my control

group and columns (3) and (4) contain the means and standard deviations for my treatment

group. In column (5), I present p-values for tests of differences in means. I do not find

statistically different mean values for any of the variables used in my determinants model,

suggesting that my match procedure successfully matches treatment companies with similar

control companies.


In column (6), I report normalized differences (i.e., the differences in means scaled by the

average of the two within-group standard deviations). Normalized differences are a more

reliable way to assess covariate balance because they are invariant to sample size (Imbens and

Wooldridge 2009; Abadie and Imbens 2011). Imbens and Wooldridge (2009) suggest that

normalized differences of greater than 0.250 in absolute value represent less than acceptable

covariate balance. I find that all normalized differences are below 0.250 in absolute value,

suggesting that the match between treatment and control companies provides acceptable

covariate balance.

Governance data in machine-readable form is unavailable for half of my sample

companies so include governance variables in my models would result in significant sample

attrition. As such, I do not include board and audit committee related variables in my models. I

investigate differences in governance characteristics for those observations that have available

governance data. In Panels A and B of Table 6, I find no difference in governance

characteristics (i.e., audit committee size, percentage of outsiders on the audit committee, and

27

percentage of outsiders on the board of directors) between treatment and control companies

either before or after disclosure committee adoption.


Table 7 describes the sample selection for my multivariate tests. For each test, I require

that the treatment and control companies have data available for the pre- and post-adoption

years. I lose 72 treatment companies (and their matching control companies) for my readability

tests, 117 treatment companies for my information asymmetry tests, and 363 treatment

companies for my earnings informativeness tests because of missing data for the treatment or

control companies.21


B. Readability Tests

The results of my readability tests appear in Tables 8, 9, and 10. In Table 8, I investigate

the relation between disclosure committee adoption and the length (#WORDSt) of the 10-K

filing. I find that the coefficient on DCt insignificant, revealing no difference in 10-K length

between treatment control companies in the pre-disclosure committee adoption period. I find

that the coefficient on DCt x AFTERt is positive and significant (p < 0.05), consistent with

treatment companies experiencing an increase in 10-K length relative to control companies

following the adoption of the disclosure committee. In addition, I find that the joint test of the

coefficients on DCt and DCt x AFTERt is positive and significant (p < 0.05), consistent with

treatment companies having longer 10-K filings than control companies in the post-adoption

period.

21 I lose 311 treatment companies for my earnings informativeness test because I require I/B/E/S

data to calculate analyst following and I require three years of future earnings and returns data.

28


Next, I examine the relation between disclosure committee adoption and the readability

of the 10-K filing. In Table 9, where GROSSFILESIZEt is the dependent variable, the coefficient

on DCt is insignificant, consistent with no difference in 10-K readability between treatment and

control companies in the pre-adoption period. I find, however, that the coefficient on DCt x

AFTERt is positive and significant (p < 0.05), consistent with disclosure committee adoption

decreasing the readability of the 10-K filing. I also find that the joint test of the coefficients on

DCt and DCt x AFTERt is positive and significant (p < 0.05), consistent with treatment

companies having less readable 10-K filings than control companies in the post-adoption period.


In Table 10, where NETFILESIZEt is the dependent variable, I find that the coefficient on

DC is insignificant, consistent with no difference in 10-K readability between treatment and

control companies in the pre-adoption period. I find a positive and significant relation (p < 0.05)

between DCt x AFTERt and NETFILESIZEt, consistent with the readability of the 10-K filing

decreasing after disclosure committee adoption. I also find that the joint test of the coefficients

on DCt and DCt x AFTERt is positive and significant, consistent with treatment companies

having less readable 10-K filings than control companies in the post-adoption period (p < 0.05).


Taken together, my tests reveal that disclosure committee adoption is associated with an

increase in the length and a decrease in the readability of 10-K filings.22

22 I also use Rosenbaum bounds (Rosenbaum 2002) to investigate how large a confounding

effect would need to be to influence the statistical significance of my results. For my #WORDS

test, I find that a confounding factor 1.3 times the size of the coefficient on DCt x AFTERt would

be needed to change my statistical inferences. For the GROSSFILESIZE and NETFILESIZE

29

C. Information Asymmetry Tests

Tables 11 and 12 present the results of my information asymmetry tests. If disclosure

committees decrease the level of information asymmetry, I expect that bid-ask spreads

(BA_SPREADt) and market illiquidity (ILLIQUIDITYt) would decline following disclosure

committee adoption. Alternatively, if the disclosure committee does not increase disclosure

quality and the amount of information available to investors, bid-ask spreads and market

illiquidity could be unchanged or could increase.

In Table 11, I find that the coefficient on DCt x AFTERt is insignificant. This suggests

that disclosure committee adoption does not change the level of information asymmetry as

captured by bid-ask spreads.


Similarly, in Table 12, I find no relation between disclosure committee adoption and the

level of illiquidity (ILLIQUIDITYt). Taken together, these results suggest that disclosure

committee adoption does not affect information asymmetry.23


D. Earnings Informativeness Tests

Next, I examine whether disclosure committee adoption impacts the informativeness of

earnings. If a disclosure committee improves disclosure quality related to current or future

earnings news, then I expect that the ERC and FERC will be larger post-adoption. The results of

tests, the effect would need to be 1.20 and 1.28 times the effects of DC x AFTER to change my

statistical inferences. 23 I also investigate whether disclosure committee adoption influences the relation between

information asymmetry and readability. In these tests, I include an interaction between DC,

AFTER, and GROSSFILESIZE. I find that the coefficient on DC x AFTER x GROSSFILESIZE

is insignificant, suggesting that disclosure committee adoption does not influence the relation

between information asymmetry and readability.

30

these tests are tabulated in Tables 13 and 14. In Table 13, consistent with prior research, I find a

negative relation between past earnings and current returns and a positive relation between

current earnings and current returns. However, the coefficient on DCt x AFTERt x EARNINGSt

is insignificant. This result suggests that disclosure committee adoption does not influence the

relation between current returns and current earnings. Next, I examine whether disclosure

committee adoption influences the extent to which future earnings are reflected in current

returns.


In Table 14, consistent with prior literature, I find negative relations between past

earnings and future returns (EARNINGSt-1 and RETURNS3t+1 to t+3) and current returns

(RETURNt), and positive relations between contemporaneous and future earnings (EARNINGSt

and EARNINGS3t+1 to t+3) and current returns (RETURNt). However, the coefficients on DCt x

AFTERt x EARNINGSt and DCt x AFTERt x EARNINGS3t+1 to t+3 are insignificant. These results

suggest that disclosure committee adoption does not change the informativeness of earnings or

the extent to which current returns reflect future earnings news.24


VI. Additional Analyses

A. Specific Word Types

I also examine the impact of disclosure committee adoption on the specific words in 10-K

filings. Specifically, I look at the number of positive, negative, and neutral words reported in a

24 My results are robust to using one year of future earnings and returns data, following Ettredge

et al. (2005).

31

company’s annual 10-K. I use data from Loughran and McDonald (2012) to calculate the

number of positive, negative, and neutral words in each company’s 10-K filing.

First, in Table 15, I examine how disclosure committee adoption impacts the number of

positive words in the 10-K filing. I find the coefficient on DCt x AFTERt positive and

significant, suggesting that disclosure committee adoption is associated with an increase in the

number of positive words in the 10-K filing.


Next, in Table 16, I examine how disclosure committee adoption impacts the number of

negative words in the 10-K filing. Similarly, I find the coefficient on DCt x AFTERt positive and


number of negative words in the 10-K filing.


Finally, in Table 17, I examine how disclosure committee adoption impacts the number

of neutral words in the 10-K filing. I find that the coefficient on DCt x AFTERt positive and


number of neutral words in the 10-K filing.


Taken together, my results suggest that disclosure committee adoption is

associated with an increase in words in general and not a specific word type. Next, I examine

whether disclosure committee adoption impacts the proportion of each word type (i.e., positive,

negative, and neutral) words among all of the words in the 10-K filing. In Tables 18, 19, and 20,

I find that disclosure committee adoption is not associated with a difference in the proportion of

each word type. Taken together, these results suggest that disclosure committee adoption is

32

associated with an increase in words in general and no change the overall tone or sentiment of

the 10-K filing.




B. Disclosure Committee Composition Tests

Over 30 percent of my sample companies disclose the composition of their disclosure

committee. I hand collect this data from SEC filings to create profiles of each disclosure

committee in my sample. The composition of the disclosure committee could influence the

committee’s operations and the quality of disclosure because the members of the committee

could influence the amount and quality of information disclosed by the company. On the one

hand, executives could provide additional information that helps the committee to improve

disclosure quality. On the other hand, the inclusion of executives on the disclosure committee

could influence the committee to reduce disclosure quality if there is an incentive to obfuscate

information.

In Table 21, I include descriptive statistics for the 246 disclosure committee adoptions

that disclosed the composition of the disclosure committee. The descriptive statistics indicate

that a senior manager sits on the disclosure committee of 90.2 percent of sample companies. The

CEO sits on the disclosure committee of 23.2 percent and the CFO sits on the disclosure

committee of 28.9 percent of my sample companies. The descriptive statistics also indicate that

Chief Operating Officers (COO) sit on 21.1 percent of disclosure committees, General Counsels

sit on 21.1 percent, accounting officers (e.g., Controllers, Chief Accounting Officers) sit on 21.5

33

percent, internal audit personal on 8.5 percent, audit committee members on 2 percent, and

directors on 6.5 percent.


In Table 22, I examine whether the composition of the disclosure committee influences

the number of words in the 10-K filing. I find a positive relation between MANAGER_ON_DC

and #WORDS. However, in Table 23, I find no relation between any of the disclosure committee

composition variables and GROSSFILESIZE. In Table 24, I find a positive and significant

relation between MANAGER_ON_DC and NETFILESIZE. Taken together, these results suggest

that the inclusion of a member of senior management on the disclosure committee results in

longer and more complex 10-K filings.




I also examine whether the composition of the disclosure committee influences the tone

or sentiment of the 10-K filing. In Table 25, I examine the relation between the percentage of

positive words included in the 10-K filing and the composition of the disclosure committee. I

find a negative and significant relation between the presence of the COO on the disclosure

committee and the number of positive words, suggesting that disclosure committees that include

COOs reduce the proportion of positive words included in the 10-K.


In Table 26, I examine the relation between the percentage of negative words in the 10-K

filing and the composition of the disclosure committee. I find that the presence of an audit

committee member on the disclosure committee (AC_ON_DC) is positively associated with the

34

number of negative words in the 10-K filing. This result suggests that the inclusion of an audit

committee member on the disclosure committee leads to an increase in the number of negative

words used in the 10-K filing.


Next, I examine whether the composition of the disclosure committee impacts

information asymmetry in the year of disclosure committee adoption. In Table 27, I find a

negative and significant relation between the presence of the Chief Risk Officer (CRO) on the

disclosure committee and BA_SPREAD. However, in Table 28, I do not find a relation between

any of the variables of interest and ILLIQUIDITY. These results do not provide robust evidence

that the composition of the disclosure committee influences information asymmetry.



Finally, I examine whether the composition of the disclosure committee influences ERCs

and FERCs. In Table 29, I find an increase in ERCs when internal audit personnel sit on the

disclosure committee.


In Table 30, I present the results of my FERC tests. I find that the presence of the CFO,

COO, and audit committee members on the disclosure committee increase FERCs. However, I

find that the presence of directors on the disclosure committee decrease FERCs. Taken together,

these results suggest that the extent to which current returns reflect future earnings news is

greater when CFOs, COOs, and audit committee members sit on the disclosure committee.


35

C. Internal Control over Financial Reporting

The SEC states that the disclosure committee should also be involved in developing and

evaluating internal control over financial reporting. Since the adoption of a disclosure committee

could be a signal that the company has committed to high quality disclosure and financial

reporting, I next examine whether disclosure committee adoption influences the reporting of

404b material weaknesses in internal control. First, I examine the relation between disclosure

committee adoption and internal control weaknesses in the year of adoption. Next, I examine the

relation between disclosure committee adoption and internal control weaknesses in the year after

adoption. The results are presented in Tables 31 and 32.

In Table 31, I find a positive and significant relation between disclosure committee

adoption and the reporting of internal control weaknesses. This result suggests that disclosure

committees improve control quality in the year of adoption by helping the company to identify

and report internal control problems.


Next, I examine the relation between disclosure committee adoption and the reporting of

internal control weaknesses in year after adoption. The dependent variable and control variables

in this regression are all measured at time t+1 relative to the disclosure committee adoption. In

Table 32, I find a negative and significant coefficient on DC x AFTER. This result suggests that

the adoption of a disclosure committee is negatively related to the reporting of future internal

control weaknesses and provides some evidence that the adoption of a disclosure committee

improves internal controls over financial reporting.


36

Taken together, these results suggest that the adoption of a disclosure committee is a

signal of an improvement in both current and future reporting.

VII. Robustness Tests

A. Timing of Disclosure Committee Adoption

I collect evidence of disclosure committee adoptions from EDGAR filings. As such,

some of the EDGAR filings do not specifically indicate the period in which the disclosure

committee has been adopted. To alleviate concerns that I have assigned the disclosure

committee adoption year to the wrong period, I exclude 214 disclosure committee adoptions that

do not explicitly state the year of adoption and re-examine my main analyses.

In Table 33, 34, and 35, I use a limited sample of 1,784 observations for my readability

tests. In these tables, consistent with my main results, I find positive and significant relations

between disclosure committee adoption and #WORDS, GROSSFILESIZE, and NETFILESIZE.




Next, in Tables 36 and 37, I examine the robustness of my information asymmetry tests

after excluding observations where the disclosure committee adoption date is unclear. Using a

sample of 1,604 observations, I continue to find disclosure committee adoption insignificantly

related to BA_SPREAD and ILLIQUIDITY.



In my earnings informativeness tests, I use a sample of 1,256 observations. In Table 38, I

continue to find that disclosure committee adoption does not influence the relation between

37

current returns and current earnings (ERC). In Table 39, I also do not find that disclosure

committee adoption does not influence the extent to which future earnings news is reflected in

current returns.



B. Measurement Window of Bid-Ask Spreads and Market Illiquidity

I also calculate the average of the bid-ask spread and illiquidity over the fiscal year. In

Table 40, I examine the relation between disclosure committee adoption and bid-ask spreads

using this alternative measurement window. In this table, consistent with my main results, I

continue to find the coefficient on DC x AFTER insignificant.


Next, in Table 41, I investigate the relation between disclosure committee adoption and

market illiquidity measured over the fiscal year. In this table, consistent with my main results, I

continue to find the coefficient on DC x AFTER insignificant.


Taken together, these results suggest the measurement window of my variables of interest

do not impact my main results and suggest that the adoption of a disclosure committee is not

associated with a change information asymmetry.

C. Alternative Measure of Information Asymmetry

Next, I examine the relation between disclosure committee adoption and an alternative

measure of information asymmetry. I follow Krishnaswami et al. (1999) and use residual

volatility as an alternative measure of information asymmetry. Residual volatility (RESID_VOL)

is calculated as the variance of market-model residuals and represents the uncertainty of

38

company-specific information that remains after removing uncertainty that is common to the

market.

The results of this test are shown in Table 42. In this table, I do not find a significant

relation between disclosure committee adoption and residual volatility. This result suggests that

my information asymmetry results are robust to an alternative definition of information

asymmetry.


VIII. Conclusion

In this paper, I examine the determinants and consequences of disclosure committee

adoption. I find that companies with less readable 10-K filings and internal control problems in

the year before adoption are more likely to adopt disclosure committees. Using a propensity

score matched sample of control companies that do not adopt a disclosure committee, I find that

disclosure committee adoption is associated with an increase in the length and a decrease in the

readability of 10-K filings. I also find that disclosure committee adoption does not affect either

bid-ask spreads or the level of market illiquidity, suggesting that disclosure committee adoption

does not reduce information asymmetry. Lastly, I find that the relation between current period

returns and current and future earnings is unchanged following disclosure committee adoption,

suggesting that disclosure committee adoption does not influence the informativeness of earnings

or the extent to which future earnings news is impounded into stock prices. Taken together,

these results suggest that disclosure committee adoption influences the length and readability of

the 10-K filing, but does not change disclosure quality.

I also perform many additional tests to examine the consequences of disclosure

committee adoption. First, I examine the relation between disclosure committee adoption and

39

specific word types used in the 10-K filing. I find that the adoption of a disclosure committee is

associated with an increase in positive, negative, and neutral words. I also do not find that

disclosure committee adoption influences the ratio positive-to total words or the ratio of

negative-to-total words in the 10-K filing. This suggests that disclosure committee adoption

doesn’t significantly change the tone or sentiment of the 10-K filing.

Next, I examine whether disclosure committee composition influences the readability of

the 10-K filing, information asymmetry, and the informativeness of earnings. Using a subsample

of companies that disclose the composition of their disclosure committee, I find some evidence

that the inclusion of senior management on the disclosure committee is associated with increased

10-K length, but I do not find evidence that the inclusion of senior management on the disclosure

committee influences information asymmetry or the informativeness of earnings.

Lastly, I examine whether disclosure committee adoption influences the quality of

internal controls. I find that disclosure committee adoption is associated with an increase in

internal control weaknesses in the year of adoption, but a decrease in internal control weaknesses

in the year after adoption. These results suggest that disclosure committee adoption is a signal of

an improvement in current and future internal controls.

To my knowledge, this study is the first to examine the impact of voluntarily adopted

disclosure committees. These results should be of interest to academic researchers, investors,

regulators, and others interested in the effect of voluntary governance mechanisms on disclosure

quality because the additional effort expended by managers and other disclosure committee

participants does not appear to improve disclosure quality. Future research can investigate how

disclosure committees impact other financial reporting outcomes. For example, future research

40

can investigate the impact of disclosure committees on financial reporting quality, voluntary

disclosure, or on information intermediaries such as analysts.

41

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46

X. Appendix A: Sample Disclosure Committee Charter

Roles and Responsibilities from Cardinal, Inc.’s Disclosure Committee Charter

48

XI. Appendix B: Variable Definitions

Variable Name Description

#NEGATIVE_WORDS The number of negative words in the 10-K filing, taken from Bill

McDonald’s website; 25

#NEUTRAL_WORDS The number of neutral words in the 10-K filing, calculated as the

total words (#WORDS) minus positive words

(#POSITIVE_WORDS) and negative words

(#NEGATIVE_WORDS);

#POSITIVE_WORDS The number of positive words in the 10-K filing, taken from Bill

McDonald’s website;25

#WORDS The number of words in the 10-K filing, taken from Bill McDonald's

website; 25

%INST_HOLD The percentage of institutional holdings (Thomson Reuters

Institutional Holdings);

%NEGATIVE_WORDS The number of negative words (#NEGATIVE_WORDS) divided by

total words (#WORDS);

%NEUTRAL_WORDS The number of neutral words (#NEUTRAL_WORDS) divided by


%POSITIVE_WORDS The number of positive words (#POSITIVE_WORDS) divided by


AC_ON_DC An indicator variable set to one if at least one audit committee

member sits on the disclosure committee, zero otherwise;

ACCTOFF_ON_DC An indicator variable set to one if the company discloses the

presence of an accounting officer (e.g., Controller, Chief Accounting

Officer) on the disclosure committee, zero otherwise;

AFTER An indicator variable set to one for the post-adoption period, zero

otherwise;

AGE Company age, calculated as the number of years to date during

which the company reports total assets on Compustat (Compustat:

AT) greater than zero;

AR_IN The proportion of accounts receivable (Compustat: RECT) and

inventory (Compustat: INVT) to total assets (Compustat: AT);

ASSET_GROWTH The growth in assets (Compustat: AT) from t-1 to t;

AUDITOR_TENURE The length of the company-auditor relationship to date;

BA_SPREAD Bid ask spread (following Corwin and Schultz (2012)), measured as

the average high-low estimator over the period from 9 months before

the fiscal year-end through 3 months after the fiscal year-end;

BIGN An indicator variable set to one if the company is audited by a Big N

auditor, zero otherwise;

BTM Book-to-market, calculated as the book value of common equity

(Compustat: CEQ) divided by the market value of equity

(Compustat: CHSO x PRCC_F);

25 The url is http://www3.nd.edu/~mcdonald/Word_Lists.html.

49

CEO_ON_DC An indicator variable set to one if the company discloses the

presence of the CEO on the disclosure committee, zero otherwise;

CFO_ON_DC An indicator variable set to one if the company discloses the

presence of the CFO on the disclosure committee, zero otherwise;

COO_ON_DC An indicator variable set to one if the company discloses the

presence of the COO on the disclosure committee, zero otherwise;

DC An indicator variable set to one for companies that adopted a

disclosure committee, zero otherwise;

DC_ADOPT An indicator variable set to one if the company adopted a disclosure

committee during the year, zero otherwise;

DELAWARE An indicator variable set to one if the company is incorporated in the

state of Delaware, zero otherwise;

DIRECTOR_ON_DC An indicator variable set to one if the company discloses the

presence of at least one director on the disclosure committee, zero

otherwise;

EARNINGS Income available to common shareholders before extraordinary

items (Compustat: IB) scaled by the lagged market value of equity

(Compustat: PRCC_F x CSHO);

EARNINGS3 The sum of income available to common shareholders before

extraordinary items (Compustat: IB) for years t+1 through t+3

scaled by the lagged market value of equity (Compustat: PRCC_F x

CSHO);

FINANCING An indicator variable set to one if M&A is not equal to one and if the

number of shares outstanding (Compustat: CHSO) increased by at

least 10 percent during the year, or if M&A is not equal to one and if

long-term debt increased by at least 20 percent during the year, zero

otherwise;

FOREIGN The natural log of the number of foreign segments (Compustat

Segment File);

GC_ON_DC An indicator set to one if the company discloses the presence of the

General Counsel on the disclosure committee, zero otherwise;

GROSSFILESIZE The natural log of the gross file size of the company’s 10-K filing,

obtained from Bill McDonald’s website;29

IA_ON_DC An indicator variable set to one if the company discloses the

presence of an internal auditor on the disclosure committee, zero

otherwise;

ILLIQUIDITY The daily ratio of absolute stock returns scaled by the dollar volume

of trading (following Amihud (2002)), averaged over the period

starting 9 months before the fiscal year-end through 3 months after

the fiscal year-end;

FF_INDUSTRYFE Industry indicator variables defined using Fama and French (1997)

industry classifications;

LEVERAGE Debt-to-assets ratio (Compustat: DLTT / AT);

LOSS3 An indicator variable set to one if EARNINGS3 is negative, zero

otherwise;

50

LOSSD An indicator variable set to one if the company reports a loss during

the year (Compustat: NI), zero otherwise;

M&A An indicator variable set to one if the company engaged in a merger

or acquisition during the year, and zero otherwise (Compustat:

SALE_FN);

RES_ANNOUNCE An indicator variable set to one if the company announced a

restatement in year t-1 due to misstatements in prior year financial

statements, zero otherwise (Audit Analytics);

MANAGER_ON_DC An indicator variable set to one if the company discloses the

presence of a member of senior management on the disclosure

committee, zero otherwise;

MVE Market value of equity (Compustat: PRCC_F x CSHO);

MWEAKNESS An indicator variable set to one if the company disclosed a material

in internal control over financial reporting under SOX 404b, zero

otherwise;

MWEAKNESS302 An indicator variable set to one if the company disclosed a material

weakness in internal control over financial reporting under SOX

section 302 in year t-1, zero otherwise;

NETFILESIZE The net 10-K file size, representing the size of the 10-K filing with

just textual content, obtained from Bill McDonald’s website;29

NUMBER_ANALYSTS The number of analysts issuing an earnings forecast for the fiscal

year;

OP_CYCLE The company’s operating cycle, calculated as the number of days

sales in accounts receivable plus the number of days sales in

inventory;

RESTATEMENT An indicator variable set to one if the company announced the

restatement of the financial statements during the fiscal year, zero

otherwise;

RESID_VOL Residual volatility, calculated as the variance of company-specific

market model residuals, estimated over the 36 months ending at the

fiscal year-end;

RET_VOL The standard deviation of daily stock returns for the fiscal year

(CRSP: RET);

RET_VOL3 The standard deviation of daily stock returns for the prior three years

(CRSP: RET);

RETURNS The buy-and-hold return for year t, measured from the beginning of

year t;

RETURNS3 The buy-and-hold return for fiscal years t+1 through t+3, measured

from the beginning of fiscal year t+1;

ROA Return on assets, measured as income before extraordinary items

(Compustat: IB) scaled by total assets (Compustat: AT);

SALE_GROWTH The growth in sales (SALE) from year t-1 to year t.

SEGMENTS The natural log of the number of business segments (Compustat

Segment File);

SIC1_INDUSTRYFE Industry indicator variables defined using one digit SIC codes;

51

SIZE The natural log of total assets (Compustat: AT);

SPECIAL_ITEMS Special items (Compustat: SPI) scaled by lagged total assets

(Compustat: AT);

STD_CFO The standard deviation of cash flows for the prior three years;

STD_INCOME The standard deviation of earnings (Compustat: IB) for the prior

three years;

STD_SALE The standard deviation of sales for the prior three years;

TURNOVER The log of annual share turnover (CRSP: VOL);

YEARFE Fiscal year indicator variables.

52

Table 1 - Disclosure Committee Sample Summary

Panel A: Disclosure Committee and Control Observations by Year

Fiscal

Year

DC_ADOPT

Observations

Control

Observations

2002 135 3,255

2003 163 3,044

2004 132 2,925

2005 59 2,742

2006 56 3,056

2007 50 2,992

2008 34 3,013

2009 33 3,114

2010 42 3,023

2011 35 2,933

2012 25 2,873

Total 764 32,970

53

Panel B: Disclosure Committee and Control Observations by Fama and French (1997)

Industry

Industry Description

DC_ADOPT

Observations

Control

Observations

1 Agriculture 1 84

2 Food Products 9 500

3 Candy & Soda 2 81

4 Beer & Liquor 3 73

5 Tobacco Products 1 45

6 Recreation 3 253

7 Entertainment 10 402

8 Printing and Publishing 7 200

9 Consumer Goods 8 432

10 Apparel 8 418

11 Healthcare 11 607

12 Medical Equipment 20 1,126

13 Pharmaceutical Products 31 1,936

14 Chemicals 20 529

15 Rubber and Plastic Products 7 211

16 Textiles 3 105

17 Construction Materials 12 540

18 Construction 8 363

19 Steel Works Etc. 3 435

21 Machinery 26 1,005

22 Electrical Equipment 9 551

23 Miscellaneous 3 153

24 Automobiles and Trucks 14 393

25 Aircraft 2 168

26 Shipbuilding and Railroad Equipment 1 84

27 Defense 1 72

28 Precious Metals 1 51

29 Non-Metallic and Industrial Metal Mining 2 100

30 Coal 1 90

31 Petroleum and Natural Gas 23 1,280

32 Utilities 34 1,026

33 Communication 21 862

34 Personal Services 9 352

35 Business Services 136 3,531

36 Computers 36 1,205

37 Electronic Equipment 53 2,137

38 Measuring and Control Equipment 13 774

39 Business Supplies 7 335

40 Shipping Containers 1 97

41 Transportation 11 884

42 Wholesale 26 1,061

43 Retail 41 1,567

54

44 Restaurants, Hotels, Motels 14 528

45 Banking 38 2,908

46 Insurance 28 1,222

47 Real Estate 5 204

48 Trading 41 1,990

Total 764 32,970

55

Table 2 - Descriptive Statistics for Determinants Model

DC_ADOPTt = 0

DC_ADOPTt = 1

(N = 32,970) (N = 764)

(1) Mean (2) Std Dev (3) Mean (4) Std Dev (5) P-value

RES_ANNOUNCEt-1 0.111 0.612 0.123 0.668 0.599

AGEt-1 20.503 14.310 20.517 15.150 0.979

AR_INt-1 0.282 0.225 0.254 0.210 0.001 ***

AUDITOR_TENUREt-1 8.998 7.776 8.730 7.370 0.346

BIGNt-1 0.749 0.434 0.881 0.324 0.000 ***

BTMt-1 0.651 0.953 0.604 0.914 0.179

FINANCINGt-1 0.266 0.442 0.276 0.447 0.524

FOREIGNt-1 1.267 2.107 1.387 2.010 0.119

GROSSFILESIZEt-1 14.252 1.088 14.121 1.015 0.001 ***

INST_HOLDINGt-1 0.400 0.352 0.399 0.350 0.956

LEVERAGEt-1 0.174 0.206 0.185 0.198 0.166

M&At-1 0.147 0.354 0.166 0.373 0.146

MWEAKNESS302t-1 0.050 0.219 0.071 0.256 0.012 ***

OP_CYCLEt-1 477.328 1141.036 330.085 842.632 0.000 ***

RET_VOLt-1 0.157 0.104 0.170 0.112 0.000 ***

ROAt-1 -0.040 0.410 -0.054 0.312 0.348

SEGMENTSt-1 1.861 1.599 2.103 1.701 0.000 ***

SIZEt-1 6.155 2.016 6.592 2.078 0.000 ***

SPECIAL_ITEMSt-1 -0.022 0.159 -0.026 0.148 0.410

STD_CFOt-1 0.074 0.116 0.072 0.107 0.697

STD_SALEt-1 0.157 0.193 0.160 0.210 0.605

Notes: This table presents comparisons of the means for companies that adopted disclosure

committees (DC_ADOPTt = 1) and the population of companies with available data that did not

adopt disclosure committees (DC_ADOPTt = 0). The p-value is from a test of the difference in

means between the two samples.

56

Table 3 - Determinants Model with Fama and French (1997) Industry Classification

Dependent Variable DC_ADOPTt

Coef P-value

INTERCEPT -10.081 0.000 ***

RES_ANNOUNCEt-1 -0.163 0.378

MWEAKNESS302t-1 0.862 0.000 ***

GROSSFILESIZEt-1 0.230 0.000 ***

SIZEt-1 0.181 0.000 ***

BTMt-1 -0.037 0.189

M&At-1 -0.037 0.739

FINANCINGt-1 0.091 0.309

SEGMENTSt-1 0.024 0.360

FOREIGNt-1 -0.016 0.476

RET_VOLt-1 0.336 0.424

STD_SALEt-1 -0.036 0.888

STD_CFOt-1 -0.080 0.851

AR_INt-1 -0.068 0.814

SP_ITEMSt-1 0.361 0.194

OP_CYCLEt-1 0.028 0.601

ROAt-1 -0.140 0.138

LEVERAGEt-1 -0.242 0.262

AGEt-1 -0.002 0.596

%INST_HLDt-1 -0.042 0.754

BIGNt-1 0.265 0.056 *

AUDITOR_TENUREt-1 -0.009 0.138

FF_INDUSTRYFE Included

YEARFE Included

Number of Observations 33,734

Pseudo R2 0.071

Area under the ROC Curve 0.726

Notes: I use logit to estimate this regression and cluster standard errors by company (Petersen

2009). ***, **, and * indicate p < 0.01, p <0.05, and p < 0.10, respectively. Significance is

based on two-tailed tests. All variables are defined in Appendix B.

57

Table 4 - Determinants Model with One Digit SIC Codes

Dependent Variable DC_ADOPTt

Coef P-value

INTERCEPT -10.050 0.000 ***

RES_ANNOUNCEt-1 -0.154 0.401

MWEAKNESS302t-1 0.843 0.000 ***

GROSSFILESIZEt-1 0.239 0.000 ***

SIZEt-1 0.169 0.000 ***

BTMt-1 -0.036 0.173

M&At-1 -0.029 0.792

FINANCINGt-1 0.084 0.348

SEGMENTSt-1 0.029 0.253

FOREIGNt-1 -0.006 0.772

RET_VOLt-1 0.355 0.383

STD_SALEt-1 -0.019 0.940

STD_CFOt-1 -0.093 0.823

AR_INt-1 -0.093 0.731

SP_ITEMSt-1 0.304 0.241

OP_CYCLEt-1 0.031 0.519

ROAt-1 -0.134 0.155

LEVERAGEt-1 -0.242 0.232

AGEt-1 -0.001 0.702

%INST_HLDt-1 -0.041 0.755

BIGNt-1 0.262 0.055 *

AUDITOR_TENUREt-1 -0.010 0.101

SIC1_INDUSTRYFE Included

YEARFE Included


Pseudo R2 0.065

Area under the ROC Curve 0.718




58

Table 5 - Propensity Score Matching

Panel A: Sample Selection

Treatment companies for the propensity score matching model 764

Less: Treatment companies missing a matching control company 32

Final sample of treatment companies for difference-in-differences tests 732

Final sample of control companies for difference-in-differences tests 732

Full sample 1,464

Panel B: Covariate Balance

DC_ADOPTt = 0

DC_ADOPTt = 1

(N = 732) (N = 732)

(1)

Mean

(2)

Std Dev

(3)

Mean

(4)

Std Dev

(5)

P-

value

(6)

Norm

Diff

ROAt-1 -0.048 0.408 -0.056 0.317 0.665 -0.016

RET_VOLt-1 0.172 0.120 0.171 0.113 0.906 -0.004

GROSSFILESIZEt-1 14.041 0.939 14.053 0.944 0.804 0.009

BTMt-1 0.574 1.406 0.599 0.928 0.692 0.015

SPECIAL_ITEMSt-1 -0.024 0.282 -0.027 0.151 0.809 -0.009

%INST_HOLDt-1 0.405 0.350 0.394 0.349 0.562 -0.021

M&At-1 0.165 0.372 0.169 0.375 0.834 0.008

FINANCINGt-1 0.268 0.443 0.272 0.445 0.860 0.007

SEGMENTSt-1 2.087 1.676 2.113 1.698 0.769 0.011

FOREIGNt-1 1.318 1.908 1.380 2.018 0.549 0.022

BIGNt-1 0.902 0.298 0.880 0.325 0.180 -0.050

AUDITOR_TENUREt-1 8.329 7.112 8.578 7.230 0.507 0.025

STD_SALEt-1 0.163 0.201 0.163 0.213 0.964 0.002

STD_CFOt-1 0.074 0.102 0.073 0.108 0.981 -0.001

SIZEt-1 6.509 2.025 6.562 2.064 0.625 0.018

LEVERAGEt-1 0.188 0.210 0.186 0.199 0.841 -0.007

AGEt-1 19.462 14.619 20.227 14.881 0.321 0.037

AR_INt-1 0.248 0.208 0.254 0.210 0.586 0.020

OP_CYCLEt-1 300.289 795.281 319.432 817.695 0.650 0.017

RES_ANNOUNCEt-1 0.055 0.227 0.045 0.208 0.401 -0.031

MWEAKNESS302t-1 0.068 0.252 0.068 0.252 1.000 0.000

Notes: This table presents comparisons of the means for the treatment companies (DC_ADOPT

= 1) and the control companies (DC_ADOPT = 0). The p-value is from a test of difference in

means between the two samples. The normalized difference is calculated following Wooldridge

(2011).

59

Table 6 - Differences in Governance Controls

Panel A: Before Adoption

DC_ADOPTt = 0

DC_ADOPTt = 1

(N = 243) (N = 263)

(1)

Mean

(2)

Std Dev

(3)

Mean

(4)

Std Dev

(5)

P-value

AUDIT_SIZE 3.724 0.064 3.699 0.068 0.794

%INDEPENDENT_BD 0.687 0.010 0.686 0.010 0.949

%INDEPENDENT_AC 0.924 0.009 0.938 0.008 0.298




Panel B: After Adoption

DC_ADOPTt = 0

DC_ADOPTt = 1

(N = 242) (N = 256)

(1)

Mean

(2)

Std Dev

(3)

Mean

(4)

Std Dev

(5)

P-value

AUDIT_SIZE 3.796 0.065 3.816 0.066 0.840

%INDEPENDENT_BD 0.714 0.009 0.716 0.009 0.887

%INDEPENDENT_AC 0.951 0.008 0.945 0.008 0.549




60

Table 7 - Difference in Differences Sample Selection

Panel A: Readability Tests

Matched treatment (disclosure committee) companies for difference-in-difference tests 732

Less: Treatment companies missing data for readability tests 72

Matched treatment companies for readability difference-in-difference tests 660

Plus: Pre-disclosure committee adoption years for treatment companies 660

Plus: Pre- and post-disclosure committee adoption years for matched control

companies 1,320

Final sample for readability difference-in-differences tests 2,640

Panel B: Information Asymmetry Tests

Matched treatment companies for difference-in-difference tests 732

Less: Treatment companies missing data for information asymmetry tests 117

Matched treatment companies for information asymmetry difference-in-difference tests 615



companies 1,230

Final sample for readability difference-in-differences tests 2,460

Panel C: Earnings Informativeness Tests

Matched treatment companies for difference-in-difference tests 732

Less: treatment companies missing data for earnings informativeness tests 363

Matched treatment companies for earnings informativeness difference-in-difference

tests 369



companies 738

Final sample for earnings informativeness difference-in-differences tests 1,476

61

Table 8 - Number of Words Test

Dependent Variable #WORDSt

Coef P-value

INTERCEPT 10.290 0.000 ***

DCt -0.003 0.915

AFTERt -0.034 0.125

DCt x AFTERt 0.062 0.041 **

SPECIAL_ITEMSt -0.066 0.140

M&At 0.032 0.247

FINANCINGt 0.057 0.015 **

RET_VOL3t 0.973 0.000 ***

LOSSDt 0.172 0.000 ***

STD_INCOMEt 0.096 0.020 **

DELAWARE 0.038 0.125

%INST_HOLDt 0.028 0.424

SIZEt 0.150 0.000 ***

BTMt 0.021 0.139

FOREIGNt -0.006 0.769

SEGMENTSt 0.039 0.046 **


YEARFE Included


Adjusted R2 0.339

Joint Test: DCt + DCt x AFTERt **

Notes: I estimate this model using ordinary least squares regression and cluster standard errors

by company (Petersen 2009). ***, **, and * indicate p < 0.01, p <0.05, and p < 0.10,

respectively. Significance is based on two-tailed tests. All variables are defined in Appendix B.

62

Table 9 - Gross File Size Test

Dependent Variable GROSSFILESIZEt

Coef P-value

INTERCEPT 15.010 0.000 ***

DCt -0.004 0.911

AFTERt -0.074 0.007 ***

DCt x AFTERt 0.084 0.021 **


M&At 0.020 0.611

FINANCINGt 0.057 0.089 *

RET_VOL3t 1.047 0.000 ***

LOSSDt 0.112 0.002 ***

STD_INCOMEt 0.122 0.119


%INST_HOLDt 0.068 0.210

SIZEt 0.189 0.000 ***

BTMt -0.006 0.737

FOREIGNt -0.055 0.080 *

SEGMENTSt 0.033 0.230


YEARFE Included


Adjusted R2 0.499





63

Table 10 - Net File Size Test

Dependent Variable NETFILESIZEt

Coef P-value

INTERCEPT 12.565 0.000 ***

DCt -0.010 0.706

AFTERt -0.037 0.087 *

DCt x AFTERt 0.070 0.016 **


M&At 0.026 0.326


RET_VOL3t 0.901 0.000 ***

LOSSDt 0.174 0.000 ***

STD_INCOMEt 0.098 0.017 **


%INST_HOLDt 0.021 0.538

SIZEt 0.153 0.000 ***

BTMt 0.020 0.144

FOREIGNt -0.004 0.831

SEGMENTSt 0.046 0.014 **


YEARFE Included


Adjusted R2 0.379





64

Table 11 - Bid Ask Spread Test

Dependent Variable BA_SPREADt

Coef P-value

INTERCEPT 0.023 0.035 **

DCt 0.001 0.416

AFTER t 0.002 0.002 ***

DCt x AFTERt 0.000 0.732

TURNOVERt-1 0.000 0.953

MVEt-1 -0.003 0.000 ***

RETURN_VOLt-1 0.006 0.000 ***

GROSSFILESIZEt-1 0.001 0.021 **


YEARFE Included


Adjusted R2 0.205




65

Table 12 - Market Illiquidity Test

Dependent Variable ILLIQUIDITYt

Coef P-value

INTERCEPT 1.837 0.018 **

DCt -0.027 0.714

AFTER t 0.139 0.029 **

DCt x AFTERt -0.051 0.509

TURNOVERt-1 -0.258 0.000 ***

MVEt-1 -0.254 0.000 ***

RETURN_VOLt-1 0.019 0.816

GROSSFILESIZEt-1 0.048 0.112


YEARFE Included


Adjusted R2 0.187




66

Table 13 - ERC Test

Dependent Variable RETURNSt

Coef P-value

INTERCEPT 0.537 0.000 *

EARNINGSt-1 -0.606 0.085 *

EARNINGSt 0.502 0.062 *

DCt -0.012 0.729

AFTERt -0.028 0.468


DCt x EARNINGSt-1 0.078 0.859

DCt x EARNINGSt 0.001 0.997

AFTERt x EARNINGSt-1 -0.250 0.657

AFTERt x EARNINGSt 0.744 0.046 **

DCt x AFTERt x EARNINGSt-1 -0.031 0.965

DCt x AFTERt x EARNINGSt -0.407 0.400


YEARFE Included

CONTROLS Included


Adjusted R2 0.412


by company (Petersen 2009). For parsimony, I do not tabulate the control variable main effects

and interactions. ***, **, and * indicate p < 0.01, p <0.05, and p < 0.10, respectively.

Significance is based on two-tailed tests. All variables are defined in Appendix B.

67

Table 14 - FERC Test


Coef P-value

INTERCEPT 0.528 0.000 ***

EARNINGSt-1 -0.502 0.060 *

EARNINGSt 0.459 0.075 *

EARNINGS3t+1 to t+3 0.267 0.099 *

RETURNS3t+1 to t+3 -0.096 0.022 **

DCt 0.007 0.868

AFTERt -0.014 0.758


DCt x EARNINGSt-1 -0.031 0.942


DCt x EARNINGS3t+1 to t+3 -0.023 0.923

DCt x RETURNS3t+1 to t+3 -0.020 0.662

AFTERt x EARNINGSt-1 -0.385 0.473

AFTERt x EARNINGSt 0.941 0.005 ***

AFTERt x EARNINGS3t+1 to t+3 -0.069 0.764

AFTERt x RETURNS3t+1 to t+3 -0.006 0.897

DCt x AFTERt x EARNINGSt-1 0.249 0.715

DCt x AFTERt x EARNINGSt -0.715 0.142

DCt x AFTERt x EARNINGS3t+1 to t+3 0.134 0.640

DCt x AFTERt x RETURNS3t+1 to t+3 0.009 0.883


YEARFE Included

CONTROLS Included


Adjusted R2 0.463





68

Table 15 - Number of Positive Words Test

Dependent Variable #POSITIVE_WORDSt

Coef P-value

INTERCEPT 5.455 0.000 ***

DCt -0.009 0.747

AFTERt -0.028 0.188

DCt x AFTERt 0.054 0.058 *


M&At 0.025 0.373


RET_VOL3t 1.177 0.000 ***

LOSSDt 0.169 0.000 ***

STD_INCOMEt 0.095 0.023 **


%INST_HOLDt 0.075 0.039 **

SIZEt 0.153 0.000 ***

BTMt 0.022 0.113

FOREIGNt 0.010 0.650



YEARFE Included


Adjusted R2 0.359





69

Table 16 - Number of Negative Words Test

Dependent Variable #NEGATIVE_WORDSt

Coef P-value

INTERCEPT 5.947 0.000 ***

DCt 0.022 0.522

AFTERt -0.026 0.328

DCt x AFTERt 0.069 0.052 *


M&At 0.009 0.784


RET_VOL3t 1.339 0.000 ***

LOSSDt 0.290 0.000 ***

STD_INCOMEt 0.224 0.000 ***

DELAWARE 0.052 0.091 *

%INST_HOLDt 0.059 0.177

SIZEt 0.167 0.000 ***

BTMt 0.020 0.190

FOREIGNt -0.006 0.817



YEARFE Included


Adjusted R2 0.321

Joint Test: DCt + DCt x AFTERt *




70

Table 17 - Number of Neutral Words Test

Dependent Variable #NEUTRAL_WORDSt

Coef P-value

INTERCEPT 10.268 0.000 ***

DCt -0.003 0.908

AFTERt -0.035 0.123

DCt x AFTERt 0.062 0.042 **


M&At 0.033 0.239


RET_VOL3t 0.967 0.000 ***

LOSSDt 0.170 0.000 ***

STD_INCOMEt 0.094 0.022 **


%INST_HOLDt 0.028 0.433

SIZEt 0.149 0.000 ***

BTMt 0.021 0.139

FOREIGNt -0.006 0.767

SEGMENTSt 0.039 0.044 **


YEARFE Included


Adjusted R2 0.321

Joint Test: DCt + DCt x AFTERt *




71

Table 18 - Percentage of Positive Words Test

Dependent Variable %POSITIVE_WORDSt

Coef P-value

INTERCEPT 0.008 0.000 ***

DCt -0.000 0.534

AFTERt 0.000 0.398


SPECIAL_ITEMSt 0.000 0.293

M&At -0.000 0.523

FINANCINGt -0.000 0.396

RET_VOL3t 0.001 0.011 **

LOSSDt -0.000 0.913

STD_INCOMEt -0.000 0.955

DELAWARE -0.000 0.324

%INST_HOLDt 0.000 0.008 ***

SIZEt 0.000 0.377

BTMt -0.000 0.967


SEGMENTSt -0.000 0.017 **


YEARFE Included


Adjusted R2 0.211

Joint Test: DCt + DCt x AFTERt




72

Table 19 - Percentage of Negative Words Test

Dependent Variable %NEGATIVE_WORDSt

Coef P-value

INTERCEPT 0.013 0.000 ***

DCt 0.000 0.115

AFTERt 0.000 0.412



M&At -0.000 0.034 **


RET_VOL3t 0.005 0.000 ***

LOSSDt 0.002 0.000 ***

STD_INCOMEt 0.002 0.000 ***


%INST_HOLDt 0.000 0.368

SIZEt 0.000 0.000 **

BTMt -0.000 0.670

FOREIGNt -0.000 0.803

SEGMENTSt -0.000 0.696


YEARFE Included


Adjusted R2 0.298





73

Table 20 - Percentage of Neutral Words Test

Dependent Variable %NEUTRAL_WORDSt

Coef P-value

INTERCEPT 0.979 0.000 ***

DCt -0.000 0.214

AFTERt -0.000 0.259



M&At 0.000 0.026 **

FINANCINGt 0.000 0.641

RET_VOL3t -0.006 0.000 ***

LOSSDt -0.002 0.000 ***

STD_INCOMEt -0.002 0.000 ***

DELAWARE -0.000 0.378

%INST_HOLDt -0.001 0.062 *

SIZEt -0.000 0.000 ***

BTMt 0.000 0.655

FOREIGNt -0.000 0.764



YEARFE Included


Adjusted R2 0.327





74

Table 21 - Disclosure Committee Composition Descriptive Statistics

(N = 246) (1) Mean (2) Std (3) Min (4) Q1 (5) Median (6) Q3 (7) Max

MANAGER_ON_DC 0.902 0.297 0.000 1.000 1.000 1.000 1.000

CEO_ON_DC 0.232 0.423 0.000 0.000 0.000 0.000 1.000

CFO_ON_DC 0.289 0.454 0.000 0.000 0.000 1.000 1.000

COO_ON_DC 0.069 0.254 0.000 0.000 0.000 0.000 1.000

GC_ON_DC 0.211 0.409 0.000 0.000 0.000 0.000 1.000

ACCTOFF_ON_DC 0.215 0.412 0.000 0.000 0.000 0.000 1.000

IA_ON_DC 0.085 0.280 0.000 0.000 0.000 0.000 1.000

AC_ON_DC 0.020 0.141 0.000 0.000 0.000 0.000 1.000

DIRECTOR_ON_DC 0.065 0.247 0.000 0.000 0.000 0.000 1.000

75

Table 22 - Disclosure Committee Composition Number of Words Test


Coef P-value

INTERCEPT 9.639 0.000 ***

MANAGER_ON_DCt 0.317 0.031 **

CEO_ON_DCt -0.017 0.891

CFO_ON_DCt -0.120 0.356

COO_ON_DCt -0.026 0.837

CRO_ON_DCt -0.090 0.666

GC_ON_DCt 0.035 0.751

ACCTOF_ON_DCt -0.026 0.810

IA_ON_DCt -0.059 0.697

DIRECTOR_ON_DCt 0.047 0.790

AC_ON_DCt 0.149 0.388


M&At -0.079 0.516


RET_VOL3t 0.355 0.498

LOSSDt 0.205 0.067 *



%INST_HOLDt -0.185 0.349

SIZEt 0.179 0.000 ***

BTMt -0.017 0.207




YEARFE Included

Number of Observations 227

Adjusted R2 0.340




76

Table 23 - Disclosure Committee Composition Gross File Size Test


Coef P-value

INTERCEPT 14.298 0.000 ***

MANAGER_ON_DCt -0.138 0.587

CEO_ON_DCt -0.120 0.497

CFO_ON_DCt -0.056 0.753

COO_ON_DCt -0.082 0.711

CRO_ON_DCt 0.177 0.676

GC_ON_DCt -0.099 0.588


IA_ON_DCt -0.011 0.965


AC_ON_DCt 0.175 0.631


M&At 0.097 0.594


RET_VOL3t 0.555 0.451

LOSSDt 0.240 0.099 *

STD_INCOMEt 0.342 0.022 **

DELAWAREt -0.024 0.854

%INST_HOLDt 0.065 0.792

SIZEt 0.260 0.000 ***

BTMt 0.041 0.027 **

FOREIGNt -0.005 0.968



YEARFE Included


Adjusted R2 0.498




77

Table 24 - Disclosure Committee Composition Net File Size Test


Coef P-value

INTERCEPT 12.200 0.000 ***

MANAGER_ON_DCt 0.323 0.029 **

CEO_ON_DCt -0.003 0.981

CFO_ON_DCt -0.115 0.364

COO_ON_DCt -0.000 0.997

CRO_ON_DCt -0.049 0.815

GC_ON_DCt 0.011 0.920


IA_ON_DCt -0.041 0.782


AC_ON_DCt 0.147 0.392


M&At -0.097 0.417


RET_VOL3t 0.270 0.596

LOSSDt 0.198 0.070 *



%INST_HOLDt -0.161 0.404

SIZEt 0.177 0.000 ***

BTMt -0.015 0.250




YEARFE Included


Adjusted R2 0.340




78

Table 25 - Disclosure Committee Composition Percentage of Positive Words Test

Dependent Variable %POSITIVE_WORDSt

Coef P-value

INTERCEPT 0.007 0.000 ***

MANAGER_ON_DCt 0.000 0.931

CEO_ON_DCt -0.000 0.798

CFO_ON_DCt -0.000 0.407

COO_ON_DCt -0.001 0.082 *

CRO_ON_DCt -0.000 0.632

GC_ON_DCt 0.000 0.271


IA_ON_DCt 0.000 0.457

DIRECTOR_ON_DCt -0.000 0.699

AC_ON_DCt -0.000 0.417


M&At -0.000 0.546


RET_VOL3t 0.000 0.916

LOSSDt -0.000 0.660

STD_INCOMEt -0.000 0.672

DELAWARE -0.000 0.666

%INST_HOLDt 0.000 0.395

SIZEt -0.000 0.981

BTMt 0.000 0.390

FOREIGNt -0.000 0.946



YEARFE Included


Adjusted R2 0.106




79

Table 26 - Disclosure Committee Composition Percentage of Negative Words Test

Dependent Variable %NEGATIVE_WORDSt

Coef P-value

INTERCEPT 0.006 0.049 **

MANAGER_ON_DCt 0.001 0.531

CEO_ON_DCt 0.000 0.827

CFO_ON_DCt -0.001 0.261

COO_ON_DCt 0.001 0.429

CRO_ON_DCt -0.001 0.352

GC_ON_DCt 0.001 0.158

ACCTOF_ON_DCt 0.000 0.729

IA_ON_DCt -0.000 0.730


AC_ON_DCt 0.004 0.013 **


M&At -0.000 0.848


RET_VOL3t 0.012 0.018 **

LOSSDt 0.001 0.522



%INST_HOLDt -0.000 0.744

SIZEt 0.000 0.209

BTMt 0.000 0.299




YEARFE Included


Adjusted R2 0.225




80

Table 27 - Disclosure Committee Composition Bid-Ask Spread Test


Coef P-value

INTERCEPT 0.032 0.433


CEO_ON_DCt -0.006 0.419

CFO_ON_DCt 0.005 0.489

COO_ON_DCt 0.006 0.667

CRO_ON_DCt -0.015 0.042 **

GC_ON_DCt -0.002 0.833

ACCTOF_ON_DCt 0.003 0.778

IA_ON_DCt 0.004 0.548


AC_ON_DCt 0.022 0.119

TURNOVERt-1 0.002 0.383

MVEt-1 -0.003 0.079 *

RETURN_VOLt-1 -0.000 0.987



YEARFE Included


Adjusted R2 0.094




81

Table 28 - Disclosure Committee Composition Illiquidity Test


Coef P-value



CEO_ON_DCt 0.088 0.790

CFO_ON_DCt 0.165 0.535

COO_ON_DCt 0.414 0.170

CRO_ON_DCt 0.213 0.610

GC_ON_DCt -0.276 0.290


IA_ON_DCt 0.316 0.326


AC_ON_DCt 1.471 0.295

TURNOVERt-1 -0.589 0.005 ***

MVEt-1 -0.163 0.132

RETURN_VOLt-1 0.187 0.481



YEARFE Included


Adjusted R2 0.164




82

Table 29 - Disclosure Committee Composition ERC Test


Coef P-value

INTERCEPT 0.769 0.028 **

EARNINGSt-1 -0.840 0.006 ***

EARNINGSt -0.159 0.705

CEO_ON_DCt -0.051 0.572

CEO_ON_DCt x EARNINGSt 0.473 0.590

CFO_ON_DCt 0.011 0.909

CFO_ON_DCt x EARNINGSt 0.343 0.680

COO_ON_DCt 0.129 0.299

COO_ON_DCt x EARNINGSt -0.034 0.974

GC_ON_DCt -0.031 0.702

GC_ON_DCt x EARNINGSt -0.677 0.400

CRO_ON_DCt -0.038 0.755

CRO_ON_DCt x EARNINGSt -0.177 0.893

ACCTOFF_ON_DCt 0.093 0.325

ACCTOFF_ON_DCt x EARNINGSt -0.567 0.646

IA_ON_DCt -0.128 0.173

IA_ON_DCt x EARNINGSt 3.930 0.003 ***


DIRECTOR_ON_DCt x EARNINGSt -0.363 0.477

AC_ON_DCt -0.124 0.358

AC_ON_DCt x EARNINGSt 2.472 0.000 ***

FE_INDUSTRYFE Included

YEARFE Included

CONTROLS Included


Adjusted R2 0.575




83

Table 30 - Disclosure Committee Composition FERC Test


Coef P-value

INTERCEPT 2.021 0.000 ***

EARNINGSt-1 -0.585 0.044 **

EARNINGSt -0.442 0.330

EARNINGS3t+1 to t+3 0.177 0.276

RETURNS3t+1 to t+3 0.015 0.650

CEO_ON_DCt 0.025 0.769

CEO_ON_DCt x EARNINGSt 1.329 0.009 ***

CEO_ON_DCt x EARNINGSt+1 to t+3 -0.195 0.296

CFO_ON_DCt -0.096 0.260

CFO_ON_DCt x EARNINGSt -0.685 0.172

CFO_ON_DCt x EARNINGSt+1 to t+3 0.479 0.006 ***

COO_ON_DCt 0.068 0.599

COO_ON_DCt x EARNINGSt -0.196 0.802

COO_ON_DCt x EARNINGSt+1 to t+3 0.704 0.057 *

GC_ON_DCt -0.034 0.703

GC_ON_DCt x EARNINGSt -0.678 0.414

GC_ON_DCt x EARNINGSt+1 to t+3 -0.297 0.387

CRO_ON_DCt -0.206 0.353

CRO_ON_DCt x EARNINGSt -2.958 0.200

CRO_ON_DCt x EARNINGSt+1 to t+3 1.924 0.158

ACCTOFF_ON_DCt 0.089 0.401

ACCTOFF_ON_DCt x EARNINGSt 0.587 0.692

ACCTOFF_ON_DCt x EARNINGSt+1 to t+3 -0.141 0.704

IA_ON_DCt -0.189 0.047 **

IA_ON_DCt x EARNINGSt 1.754 0.237

IA_ON_DCt x EARNINGSt+1 to t+3 0.192 0.540


DIRECTOR_ON_DCt x EARNINGSt 0.112 0.859

DIRECTOR_ON_DCt x EARNINGSt+1 to t+3 -0.548 0.087 *

AC_ON_DCt -0.272 0.017 **

AC_ON_DCt x EARNINGSt 0.869 0.181

AC_ON_DCt x EARNINGSt+1 to t+3 0.734 0.093 *

FE_INDUSTRYFE Included

YEARFE Included

CONTROLS Included


Adjusted R2 0.608





84

Table 31 – Disclosure Committee Adoption and Current Period Material Weaknesses

Dependent variable MWEAKNESSt

Coef P-value

INTERCEPT -5.286 0.000 ***

DCt 0.126 0.671

AFTERt -0.925 0.001 ***

DCt x AFTERt 1.160 0.002 ***

RESTATEMENTt 1.345 0.000 ***


M&At 0.181 0.503


RET_VOLt 0.633 0.589

LOSSDt 0.906 0.000 ***


SIZEt -0.087 0.173

BTMt 0.090 0.541


SEGMENTSt 0.542 0.013 **

SALE_GROWTHt 0.096 0.727


YEARFE Included

N 1,216

Pseudo R2 0.200

Area under ROC 0.809




85

Table 32 – Disclosure Committee Adoption and Future Material Weaknesses

Dependent Variable MWEAKNESSt+1

Coef P-value

INTERCEPT -3.533 0.008 ***

DCt 1.230 0.000 ***

AFTERt -0.224 0.363

DCt x AFTERt -0.599 0.062 *

RESTATEMENTt+1 1.094 0.000 ***

SPECIAL_ITEMSt+1 -1.800 0.052 *

M&At+1 0.218 0.415

FINANCINGt+1 -0.010 0.963

RET_VOLt+1 1.352 0.288

LOSSDt+1 0.529 0.022 **

STD_INCOMEt+1 0.644 0.180

SIZEt+1 -0.027 0.686

BTMt+1 0.136 0.174

FOREIGNt+1 0.233 0.168

SEGMENTSt+1 0.229 0.273

SALE_GROWTHt+1 -0.321 0.090 *


YEARFE Included

N 1,612

Pseudo R2 0.150

Area under ROC 0.787




86

Table 33 - Number of Words Test Excluding Unclear Adoption Timing Observations


Coef P-value

INTERCEPT 10.130 0.000 ***

DCt -0.017 0.645

AFTERt -0.043 0.122

DCt x AFTERt 0.096 0.011 **


M&At 0.030 0.390


RET_VOL3t 1.114 0.000 ***

LOSSDt 0.159 0.000 ***

STD_INCOMEt 0.035 0.415 **


%INST_HOLDt 0.068 0.135

SIZEt 0.148 0.000 ***

BTMt 0.029 0.096

FOREIGNt -0.022 0.406

SEGMENTSt 0.064 0.010 **


YEARFE Included


Adjusted R2 0.482





87

Table 34 - Gross File Size Test Excluding Unclear Adoption Timing Observations


Coef P-value

INTERCEPT 14.766 0.000 ***

DCt -0.072 0.128

AFTERt -0.086 0.013 **

DCt x AFTERt 0.136 0.003 ***

SPECIAL_ITEMSt -0.131 0.045 **

M&At 0.006 0.893


RET_VOL3t 0.983 0.000 ***

LOSSDt 0.149 0.001 ***



%INST_HOLDt 0.094 0.152

SIZEt 0.175 0.000 ***

BTMt -0.026 0.266

FOREIGNt -0.009 0.824

SEGMENTSt 0.082 0.017 **


YEARFE Included


Adjusted R2 0.482





88

Table 35 - Net File Size Test Excluding Unclear Adoption Timing Observations


Coef P-value

INTERCEPT 12.538 0.000 ***

DCt -0.023 0.497

AFTERt -0.047 0.082 *

DCt x AFTERt 0.104 0.004 ***


M&At 0.024 0.470


RET_VOL3t 1.045 0.000 ***

LOSSDt 0.163 0.000 ***


DELAWARE 0.063 0.043 **

%INST_HOLDt 0.062 0.163

SIZEt 0.151 0.000 ***

BTMt 0.029 0.092 *

FOREIGNt -0.017 0.506

SEGMENTSt 0.071 0.003 ***


YEARFE Included


Adjusted R2 0.363





89

Table 36 - Bid Ask Spread Test Excluding Unclear Adoption Timing Observations


Coef P-value

INTERCEPT -0.001 0.957

DCt 0.002 0.100

AFTER t 0.003 0.000 ***


TURNOVERt-1 0.000 0.852

MVEt-1 -0.003 0.000 ***

RETURN_VOLt-1 0.006 0.000 ***

GROSSFILESIZEt-1 0.001 0.085 *


YEARFE Included


Adjusted R2 0.214




90

Table 37 - Market Illiquidity Test Excluding Unclear Adoption Timing Observations


Coef P-value


DCt -0.031 0.739

AFTER t 0.224 0.005 ***


TURNOVERt-1 -0.235 0.000 ***

MVEt-1 -0.291 0.000 ***

RETURN_VOLt-1 -0.042 0.705



YEARFE Included


Adjusted R2 0.191




91

Table 38 - ERC Test Excluding Unclear Adoption Timing Observations


Coef P-value


EARNINGSt-1 -0.547 0.054 *

EARNINGSt 0.830 0.000 ***

DCt 0.018 0.618

AFTERt 0.018 0.684




AFTERt x EARNINGSt-1 0.006 0.990

AFTERt x EARNINGSt -0.264 0.562


DCt x AFTERt x EARNINGSt 0.386 0.527


YEARFE Included

CONTROLS Included


Adjusted R2 0.403





92

Table 39 - FERC Test Excluding Unclear Adoption Timing Observations


Coef P-value


EARNINGSt-1 -0.611 0.036 **

EARNINGSt 0.759 0.000 ***

EARNINGS3t+1 to t+3 0.337 0.035 **

RETURNS3t+1 to t+3 -0.201 0.000 ***

DCt -0.037 0.396

AFTERt -0.025 0.599



DCt x EARNINGSt -0.072 0.832

DCt x EARNINGS3t+1 to t+3 0.025 0.909

DCt x RETURNS3t+1 to t+3 0.102 0.015 **

AFTERt x EARNINGSt-1 0.003 0.995

AFTERt x EARNINGSt -0.523 0.253

AFTERt x EARNINGS3t+1 to t+3 0.139 0.572

AFTERt x RETURNS3t+1 to t+3 0.061 0.164


DCt x AFTERt x EARNINGSt 0.519 0.388

DCt x AFTERt x EARNINGS3t+1 to t+3 0.064 0.870

DCt x AFTERt x RETURNS3t+1 to t+3 -0.094 0.189


YEARFE Included

CONTROLS Included


Adjusted R2 0.463





93

Table 40 - Bid Ask Spread Test Measuring BA_SPREAD over the fiscal year


Coef P-value

INTERCEPT 0.029 0.004 ***

DCt 0.002 0.155

AFTER t 0.002 0.006 ***


TURNOVERt-1 -0.002 0.011 **

MVEt-1 -0.002 0.000 ***

RETURN_VOLt-1 0.010 0.000 ***

GROSSFILESIZEt-1 0.001 0.019 **


YEARFE Included


Adjusted R2 0.214




94

Table 41 - Market Illiquidity Test Measuring ILLIQUIDITY over the fiscal year


Coef P-value

INTERCEPT 2.010 0.018 **

DCt -0.084 0.289

AFTER t 0.095 0.169


TURNOVERt-1 -0.417 0.000 ***

MVEt-1 -0.183 0.000 ***

RETURN_VOLt-1 0.249 0.004 ***



YEARFE Included


Adjusted R2 0.183




95

Table 42 – Residual Volatility Test

Dependent Variable RESID_VOLt

Coef P-value

INTERCEPT 0.100 0.000 ***

DCt 0.001 0.968

AFTER t 0.001 0.921


TURNOVERt-1 0.007 0.000 ***

MVEt-1 -0.006 0.000 ***

RETURN_VOLt-1 0.023 0.000 ***



YEARFE Included


Adjusted R2 0.310


