Board Characteristics and Disclosure Tone

Minna Martikainena

Antti Miihkinenb

Luke Watsonc

Version: August 2016

Authors’ contact information:

aHanken School of Economics,

(Department of Accounting, Helsinki, Finland)

minna.martikainen@hanken.fi

bUniversity of Florida (Fisher School of Accounting)/ Aalto University School of Business

(Department of Accounting, Helsinki, Finland)

antti.miihkinen@warrington.ufl.edu

cUniversity of Florida (Fisher School of Accounting)

luke.watson@warrington.ufl.edu

Acknowledgements:

We are grateful for helpful comments from participants at the 2016 AAA Annual Meeting and the 23rd

Annual Conference of the Multinational Finance Society.

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Board characteristics and disclosure tone

Abstract

We examine the role of corporate boards of directors in shaping disclosure tone. Boards of

directors play an important governance role (Beasley, 1996), leading us to expect boards of

directors to influence financial reporting narratives. Specifically, we investigate whether the tone

of firms’ narrative disclosures provided in annual 10-K reports is associated with the age, gender

uniformity, human capital, and turnover of its board of directors. Analyzing a large sample of SEC

registrants from 2003 to 2014, the results indicate that directors’ age is negatively associated with

negative, positive and uncertain disclosure tone, but positively associated with litigious tone. These

results are consistent with older directors being risk averse, contributing to cautious reporting.

Meanwhile, directors’ gender uniformity and human capital are positively associated with all four

types of tone: negative, positive, uncertain, and litigious, indicative of richer narrative disclosures.

Board turnover is positively associated with negative and litigious tone yet negatively associated

with positive and uncertain tone, suggesting that new directors bring their own disclosure styles

that fade over time. Overall, our study helps decode the “black box” of disclosure tone which

Loughran and McDonald (2011) show has important economic implications.

Keywords: Board of directors, annual report, 10-K, tone, narrative disclosure

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Board characteristics and disclosure tone

1. Introduction

Disclosures help firms provide relevant and precise information to market participants.

Narrative disclosures such as those found in annual 10-K reports allow firms to place quantitative

disclosures in context and provide additional information. Although earnings and other news

releases occur prior to the 10-K, the additional context offered by the annual report is valuable to

market participants. Specifically, Loughran and McDonald (2011) show that the tone of annual

reports is associated with firms’ future financial performance, volatility, fraud, and material

weaknesses, suggesting that there is important information in annual reports incremental to that

found in earnings announcements. Since annual report tone has economic consequences and is a

product of individuals' writing and editing, how individuals' characteristics contribute to tone is a

natural and important question that we investigate in this paper.

Large sample studies on disclosure tone focus primarily on the economic consequences of

tone and/or style in financial reports, suggesting that tone and/or style matters and provides

additional information in addition to quantitative information (e.g. Tetlock, 2007; Cecchini et al.

2011; Loughran and McDonald, 2011; Yang, 2012). In addition, while neoclassical economic

theory contends that individuals are interchangeable rational economic agents, behavioral finance

research has shown that individuals’ characteristics matter. This strand of literature tends to focus

on the influence of top executives, consistent with upper echelons theory (Hambrick and Mason,

1984; Hambrick, 2007) in which executive characteristics affect firm-level decisions. In

accounting, this literature has used a related argument known as "tone at the top" to suggest that

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managers adopt unique disclosure styles (Bamber et al., 2010; Ge et al., 2011) and also that

managers’ disclosure choices interrelate with investor sentiment (Brown et al., 2012).

Previous literature shows that boards of directors play a governance role with respect to

financial reporting (Beasley, 1996; Beasley et al., 2000), with various board characteristics

affecting the quality of reported earnings (Xie et al., 2003; Klein 2002; Peasnell et al., 2005). Our

paper builds upon this literature by investigating the role of board members in narrative

disclosures. While typically corporate executives, general counsel, and controllers play a large role

writing of annual reports, some of these individuals (especially CEOs) serve as inside directors on

the firm’s board; thus, inside directors likely have some direct involvement in the actual writing

of the 10-K.1 Likewise, outside directors play an advisory and gatekeeping role that includes

reviewing multiple drafts of annual reports, making comments and suggesting revisions thereon.

In particular, the audit committee is charged with overseeing the financial reporting process which

includes preparation of annual reports. Beyond any of these direct roles, the board’s role in the

selection of the firm’s chief financial officer and general counsel, for example, conveys an indirect

effect of the board on the firm’s annual report.

We expect certain attributes of the board to affect the tone of 10-K reports. First, we expect

that older directors’ risk aversion will produce less rich disclosures with more litigious tone as

board members reduce the informativeness of disclosures while also including language that helps

mitigate litigation risk. Second, we expect that the gender uniformity of the board will produce

richer tone as like-minded individuals disagree less and act with fewer checks on their behavior.

Third, we expect that directors’ human capital will produce richer, more informative content as

more competent, valuable directors write more strongly. Fourth, we expect that recent board

1 Inside (outside) directors are also known as executive (non-executive) directors.

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turnover will increase the richness of disclosure as new members bring their own disclosure styles

and reconsider boilerplate text.

We proxy for board characteristics using directors’ average age (capturing risk aversion),

male-to-female ratio (uniformity), education and chief financial officer experience (human

capital), and attrition rate (turnover). We compute these characteristics at three levels within the

board of directors: (i) inside director(s); (ii) outside directors not on the audit committee; and (iii)

outside directors on the audit committee. We examine four specific aspects of tone (negative,

positive, uncertain, and litigious) using the dictionaries developed by Loughran and McDonald

(2011). The target sample consists of SEC registrants between 2003 and 2014. We retrieve board

characteristics from BoardEx and control variables from Compustat, yielding a main sample of

22,748 firm-year observations. We estimate OLS regression analyses of disclosure tone on board

member characteristics and control variables to inform our inferences.

The results show that directors’ experience and risk aversion, as measured by average age,

is associated with disclosure tone. Board age is negatively associated with negative, positive, and

uncertain disclosure tone, with these results being strongest for inside directors. Outside director

age is positively associated with litigious disclosure tone. These results highlight the cautiousness

that comes with experience and risk aversion. That is, older board members make fewer positive

or negative assertions, refrain from uncertain language, and are more likely to discuss the legal

environment.2,3 These results support our prediction that more risk averse, experienced boards

produce disclosures containing less rich language that may not be useful to investors.

2 The dictionary for litigiousness reflects propensity for legal contest. 3 Disclosure tone is ostensibly the end result of management intentions, board member requirements, and the efforts

of the internal investor communication department and/or external investor relations communications agencies. In this

paper we implicitly assume that board members are advisors to management and gatekeepers of information that is

provided to investors.

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Next, we show that uniformity, as measured by the board’s male-to-female-ratio, is

associated with disclosure tone. Our results show that inside director uniformity is positively

associated with uncertain tone. We find that outside director uniformity is positively associated

with negative, positive, and uncertain tone. The associations with negative and uncertain tone are

likewise observed in the audit committee. Our results combine to suggest that director uniformity

leads to richer language in annual reports, consistent with similar viewpoints across the board.

Our next set of tests indicates that board members’ human capital is related to disclosure

tone. Specifically, board members’ average education is positively associated with negative,

positive, uncertain, and litigious tone, suggesting that highly educated board members have greater

ability and/or willingness to provide rich information. We find some evidence of incremental

effects of directors’ experience in chief financial officer (CFO) roles in that non-audit committee

members with CFO experience help produce incrementally more negative and uncertain tone.

Next we examine board turnover using the extent of yearly changes in the members of the

board. The results provide evidence that attrition is positively associated with negative and

litigious tone across all three director groups. We also detect a negative association between inside

director attrition and positive tone. Finally, we find a negative association between director

turnover and uncertain tone. Taken together, the evidence is consistent with our expectation that

new board members bring new disclosure styles to the board. These findings may also be at least

partly attributed to financial distress because board turnover is likely higher in distressed firms,

and financial distress is also likely to increase (decrease) the use of negative (positive) language.

In addition to controlling for financial performance in our main test, we examine this possibility

further by conditioning our sample on the sign of earnings in supplemental analyses. Among loss

firms we find that inside director attrition remains positively and significantly associated with

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negative tone; meanwhile, among profitable firms, a similar association manifests for both inside

and outside directors. These results are consistent with board turnover adding richness to

disclosures regardless of firm performance. We further subject our results to a slate of

supplemental analyses, including Impact Threshold for a Confounding Variable analysis, and find

that our results appear robust. Nonetheless, we emphasize that our findings are associations and

not necessarily causal relations.

Our study contributes to the extant literature by identifying an important role the board of

directors plays in advising and monitoring the firm. We show that disclosure tone is significantly

associated with board member characteristics, which is meaningful given prior research that

reports on the economic consequences of disclosure tone and/or style (e.g., Tetlock, 2007;

Loughran and McDonald, 2011; Yang, 2012). Given this research concluding that tone matters,

we help uncover the drivers of tone. This study also contributes to literature on the role of

individual managers in influencing firms’ disclosure choices and style (Bertrand and Schoar, 2003;

Bamber et al., 2010; Ge et al., 2011), complementing and extending it by examining the role played

by the board. We show that the managerial traits of inside directors that affect disclosure can

manifest incrementally in outside directors both on and off the audit committee. Our results suggest

that future researchers should consider influence of outside directors on firm disclosure and

choices, as we show that their influence is incremental to the more commonly investigated

influence of top executives. Finally, our study relates to the line of research documenting the

effects of board characteristics on firm outcomes in general (e.g., Ahern and Dittmar 2012). These

studies inform corporate boards, shareholders, search agencies, and other parties looking to elect

and retain directors who will benefit the firm across multiple dimensions. We identify specific

director traits that are associated with various aspects of disclosure tone. To the extent that

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stakeholders find these aspects of disclosure tone desirable or undesirable, they should select or

avoid these director traits.

The remainder of the paper is structured as follows. Section 2 reviews prior literature and

develops the hypotheses. Section 3 explains the methodology and variable definitions. Section 4

discusses the sample and descriptive statistics. We discuss the results or our empirical tests in

Section 5 and conclude the paper in Section 6.

2. Literature Review and Hypothesis Development

2.1. Corporate disclosure choices and disclosure tone

Early disclosure literature predicts that information problems in capital markets are non-

existent (Grossman, 1981; Milgrom, 1981) because under the unraveling result theorem, firms are

motivated to disclose all relevant information. These disclosure models generally assume that

disclosures are costless and investors know that a firm has information. Verrecchia (1983) and

Dye (1985) discard such assumptions in pursuit of a theoretical foundation for research on

corporate disclosure. The growing body of empirical research in the area has provided evidence of

a wide array of determinants of disclosure choices (Beyer et al., 2010). Firm size is a common

determinant of disclosure choices (e.g., Brammer and Pavelin, 2006; Lang and Lundholm, 1993;

Miihkinen, 2012). Other documented drivers for disclosure choices include profitability (e.g.,

Prencipe, 2004), external financing needs (e.g. Lang and Lundholm, 1993), and risk characteristics

such as bankruptcy risk, business risk and systematic risk (Jorgensen and Kirschenheiter, 2003;

Linsley and Shrives, 2006; Dobler et al., 2011; Miihkinen, 2012). Recent evidence also identifies

a limited set of corporate governance factors that influence disclosure. For example, Xie et al.

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(2003) conclude that board and audit committee characteristics may constrain managers’

propensity to manage earnings. Gul and Leung (2004) suggest that CEO duality is related to lower

levels of voluntary corporate disclosures, although this association is moderated by the expertise

of the outside directors.

Certain economic consequences of disclosure tone are clearly identified in prior research.

Antweiler and Frank (2004) find that stock-related messages posted on Yahoo! message forums

help predict market volatility. Tetlock (2007) analyses the pessimism of Wall Street Journal

columns and finds that high media pessimism can predict stock prices. Brown and Tucker (2011)

study the informativeness of firms’ Management Discussion and Analysis (MD&A) disclosures

within Form 10-K and find that changes in disclosures are positively related to economic changes.

However, they also find that despite the increasing trend in MD&A length over time, the degree

to which MD&A changes from year to year is decreasing, indicative of a decline in the usefulness

of MD&A.

Loughran and McDonald (2011) demonstrate that MD&A does not provide superior

information to whole 10-Ks. Their analyses of 10-Ks provide evidence that negativity as measured

by the Harvard Psychological Dictionary is not associated with 10-K filing returns but they create

a new dictionary that is able to detect such a relation. They also develop five additional dictionaries

(positive, uncertainty, litigious, strong modal, and weak modal) and provide evidence that these

lists can gauge disclosure tone.4

4 Disclosure tone can be used as a proxy for several developments in the firms’ operating environment. Law and Mills

(2015) use Loughran and McDonald’s dictionary for negative words and show that financially constrained firms (as

measured by the ratio of negative words in the annual reports) pursue more aggressive tax planning strategies.

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To this point, despite many meaningful studies on disclosure tone, research on the

influence of corporate leadership on tone is extremely limited. Patelli and Pedrini (2015) suggest

that tone of the top may be determined by both board of directors and chief executive officers.

They argue that the tone of the CEO letters is one fundamental way for directors to enact

leadership. They also provide empirical evidence that aggressive financial reporting is positively

associated with language that is resolute, complex, and not engaging. Bozzolan et al. (2015) report

that the management of the Fiat Group uses disclosure tone strategically to implement different

disclosure styles to communicate with various stakeholders (i.e., local press, international press,

and financial analysts) with different levels of salience and optimism. Thus, existing literature

implies that management and directors help set disclosure tone. However, to our knowledge, no

study has yet conducted a detailed analysis of the role of the board of directors in setting disclosure

tone. We pursue this research question in the context of certain board characteristics, which we

discuss in greater detail below.

2.2. Board member experience and risk aversion

Prior literature argues that risk aversion increases with age (Vroom and Pahl, 1971). Older

managers are often considered to be more sensible and prudent whereas younger and more

inexperienced managers are prone to take greater risks (Menkhoff et al., 2006). Further, the

experience gained by older directors should be helpful in advisory and monitoring capacities.

Older individuals are less tolerant of uncertainty (Jost, Glaser, Kruganski, and Sulloway, 2003).

For all these reasons, directors’ age could affect aspects of disclosure tone.

We expect that the risk aversion, sensibility and experience of older board members will

result in more moderate (i.e., less negative and less positive) tone. Meanwhile, we expect that the

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conservatism and lack of tolerance for uncertainty that comes with age will result in less uncertain

tone. Last, we expect that the risk aversion and experience of older board members will result in

more litigious tone as the board seeks to resolve doubt about the legal environment. These

expectations lead to the following empirical prediction:

H1: Board member age is associated with disclosure tone.

Whether age actually affects disclosure tone is uncertain for several reasons. Intellectual

curiosity and information processing ability decline with age (Roberts, Walton, and Viechtbauer,

2006), contributing to an increase in conservatism; however, older individuals use experience to

effectively overcome their slower information-processing ability. Older board members who are

close to retirement may be prone to moral hazard problems; for example, they might have already

established status and lack motivation to affect the disclosure choices of the firm. There is also

evidence that managerial turnover is more performance sensitive for younger managers (Chevalier

and Ellison, 1996), suggesting reduced threat of termination and perhaps lower motivation for

older board members.

2.3. Board member uniformity

In general, corporate boards are demographically quite uniform. One potentially important

departure from board uniformity is the participation of female board members. Corporate boards

are slowly becoming more diverse (i.e. less uniform), as females made up less than five percent of

directors in 1984 (Bilimoria and Piderit 1994) but comprise nearly nine percent of our sample.

This emerging diversity has the potential to affect board decisions, and we evaluate its effect on

annual report tone. While psychological studies have disproven many perceived cognitive

differences between the sexes (e.g., Spelke 2005), it is possible that differences arise through

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specific cognitive or experiential means. For instance, Barber and Odean (2001) suggest that men

are generally more overconfident than women in a financial context. Likewise, uniformity makes

it less likely that directors bring a range of experiences and skill sets to the board, making like-

minded thinking more prevalent. Thus, we expect that the overconfidence and similar experiences

of uniform boards result in more emphatic tone, leading to our next hypothesis:

H2: Board uniformity is associated with disclosure tone.

Although we expect uniformity to affect tone, there are reasons to believe otherwise.

Bilimoria and Piderit (1994) indicate that female directors experience sex-based bias that could

limit their effects. Ahern and Dittmar (2011) note that female board members tend to be younger

and less experienced, which could limit their voice in the firm relative to older, more experienced

directors.

2.4. Board members’ human capital

The competence of the board is a function of directors’ knowledge of the firm, their general

managerial capability and human capital (Boyatzis et al., 2002). Board members’ human capital

could positively affect disclosure tone for two main reasons. First, board members’ human capital

is a source of competitive advantage (Khanna, Jones, and Boivie, 2014). Martikainen et al. (2015)

demonstrate that the breadth of risk disclosure coverage is negatively associated with directors’

human capital, suggesting more focused risk discussions consistent with superior judgment. To

the extent that managers and fellow directors recognize these advantages, directors with significant

human capital will be more influential as their peers value their input. Second, human capital is

associated with stronger writing skills throughout one’s adult life (Kaufman, Kaufman, Liu, and

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Johnson 2009). We expect that stronger writing skills associated with human capital will manifest

in the form of richer language either through direct involvement in actual writing or through the

reviewing of multiple drafts. Both of these reasons suggest greater tone across all dimensions:

positive, negative, uncertain, and litigious. We predict the following:

H3: Board members’ human capital is associated with disclosure tone.

We would not expect to find this relation if human capital causes overanalysis, leading to

weaker language. Further, if part of the competitive advantage attributable to human capital is in

protecting trade secrets, disclosures may actually be more opaque and therefore less tonal in the

presence of directors with significant human capital.

2.5 Board member turnover

Board member turnover is likely to affect disclosure tone because following turnover, new

directors are likely to join the board of directors. Their fresh perspective and new experience will

alter the collective makeup of the board. For instance, Hambrick et al. (1993) show that executives’

tenure in the organization, is positively related to commitment to the status quo, suggesting that

board member tenure would be negatively associated with richness of disclosure tone. Therefore,

we expect that the addition of outsiders will also prompt reevaluation of boilerplate disclosures

(Brown and Tucker, 2011), thus increasing the richness of disclosure tone. We predict the

following:

H4: Board member turnover is associated with disclosure tone.

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Despite our expectation, it is not assured that board turnover affects disclosure tone. New

board members’ contributions may be discounted relative to those of established, trusted directors.

New board members also lack familiarity with the specific firm and thus may produce more

boilerplate disclosures.

In the next section, we describe the research design we use to test our hypotheses.

3. Research Design

3.1. Disclosure tone in large sample studies

Beginning with Antweiler and Frank (2004) and Tetlock (2007), disclosure tone has

become a popular method for analyzing the sentiment of disclosures because it can be examined

through automated content analysis in large sample studies. As described in Li (2010b) and

summarized in Purda and Skillicorn (2015), two main streams for analyzing disclosure tone in

large sample studies prevail. The first approach builds on the existing literature in linguistics and

psychology. In this approach, a scholar uses a manually created dictionary that (s)he predicts to be

associated with a particular disclosure sentiment such as negativity. Dictionaries refer to

predefined lists of words, also known as "bags of words.” The appearance of these words in

financial reports is then automatically analyzed. This approach has been used in Tetlock (2007),

Pennebaker et al. (2007), Loughran and McDonald (2011), and Larcker and Zakolyukina (2012),

for example. The advantage of predefined word lists is that they are replicable and their effects

have been scientifically proven. Yet context is important to consider, as words relevant to financial

disclosures may have different meanings or importance in other contexts. Loughran and McDonald

14

(2011) show in a large sample of 10-Ks that almost three-fourths of the words identified as negative

by Harvard Dictionary are irrelevant in a financial context, leading them to develop alternative

word lists that better capture tone in financial texts. We follow Loughran and McDonald (2011)

and use their financial-text-specific tone measures.5

The second approach employs statistical methods to let the data determine which words

are relevant. The origins of this method are in computer science but recently it has been used to

analyze the tone of business texts (Antweiler and Frank, 2004; Li, 2010a; Cecchini et al., 2010;

Goel et al., 2010; Humphreys et al., 2011). Data-generated world lists may permit higher

coverage; however, they can be criticized for a failure to specifically address different areas of

disclosure tone, and it can be unclear what their word lists capture. Sometimes both predefined

word lists and data-generated word lists are used in parallel (Goel et al., 2010; Humphreys et al.,

2011). Since we are interested in identifying predicted relations between specific board

characteristics and specific aspects of tone, we employ the first approach.

3.2. Measures of disclosure tone

We use the following four tone measures for firms’ annual reports (10-K and 10-K405)

computed by Loughran and McDonald (2011): negative, positive, uncertainty and litigious. For

example, the dictionary of negative words contains a list of words and word combinations that

normally signify negativity in a financial context (see Equation 1a). We compute the applied tone

measures by dividing the number of specific tonal words in a given filing by the number of total

5 We thank Bill McDonald for making the tone measures and dictionaries available. They can be downloaded from

his website (http://www3.nd.edu/~mcdonald).

15

words in that filing. This procedure gives us the following measures which we use as the dependent

variables in the study6:

Negativity_ratio = ratio of negative words to total words in the firm’s annual report

(e.g., loss, bankruptcy, indebtedness, felony, misstated,

discontinued, expire, unable). (1a)

Positivity_ratio = ratio of positive words to total words in the firm’s annual report

(e.g., beneficial, excellent, innovative). (1b)

Uncertainty_ratio = ratio of uncertainty-related words to total words in the firm’s

annual report (e.g., ambiguity, approximate, assume, risk).

These words emphasize uncertainty over risk. (1c)

Litigious_ratio = ratio of litigious words to total words in the firm’s annual report

(e.g., admission, breach, defendant, plaintiff, remand, testimony).

These words relate to the firm’s legal environment. (1d)

3.3. Independent variables

We compute independent variables for both outside and inside directors. Outside directors

are defined as board members who are not employees of the company. Inside directors are defined

as full-time employees of the company who are on its board. In the variables, suffix Inside

describes inside (“executive”) directors and suffix Outside describes outside (“non-executive”)

directors. We further split the outside director variables into separate measures for audit committee

members and non-audit committee members, as we expect audit committee members to be more

heavily involved with the financial reporting process than non-audit committee members. We

denote audit committee member characteristics with the suffix ACOM, which in a regression

6 In supplemental tests we also review dictionaries for words with strong and weak modality as specified in Loughran

and McDonald (2011) and a dictionary for constraining words as specified in Bodnaruk et al. (2015).

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framework capture the incremental assocations of audit committee member characteristics that are

incremental to those of outside directors in general.

We use age to proxy for risk aversion and experience following Vroom and Pahl (1971)

and Menkhoff et al. (2006). Age_Inside, Age_Outside, and Age_ACOM measure the average age

of the board members in each of the three director groups. We use educational attainment to proxy

for human capital following Boyatzis et al. (2002), Kaufman et al. (2009), and Khanna et al.

(2014), among others. Edu_Inside, Edu_Outside, and Edu_ACOM measure the average years of

education for board members. We measure director turnover using Attrition to capture the turnover

rate of the board in the preceding three years.

The control variables consist of firm fundamentals that are expected to capture differences

in firms’ disclosure choices according to previous disclosure literature. Size is the natural logarithm

of the total assets of the firm. Larger firm size is consistently linked in more intensive corporate

disclosures (e.g., Lang and Lundholm, 1993; Brammer and Pavelin, 2006). One potential reason

is that large firms are more vulnerable to political costs, increasing demand for disclosure (Watts

and Zimmerman, 1978). ROA measures firm profitability as the firm’s return on assets. Prior

evidence on the impact of profitability on disclosures is mixed (Leuz, 2000; Prencipe, 2004;

Troberg et al. 2010; Miihkinen, 2012).

We also include more specific control variables for firm risk. StdevROA is the five-year

standard deviation of return on assets, which captures variation in firms’ business risk. Leverage

measures the financial leverage and bankruptcy risk of the firm. It is the ratio of long-term debt to

total assets of the firm. BTM is the book-to-market ratio, which measures growth prospects. It is

the ratio of total book value of common equity to year-end market capitalization.

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Governance controls include variables that potentially impact disclosure choices and tone.

The incumbent auditor plays a role in guiding and supervising annual reports. Auditor expertise

and reputational risk are positively associated with audit firm size (e.g., Craswell, Francis, and

Taylor 1995), so we include a control for auditor size, Big_N. Big_N is a dummy variable equal

to 1 if a firm is audited by a Big N auditor, and zero otherwise. Board_Size contains the total

number of board members in the corporate board. Outside_ratio is the ratio of outside board

members to total number of board members in the corporate board. Complete variable definitions

are provided in the Appendix.

3.4. Regression model

We test our hypotheses using cross-sectional ordinary least squares regression analysis.7

The dependent variable is the specific disclosure tone variable, Tone (Negativity_ratio,

Positivity_ratio, Uncertainty_ratio, or Litigious_ratio). Three blocks of independent variables are

included as explanatory variables. First, board characteristics include our main test variables which

we expect to capture the associations between disclosure tone and risk aversion/experience, human

capital, uniformity, and turnover. Second, firm fundamentals include control variables as

suggested in the previous literature. Third, governance controls consist of basic governance

controls that are addressed in prior research.

We include year fixed effects in the model to capture the impact of macroeconomic and

regulatory factors that may cause systematic time series variation in firms’ disclosure tone. We

include industry fixed effects to control for specific trends and effects caused by the differing

7 Alternatively, we employ a generalized linear model to account for the restricted range of the dependent variables.

Inferences from regressions using this model yield similar results. We present the OLS results for ease of

interpretation.

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operating environments of various industries. We compute regression coefficients using

heteroscedasticity-corrected standard errors clustered by firm. In addition, we winsorize the

continuous variables at the 1 percent and 99 percent levels in the main tests.

The main tests in the paper involve the estimation of the following multivariate regressions

for different disclosure tone measures. β represent the regression parameters to be estimated, e

represents the regression residual, subscripts i and t refer to the firm and year, respectively):

𝑇𝑜𝑛𝑒𝑖𝑡 = 𝛽0 + ∑ 𝛽𝑐𝑏𝑜𝑎𝑟𝑑 𝑐ℎ𝑎𝑟𝑎𝑐𝑡𝑒𝑟𝑖𝑠𝑡𝑖𝑐𝑠𝑖𝑡 + ∑ 𝛽𝑓𝑓𝑖𝑟𝑚 𝑓𝑢𝑛𝑑𝑎𝑚𝑒𝑛𝑡𝑎𝑙𝑠𝑖𝑡𝑓 𝑐 +

∑ 𝛽𝑔𝑔 𝑔𝑜𝑣𝑒𝑟𝑛𝑎𝑛𝑐𝑒 𝑐𝑜𝑛𝑡𝑟𝑜𝑙𝑠𝑖𝑡 + 𝑒𝑖𝑡 (2)

4. Data, Sample and Descriptive Statistics

4.1. Data and sample selection

We analyze the board characteristics and tone of SEC registrants’ 10-Ks from years 2003 through

2014. Table 1 shows our sample selection process. We retrieve data on board characteristics from

BoardEx. The database contains biographical information on most board members and senior

executives in North America. We use CIK numbers to match the board characteristics relevant for

this study with BoardEx CIK-code file (file A), leaving 73,103 observations. We obtain values for

the tone variables from Bill McDonald’s web site which contains data for firms’ 1994-2014 annual

reports (10-K and 10-K405). Matching the initial file with Compustat annual data and yields

65,142 observations (file B).

Next, we match file A with file B using CIK codes, producing 47,504 observations. We

exclude fiscal year 2002 as a pre-SOX year and omit financial institutions (SIC 6000-6999) as in

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prior literature, leaving 35,457 observations.8 We also exclude 1,660 observations from utility

companies, whose regulated environments may produce different disclosure tone. We exclude

amended 10-Ks, 10K-SBs, and firm-years whose 10-K filings include fewer than 2000 words in

line with previous studies (e.g. Loughran and McDonald, 2011; Law and Mills, 2015). Finally, we

lose 10,642 observations because of missing data for some of the independent variables. The final

sample includes 22,748 observations.

(Table 1)

4.2. Descriptive statistics

Table 2a provides descriptive statistics for our variables. The mean values for

Negativity_ratio, Positivity_ratio, Uncertainty_ratio, and Litigious_ratio are 0.018, 0.007, 0.014,

and 0.014, respectively, meaning that cumulatively about 5.3 percent of the words in firms’ annual

reports contain negative, positive, uncertain, or litigious tone as specified in the respective

dictionaries. Interestingly, there are large differences in the tone of the reports. For example, the

maximum value of Negativity_ratio is 2.9 percent whereas the minimum value is 0.5 percent. In a

similar vein, the maximum value of Litigious_ratio is 3.7 percent whereas the minimum value is

0.4 percent. The documented standard deviation is highest for the litigious ratio.

The mean age of inside directors is 55.063 years. Minimum and maximum ages are 74

years and 37 years respectively. Outside directors tend to be slightly older with a mean age of

60.289 years. Maximum and minimum ages are 78 years and 40 years, respectively.

8 Fiscal year 2002 was kept in the sample in the early phases of matching to make sure that we do not lose observations

whose Compustat datadates are in 2002 but relate to fiscal year 2003.

20

The mean of Edu_Inside is 2.026 and the mean of Edu_Outside is 2.234, meaning that the

average director has more than two educational qualifications. In real-world terms, one might think

of this as having undergraduate and master’s degrees, or undergraduate and MBA degrees.

However, the statistics document variation in boards’ average education levels as can be seen from

the wide range between maximum and minimum values of these variables. Further, 11.4 percent

of outside directors have CFO experience, a qualification which jumps to 19 percent for those on

the audit committee.

The means for Attrition_Inside and Attrition_Outside are 38.060 and 28.393, respectively.

The attrition rate of inside (outside) directors indicates that about 38 (28) percent of them are

replaced during an average three-year period. This suggests that inside directors experience more

turnover than outside directors; however, one must bear in mind that there are typically only one

or two inside directors on the board. The maximum values of the variables are over 100 which

reflects that in some firms, the number of directors who have left in the past three years is higher

than the number of directors on the board.

(Table 2A)

In terms of control variables, the distributions of most variables are fairly normally

distributed as the mean and median values are close to each other. However, the means and

medians of ROA, StdevROA, and Leverage differ meaningfully, indicative of large firms skewing

the means of profitability, business risk, and financial structure. About 34 percent of our

observations are loss years, allowing us to develop insights as to the relation between tone and

board characteristics under both profit and loss conditions.

21

5. Empirical Results

5.1. Univariate analyses

We present a correlation matrix of the variables in Table 2b. The Pearson correlation

coefficients demonstrate that disclosure tone scores are significantly correlated with each other.

However, the coefficients are relatively low with the highest absolute correlation being between

uncertainty and litigiousness (-0.504). This suggests that the use of different tones are interrelated

but the use of any particular tone is also dependent on many other factors. Positive and negative

tone are positively correlated consistent with both reflecting rich disclosure rather than strictly

financial factors.

Age is negatively and significantly correlated with negative and positive tone. Education is

positively and significantly correlated with all tone measures while CFO experience has similar

positive associations with negative, positive, and uncertain tone. Attrition is positively and

significantly correlated with negative and litigious tone, but significantly negatively correlated

with uncertain tone. The correlation coefficients between the tone variables and the test variables

suggests that tone varies with board characteristics.

(Table 2b)

5.2. Multivariate analyses

Table 3 reports regression results from multivariate OLS regressions of annual report tone

on board characteristics and control variables. Column 1 estimates negative disclosure tone with

22

an adjusted R-squared of 17.9 percent, suggesting reasonable explanatory power. Age_Inside and

Age_Outside are negatively and statistically significantly associated with the Negativity_ratio.

This finding provides evidence that older average age of the board is associated with the use of

relatively fewer negative words in annual reports, consistent with risk aversion and experience

causing more cautious language. The economic magnitude of the effect is such that a ten year

greater average age across inside (outside) directors corresponds to an approximate 2.0 (0.9)

percentage point reduction in the ratio of negative words to total words, which is a large effect

considering negative words make up only 1.8 percent of total words. Audit committee age lacks a

statistically significant relation as evidenced by the insignificant coefficient estimate on

Age_ACOM. Size, Loss, and Big_N are positively and statistically significantly related to the use

of negative words, while BTM, Leverage, and Board_Size exhibit a negative and statistically

significant relation.

The relation between negative tone and gender uniformity is reflected in the statistically

significant positive coefficient estimates on Male_Outside and Male_ACOM. These coefficient

estimates indicate that boards with higher ratios of male outside directors to total outside directors

use more negative tone. It is consistent with less diverse (i.e. more uniform) boards producing

stronger language as a result of similar viewpoints.

All three Education variables are positively and statistically significantly related to

negative tone.9 The economic magnitude of the coefficient estimate on Edu_Outside indicates that

an average difference of one educational qualification (e.g., moving from the average outside

9 We cannot state conclusively whether the value of directors’ human capital as measured by education level is

driven by intelligence. More intelligent directors are prone to obtain more education. Pedagogical literature shows

that university performance is driven by several factors such as intelligence and existing knowledge. However, this

should not threaten the main conclusions if the level of education is a reasonable proxy for factors that determine

board member competence.

23

director having a bachelor’s degree to a master’s degree) results in a 1.46 percentage point increase

in the ratio of negative words to total words.10 While this effect seems quite large, we emphasize

that such a move would require an additional degree for every outside director. Continuing with

our analysis of directors’ human capital, there is a positive and statistically significant relation

between CFO_Exp_Outside but not on CFO_Exp_ACOM, suggesting that CFO experience on the

board contributes to negative tone even if the outside director in question does not serve on the

audit committee.

Board turnover as measured by all three Attrition variables is positively and statistically

significantly associated with negative tone. These results suggest that new directors bring their

own disclosure style to the board, regardless of whether they are inside or outside directors and

whether they are on or off the audit committee. An alternative explanation for this finding is that

financially distressed firms tend to both turn over their directors and produce annual reports with

relatively negative tone. We explore this possibility further in Section 5.2. The effects of education

and attrition on negative tone occur regardless of director type (inside, outside not on audit

committee, or outside on audit committee).

The conclusions we draw from Column 1 are as follows. Director age is negatively related

to negative tone, consistent with risk aversion prompting less rich disclosure. Uniformity is

positively associated with negative, positive, and uncertain tone suggesting that director uniformity

leads to richer language in annual reports. Outside director education and CFO experience are

positively related to negative tone, consistent with richer disclosure by directors with highly

developed human capital. Board turnover is positively associated with negative tone after

10 Coefficient estimate on Edu_Outside / mean Negative_ratio = 0.000262 / 0.018 = 0.0146.

24

controlling for financial performance, consistent with new directors bringing their own disclosure

voice to the firm.

We provide regression results for the dependent variable Positivity_ratio in Column 2. To

some extent, the results are similar to those reported for our analysis of Negativity_ratio in Column

1. Inside director age is negatively and statistically significantly associated with positive tone while

outside director uniformity and education are positively and statistically significantly associated

with positive tone. Audit committee member education has an incremental positive association

with positive tone. These findings are indicative of similar decision processes behind the use of

negative and positive words in firms’ financial reports. However, there are some differences.

Attrition_Inside is negatively and significantly associated with positive tone, suggesting that

executive turnover reduces the use of positive tone. Outside director and audit committee member

attrition and CFO experience yield no statistically significant relations with positive tone. Relative

to negative tone, it appears that positive tone is somewhat more difficult to explain, consistent with

Loughran and McDonald (2011) which focuses on negative tone.

Column 3 depicts regression results for the dependent variable Uncertainty_ratio. The

results follow a similar pattern to Column 1. All three dimensions of director age are negatively

related to uncertain tone. This is consistent with more experienced, risk averse directors being

reluctant to present information in uncertain terms. All three dimensions of director uniformity are

positively associated with uncertain tone, suggesting that like-mindedness of similar board

members facilitates the provision of uncertain language. We find that inside and outside directors’

human capital (as measured by education as well as CFO experience for outside directors) is

positively associated with uncertain tone, suggesting that more qualified and confident directors

are willing to discuss uncertainty. Interestingly, despite the strong effects of human capital on

25

uncertain tone we do not find incremental effects of human capital for audit committee members

as evidenced by the statistically insignificant coefficient estimates on Edu_ACOM and

CFO_Exp_ACOM. Similarly, Attrition_Inside and Attrition_Outside are negatively and

significantly associated with uncertain disclosure tone, while Attrition_ACOM is marginally

statistically significant in the same direction. Again, this finding provides evidence that newcomers

to the board impact disclosure style; in particular, new board members avoid uncertain tone.

Column 4 shows the results of our analysis of litigious tone. Age_Outside and Age_ACOM

are positively and significantly associated with litigious tone, indicating that older average outside

director age increases litigious tone. This finding is consistent with older board members’ risk

aversion prompting them to recommend more litigious tone to inform shareholders of the firm’s

legal environment with the expectation that such disclosure might help avoid litigation risk; for

example, in the form of shareholder class-action lawsuits. Education among all three director

groups is positively and significantly associated with litigious tone, again consistent with human

capital raising the richness of disclosure. While there is a borderline significant negative

coefficient estimate on CFO_Exp_ACOM, it is more than offset by the positive coefficient estimate

on CFO_Exp_Outside, meaning that in sum there is no detectable relation between all outside

directors’ CFO experience and litigious tone. All three attrition rates are positively and

significantly associated with litigious tone in line with the interpretation that new directors bring

their own disclosure choices and style, and consistent with new directors attempting to discourage

potential litigation. Among control variables, size is positively and significantly associated with

litigious tone which suggests that larger firms attempt to avoid litigation costs by using specific

tone.

(Table 3)

5.3 Supplemental tests

26

We conduct a variety of supplemental tests to ensure that our results are not sensitive to

certain research choices. First, we examine whether the sign of earnings (i.e., profit or loss) affects

our findings and report the results of these tests in Tables 4a and 4b. Among profitable firms, we

have 15,016 observations as reported in Table 4a. Our results in the positive earnings subsample

are quite similar to our results for the full sample. In general, there is evidence that age is negatively

associated with negative and uncertain tone, while positively associated with litigious tone. Inside

director age is also negatively associated with positive tone. We find that a greater proportion of

male outside directors is positively associated with negative and uncertain tone, both on and off

the audit committee, while male inside directors are also associated with more uncertain tone. We

detect evidence that education is positively associated with negative, positive and litigious tone.

Outside director CFO experience is positively associated with negative and uncertain tone, while

audit committee CFO experience is weakly negatively associated with litigious tone. Our findings

on director turnover are also quite similar to our main results, in that we find evidence of positive

associations with negative and litigious tone but negative associations with uncertain tone.

(Table 4a)

Table 4b contains the results of our tests for firms with negative earnings. In particular, we

are interested to see whether our inferences change materially when financial performance is poor.

Since we condition on the sign of earnings, our number of observations falls to 7,732. Nonetheless,

we continue to document some statistically significant associations. We find that inside director

age is negatively related to negative, positive, and uncertain tone, and that outside director age is

negatively associated with uncertain tone. Gender uniformity remains positively associated with

negative, positive and uncertain tone, with some evidence of positive associations with litigious

tone. Inside and outside director education levels are positively related to negative, positive, and

27

uncertain tone, with audit committee education having an incremental effect only on negative tone.

Outside director CFO experience is positively associated with negative tone, but we find no other

effects of directors’ CFO experience in loss firm-years. We find that inside director turnover has

sweeping effects, with positive effects on negative and litigious tone yet negative effects on

positive and uncertain tone. Outside director attrition is positively linked to negative and litigious

tone, and we find no incremental effects of audit committee attrition. Altogether, the results

presented in Table 4b are consistent with our main results and help eliminate the suggestion that

certain board committee characteristics, in particular attrition, may be capturing poor financial

performance. Rather, we show that these characteristics are linked to disclosure tone regardless of

financial performance.

(Table 4b)

Untabulated results show that the use of negative words has increased beginning with

annual reports for fiscal year 2008. This implies that the financial crisis might have caused

increases in the negative tone of disclosures. We consider whether risk aversion and experience

moderate board members’ use of tone around the financial crisis. We compute a new dummy

variable, Post_crisis, which obtains a value of 1 if the annual reports are released after the collapse

of the stock markets, i.e., after the release of the 2007 annual reports, and zero otherwise. Since

we expect the financial crisis to have the greatest influence on firms with relatively risk-averse

directors, we interact Post_crisis with Age and include these three new interaction terms in the

regressions. We repeat these tests for all four disclosure tone measures. Interestingly, the results

(untabulated) provide evidence that the interaction variable between Post_crisis and Age_NED is

positive and significant when Uncertainty_ratio is the dependent variable. This finding indicates

that the negative and significant association between outside board members’ age and the use of

28

uncertain tone has diminished following the financial crisis. For other tone measures we cannot

document any statistically significant interaction effect. The results suggest that board member

responsiveness to changes in the economic environment may be partly explained by risk aversion

and experience.

Next, we test the associations between board characteristics and the tone measures

retrieved from three other tone dictionaries specified in Loughran and McDonald (2011) and

Bodnaruk et al. (2015): modal weak words, modal strong words and constraining words (results

untabulated). For modal weak and strong tone, age (education) is negatively (positively) and

significantly associated, again consistent with risk aversion and experience producing less rich

language and human capital producing richer language. Moreover, age is negatively and

significantly associated with the use of constraining words perhaps again consistent with

manifestations of risk aversion, while education is not significantly associated with constraining

tone. Attrition_rate_Inside is positively and significantly related to constraining tone, suggesting

that turnover is associated with discussion of financial constraints.

In additional untabulated analyses, we add controls for directors’ financial incentives using

two variables, ToLiquidWealth and AvSalary. ToLiquidWealth measures directors’ ownership in

the firm as the sum of value of shares held and the intrinsic value of exercisable options. The

average outside director has accumulated equity wealth (ToLiquidWealth) worth over $5 million

and receives cash compensation of nearly $50,000 for service on the board (AvSalary). On average,

the inside directors have much higher wealth at nearly $20 million and annual cash compensation

at $758,366 than outside directors, which is logical as these executives are employed full-time by

the firm. We expect that directors’ financial self-interest aligns their incentives with those of

existing shareholders and motivates them to maximize firm value via disclosure tone. AvSalary

29

measures the average annual salary of the board of directors. Because salary is not directly linked

to firm value, we do not expect it to increase directors’ motives for value maximization. Since

compensation data is not available for the bulk of directors, our sample is drastically smaller in

these tests (n = 4,578), yet we continue to find evidence that director age, uniformity, human

capital, and turnover help determine disclosure tone in the same directions as in our main tests.

In untabulated results, we subject our data to a generalized linear model with a logit link and the

binomial family in order to more accurately estimate our model given that the values of our

dependent variables values fall between zero and one. This estimation yields inferences that are

qualitatively very similar to our main results. We present the OLS estimates for ease of

interpretability.

Since it is not possible to observe and/or control for every omitted confounding variable,

we conduct Impact Threshold of a Confounding Variable (ITCV) analysis to give the reader an

idea of how such variables would or would not affect our findings (Frank, 2000).11 ITCV

analysis can provide evidence on the internal and external validity of the results. In our case, we

are most concerned with internal validity, while external validity seems strong in our large,

multi-year, multi-industry sample.12 ITCV internal validity tests estimate how strongly the

omitted (confounding) variable must correlate with the treatment (independent) variable to

invalidate the documented relation between the treatment variable and the dependent (outcome)

11 We thank Ken Frank for providing guidance on conducting ITCV-analysis on his website

https://msu.edu/~kenfrank/research.htm 12 In untabulated analysis, we run ITCV external validity checks and generally find that our results have high

external validity. For example, it would require 81.8 percent of our sample to be be replaced with a group of

observations having zero correlation between the treatment (Age_Inside) and outcome (Negative_ratio) variable to

invalidate our observed relation between Age_Inside and Negative_ratio. Of all our observed relations, the lowest

replacement threshold exists for CFO_Exp_ACOM in the Litigious_ratio model at 15.4 percent.

30

variable. Table 5 reports the results of ITCV analysis. ITCV-index is the lowest product of the

partial correlation between the outcome variable and the confounding variable and the partial

correlation between the treatment variable and the confounding variable that would make the

coefficient on the treatment variable statistically insignificant at the 5 percent level. The impact

of the confounding variable is maximized if its mutual correlation between the treatment and

outcome variable is equal. Thus, the threshold correlation (th_corr) is the square root of the

absolute value of ITCV-index, which facilitates interpretation of ITCV analysis by providing the

minimum correlation necessary between the confounding variable and both the outcome and

treatment variables for the relation between the treatment variable and outcome variable to

become statistically insignificant at the 5 percent level. For example, if Age_Inside is the

treatment variable, the correlation between the confounding variable and the outcome variable

and the correlation between the confounding variable and the treatment variable would both need

to be higher than 0.223 to invalidate our inferences.13

In the negativity model, we report the lowest threshold correlation (0.126) for the

treatment variable Age_Outside and the highest value for Attrition_Inside (0.263). Thus, using

Age_Outside as the treatment variable, the correlation between it and the confounding variable

must be higher than 0.126 to invalidate the documented negative relation between Age_Outside

and Negative_ratio. Comparing this value to the correlation between Age_Outside and

Negative_ratio from Table 2b (-0.054), the omitted variable would need to have more than twice

as high of a correlation with the outcome variable than the existing treatment variable has. Using

Attrition_Inside as the treatment variable, the correlation between the outcome variable and the

confounding variable must be higher than 0.263. The existing correlation between Attrition_Inside

13 One of the correlations would have to be negative because the sign of the relation between the treatment and

outcome variable is negative.

31

and Negativity_ratio from Table 2b is 0.127, again indicating that a confounding variable would

need to have more than twice as large of a correlation.

In the positivity model the lowest ITCV-index value (-0.010) is for Attrition_Inside which

means that the threshold correlation is 0.102. In the uncertainty and litigation models we obtain

the lowest ITCV-values on the treatment variables Attrition_Outside (-0.007) and

CFO_Exp_ACOM (-0.002) which equate to threshold correlations of 0.082 and 0.041,

respectively.

The average threshold correlation is 0.183, while the average correlation coefficient

between the treatment and outcome variables is 0.042, meaning that for our average-strength result

to become statistically insignificant, an omitted correlated variable would have to have more than

four times stronger correlation with the outcome variable than the existing treatment variable has,

and it must also have at least this strong of a correlation with the treatment variable. Given the

modest correlations even among closely related board characteristics, it seems unlikely that our

results would be significantly affected by confounding variables.14 We conclude that the ITCV

analysis greatly mitigates concern about omitted correlated variable bias.

(Table 5)

Our next supplemental test uses a variance inflation factor test to determine whether the

correlations between the independent variables unduly inflate the standard errors in our

multivariate analyses. In untabulated results, these tests indicate that multicollinearity is not a

concern.

14 For example, the correlation between Age_Inside and Age_Outside, which measure the mean ages of inside and

outside directors (not on the audit committee) respectively, is one of the highest correlations between board

characteristics, yet is only 0.241. For comparison, the threshold correlation on Age_Inside is 0.223.

32

Finally, we rerun our main tests applying two-way clustering as suggested by Petersen

(2009).15 We cluster on firm and year and provide qualitatively similar results as in the main tests.

We avoid year clustering in our main tests due to our inclusion of year fixed effects intended to

capture time trends in disclosure.

6. Summary and conclusions

Corporate boards of directors play important advisory and gatekeeping roles, often

influencing disclosure tone through their editorial role over financial reporting. We examine the

role of the board of directors in setting disclosure tone. More specifically, we investigate whether

board member risk aversion and experience, uniformity, human capital, and turnover affect

disclosure tone.

We sample 22,748 10-K annual reports filed by SEC registrants between 2003-2014 and

identify four primary aspects of disclosure tone following Loughran and McDonald (2011):

negativity, positivity, uncertainty, and litigiousness. We proxy for directors’ risk aversion and

experience using director age; uniformity using gender balance; human capital using educational

attainment and CFO experience; and turnover using 3-year attrition rates.

To summarize our results, we find that age is negatively associated with the use of positive,

negative, and uncertain tone; meanwhile, there is some evidence that age is positively associated

with litigious tone. These results are consistent with older directors’ risk aversion prompting more

guarded language in annual reports. We document that directors’ gender uniformity is positively

associated with negative and uncertain tone, suggesting that uniform boards produce richer

15 See http://www.kellogg.northwestern.edu/faculty/petersen/htm/papers/se/se_programming.htm

33

disclosure with fewer checks and balances. We find some evidence that outside directors’

educational attainment is positively associated with negative, positive, uncertain, and litigious

tone, all consistent with highly educated board members writing richly. Similarly, outside directors

who have CFO experience bring more negative and more uncertain tone. We find that board

turnover is positively associated with negative tone and litigious tone, and negatively related to

positive and uncertain tone, consistent with new board members bringing fresh voices to corporate

disclosure. Our results suggest that directors’ risk aversion/ experience, uniformity, human capital,

and turnover all impact disclosure tone.

This paper contributes to the literature on disclosure tone. Previous literature has focused

on the economic consequences of disclosure tone and/or style (e.g., Tetlock, 2007; Loughran and

McDonald, 2011; Yang, 2012) or the role of management behind firms’ disclosure choices/style

(Bertrand and Schoar, 2003; Bamber et al., 2010; Ge et al., 2011). However, the board’s influence

on disclosure has been largely overlooked. We add to the literature by demonstrating evidence of

associations between board characteristics and the tone of 10-K reports. Further, while it has

recently been shown that 10-K report tone contains information (Loughran and McDonald 2012),

our study is one of the first to identify specific factors that influence such tone.

Our inferences are limited by the fact that we cannot be certain that the board characteristics

we identify are not picking up the effects of a correlated omitted variable; however, the robustness

of our results to subsample analyses and several supplemental tests including ITCV lends

confidence to our inferences. Nonetheless, we emphasize that we find evidence of associations and

not necessarily causal effects.

34

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40

Appendix. Variable definitions

Variable Description of the empirical measure

Dependent variables

Negative_ratio The proportion of negative words to total words in the firm’s annual report

Positive_ratio The proportion of positive words to total words in the firm’s annual report

Uncertainty_ratio The proportion of uncertainty words to total words in the firm’s annual report

Litigious_ratio The proportion of litigious words to total words in the firm’s annual report

Independent variables

Test variables

Age_Inside Average age of executive directors

Age_Outside Average age of non-executive directors outside the audit committee

Age_ACOM Average age of non-executive directors on the audit committee

Male_Inside Percentage of executive directors who are male

Male_Outside Percentage of non-executive directors who are male, outside the audit committee

Male_ACOM Percentage of non-executive directors on the audit committee who are male

Edu_Inside Total educational qualifications (i.e., degrees) held by the executive directors

divided by the number of executive directors

Edu_Outside

Total educational qualifications held by non-executive directors outside the audit

committee divided by the number of non-executive directors outside the audit

committee

Edu_ACOM Total qualifications gained by the non-executive directors on the audit committee

divided by the number of non-executive directors on the audit committee

CFO_Exp_Outside Ratio of non-executive directors outside the audit committee with CFO

experience to total number of board members

CFO_Experience_ACOM Ratio of non-executive directors on the audit committee with CFO experience to

total number of board members

Attrition_Inside

Number of executive directors that have left the board within the last year divided

by the average number of executive directors for the preceding three years.

Missing observations are supplemented with an alternative number which uses

average number of executive directors for the preceding reporting year as the

denominator.

41

Appendix. (cont.)

Variable Description of the empirical measure

Test variables (cont)

Attrition_Outside

The number of non-executive directors that have left the board within the last

year divided by the average number of non-executive directors for the preceding

three years. Missing observations are supplemented with an alternative number

which uses average number of non-executive directors for the preceding reporting

year as a denominator.

Attrition_ACOM

The turnover ratio of the audit committee members is the sum of starting_percent

and finishing_percent. starting_percent is the sum of the new audit committee

members to the size of the committee. finishing_percent is the sum of the

resigned committee members to the size of the committee.

Control variables

Size Natural logarithm of total assets

ROA

Earnings before interest and taxes scaled by the mean of the lagged and current

total assets if available. If total assets values are missing, it is scaled by the lagged

assets if available and after that lead asset values are used if needed.

StdevROA

Standard deviation of return on assets ratio measured over the last five years (t to

t-4). Five observations are used when available. Minimum of three observations

are required.

Leverage Ratio of long-term debt to total assets

Loss Binary variable set equal to 1 if a firm reported negative net income in the 10-K

filing year, and 0 otherwise

BTM Ratio of total common equity to year-end market capitalization

Big_N Binary variable set equal to 1 if a firm is audited by a Big N auditor, 0 otherwise

Board_Size Total number of board members

Ned_ratio Ratio of non-executive directors to total number of board members

42

Table 1. Sample selection

Sample selection criteria Lost

observations

Remaining

observations

Phase 1 (file A)

BoardEx data for years 1999-2014 80,335

Linking BoardEx data with BoardEx CIK-code file = file A (7,232) 73,103

Phase 2 (file B)

Compustat annual data for years 2002-2014 145,021

Tone measures for the SEC annual filings (10-K and 10-

K405) for fiscal year end dates 2002-2014 105,681

Tone measures for10-K filings after removal of late filings (7) 105,674

Matching Compustat annual data with Tone data = file B (79,879) 65,142

Phase 3 (final sample)

Matching file A with file B 47,504

Exclusion of fiscal year 2002 (pre-SOX) (1,776) 45,728

Exclusion of financial institutions (sic 6) (10,271) 35,457

Exclusion of firms in the regulated industries of utilities (sic

4900 - 4999) (1,660) 33,797

Exclusion of firm-years whose 10-K filing includes less than

2000 words (4) 33,793

Exclusion of firm-years that do not have existing board size

in the database (58) 33,735

Exclusion of firm-years that have executive directors in the

audit committee (345) 33,390

Exclusion of firm-years lacking values for other independent

variables (10,642) 22,748

This table reports our sample selection process. Our final sample consists of 22,748 observations.

43

Table 2a. Descriptive statistics (n = 22,748)

Variable Mean Median

Upper

quartile

(75 %)

Lower

quartile

(25 %)

Standard

deviation Maximum Minimum

Negativity_ratio 0.018 0.017 0.020 0.015 0.004 0.029 0.005

Positivity_ratio 0.007 0.007 0.008 0.006 0.002 0.012 0.002

Uncertainty_ratio 0.014 0.014 0.016 0.012 0.003 0.021 0.006

Litigious_ratio 0.014 0.013 0.018 0.009 0.007 0.037 0.004

Age_Inside 55.063 55.000 60.000 50.000 7.195 74.000 37.000

Age_Outside 60.289 61.000 65.000 56.000 7.043 78.000 40.000

Age_ACOM 60.626 60.750 64.333 57.000 5.606 75.000 44.333

Male_Inside 0.288 0.250 0.333 0.200 0.146 1.000 0.000

Male_Outside 0.566 0.600 0.714 0.500 0.185 1.000 0.000

Male_ACOM 0.839 1.000 1.000 0.667 0.197 1.000 0.333

Edu_Inside 2.026 2.000 2.500 1.500 0.819 5.000 1.000

Edu_Outside 2.234 2.111 2.600 2.000 0.647 4.000 1.000

Edu_ACOM 2.275 2.333 2.667 2.000 0.539 3.800 1.000

CFO_Exp_Outside 0.114 0.111 0.167 0.000 0.105 0.400 0.000

CFO_Exp_ACOM 0.190 0.200 0.333 0.000 0.206 0.667 0.000

Attrition_Inside 38.060 0.000 76.600 0.000 54.090 200.500 0.000

Attrition_Outside 28.393 23.700 41.800 11.000 25.322 119.400 0.000

Attrition_ACOM 22.500 0.000 33.333 0.000 32.500 169.000 0.000

Size 6.337 6.362 7.733 4.900 2.079 12.010 -3.270

ROA -0.006 0.069 0.123 -0.009 0.430 0.439 -9.820

StdevROA 0.105 0.043 0.091 0.021 0.429 11.420 0.001

Leverage 0.182 0.123 0.282 0.000 0.221 1.506 0.000

Loss 0.340 0.000 1.000 0.000 0.474 1.000 0.000

BTM 0.463 0.413 0.681 0.229 0.933 5.615 -13.698

Big_N 0.795 1.000 1.000 1.000 0.404 1.000 0.000

Board_Size 8.418 8.000 10.000 7.000 2.093 17.000 3.000

Outside_ratio 0.825 0.857 0.889 0.778 0.082 1.000 0.400

This table reports descriptive statistics for the tone measures and board characteristics. Variables are defined in the Appendix.

44

Table 2b: Correlation matrix

This table reports Pearson correlation coefficients for the tone measures and test variables. Variables are defined in the

Appendix. Correlation coefficients are bolded when significant at 1 percent or lower; italicized when significant at 5 percent

or lower, and underlined when significant at 10 percent or lower.

1a 1b 1c 1d 2a 2b 2c 3a 3b 3c 4a 4b 4c 5a 5b 6a 6b

1a. Negativity_ratio

1b. Positivity_ratio 0.029

1c. Uncertainty_ratio 0.282 0.247

1d. Litigious_ratio 0.268 -0.271 -0.504

2a. Age_Inside -0.108 -0.061 -0.068 -0.001

2b. Age_Outside -0.054 -0.013 -0.030 0.017 0.241

2c. Age_ACOM -0.035 -0.031 -0.012 0.006 0.254 0.268

3a. Male_Inside -0.026 -0.070 0.080 -0.094 0.022 -0.016 0.006

3b. Male_Outside 0.119 0.085 0.038 0.072 -0.064 -0.007 -0.050 -0.487

3c. Male_ACOM 0.097 0.008 0.099 -0.006 -0.047 -0.062 0.049 0.090 -0.005

4a. Edu_Inside 0.064 0.064 0.038 0.046 0.049 0.045 0.028 -0.001 0.042 0.052

4b. Edu_Outside 0.083 0.118 0.046 0.047 0.021 0.048 0.036 -0.028 0.028 0.040 0.105

4c. Edu_ACOM 0.066 0.059 0.017 0.045 0.025 0.016 0.032 -0.030 0.059 0.005 0.086 0.090

5a. CFO_Exp_Outside 0.137 0.039 0.124 -0.007 -0.065 -0.049 -0.131 -0.104 0.121 0.037 -0.002 0.009 0.025

5b. CFO_Experience_ACOM 0.112 0.032 0.098 -0.016 -0.065 -0.045 -0.137 -0.118 0.106 0.035 -0.005 0.020 0.026 0.697

6a. Attrition_Inside 0.122 0.004 -0.043 0.062 -0.154 -0.070 -0.065 -0.131 0.103 0.016 0.012 0.013 0.035 0.028 0.026

6b. Attrition_Outside 0.127 0.008 -0.006 0.056 -0.075 -0.140 -0.162 0.027 0.003 0.028 0.015 0.022 0.022 0.076 0.055 0.298

6c. Attrition_ACOM 0.066 0.023 -0.028 0.047 -0.061 -0.082 -0.161 -0.049 0.064 0.039 0.007 0.012 0.025 0.041 0.051 0.126 0.319

45

Table 3. Board characteristics and the tone of annual reports

(1) (2) (3) (4)

Negativity_ratio Positivity_ratio Uncertainty_ratio Litigious_ratio

Age_Inside -0.0000370*** -0.00001088** -0.0000254*** -0.00000755

(-5.61) (-3.83) (-5.33) (-0.76)

Age_Outside -0.0000169*** -0.00000393 -0.0000177*** 0.0000215**

(-2.58) (-1.40) (-3.79) (2.18)

Age_ACOM 0.00000356 -0.00000143 -0.0000154** 0.0000228*

(0.42) (-0.36) (-2.46) (1.77)

Male_Inside 0.000518 0.000286 0.000746** 0.000959

(1.12) (1.37) (2.20) (1.38)

Male_Outside 0.00141*** 0.000269** 0.000957*** 0.000417

(4.91) (2.25) (4.47) (0.93)

Male_ACOM 0.000985*** 0.0000275 0.000765*** 0.000350

(4.36) (0.29) (4.89) (1.05)

Edu_Inside 0.000138** 0.0000390 0.0000881** 0.000293***

(2.41) (1.55) (2.21) (3.45)

Edu_Outside 0.000262*** 0.000148*** 0.000185*** 0.000273***

(3.76) (5.10) (3.75) (2.67)

Edu_ACOM 0.000217*** 0.0000820** 0.0000823 0.000220*

(2.63) (2.30) (1.39) (1.74)

CFO_Exp_Outside 0.00191*** 0.000318 0.000993** 0.00118

(3.18) (1.32) (2.34) (1.26)

CFO_Exp_ACOM 0.000252 -0.0000587 0.000207 -0.000736*

(0.84) (-0.49) (1.00) (-1.67)

Attrition_Inside 0.00000479*** -0.000000734*** -0.00000135*** 0.00000268**

(7.47) (-2.82) (-2.98) (2.45)

Attrition_Outside 0.00000818*** 0.000000113 -0.00000248** 0.0000131***

(5.73) (0.20) (-2.37) (5.44)

Attrition_ACOM 0.00000248*** 0.000000225 -0.000000911 0.00000289*

(3.35) (0.72) (-1.62) (1.88)

Size 0.000240*** -0.0000616*** -0.0000861*** 0.000605***

(5.96) (-3.67) (-3.20) (10.23)

ROA -0.0000718 -0.000266*** 0.000104 -0.000184

(-0.72) (-4.29) (1.24) (-1.33)

StdevROA 0.0000375 -0.000135*** -0.000188** 0.000307**

(0.42) (-3.42) (-2.52) (2.52)

Leverage -0.00123*** -0.000842*** -0.00110*** 0.000972***

(-5.64) (-8.15) (-7.07) (2.78)

Loss 0.00188*** 0.0000901** 0.000171*** 0.000663***

(22.71) (2.41) (2.83) (4.89)

46

BTM -0.0000827** -0.0000735*** 0.0000459* -0.000108

(-2.02) (-3.95) (1.73) (-1.47)

Big_N 0.000782*** 0.000392*** 0.000678*** -0.000314

(5.78) (7.05) (6.81) (-1.58)

Board_Size -0.000169*** 0.0000721*** -0.000136*** 0.0000747

(-5.72) (5.14) (-6.61) (1.55)

Outside_ratio 0.000154 0.000869** -0.00128** 0.00630***

(0.18) (2.33) (-2.06) (4.96)

Intercept 0.0151*** 0.00648*** 0.0169*** -0.000911

(14.04) (13.84) (22.16) (-0.56)

Observations 22,748 22,748 22,748 22,748

Adjusted R-squared 0.179 0.176 0.176 0.079

This table reports regression results for the determinants of negative, positive, uncertain, and litigious tone in firms’

annual reports. Year and industry fixed effects are included and standard errors are clustered by firm. All variables

are winsorized at the 1 percent and 99 percent level. ***, **, and * denote statistical significance at the 1 percent, 5

percent, and 10 percent levels respectively. Variables are defined in the Appendix.

47

Table 4a. Regression using subsample of profitable firm-years

(1) (2) (3) (4)

Negativity_ratio Positivity_ratio Uncertainty_ratio Litigious_ratio

Age_Inside -0.0000409*** -0.0000135*** -0.0000239*** -0.00000864

(-4.99) (-3.95) (-4.11) (-0.68)

Age_Outside -0.0000192** -0.00000386 -0.0000180*** 0.0000314**

(-2.29) (-1.13) (-3.16) (2.51)

Age_ACOM 0.0000111 0.00000142 -0.0000176** 0.0000436***

(1.05) (0.30) (-2.32) (2.68)

Male_Inside 0.000285 0.000387 0.000807** 0.000330

(0.48) (1.55) (1.97) (0.39)

Male_Outside 0.00147*** 0.0000110 0.000966*** 0.000283

(4.38) (0.08) (3.85) (0.53)

Male_ACOM 0.000893*** -0.0000943 0.000802*** 0.000296

(3.27) (-0.86) (4.49) (0.73)

Edu_Inside 0.000164** -0.00000674 0.0000715 0.000388***

(2.28) (-0.22) (1.45) (3.61)

Edu_Outside 0.000309*** 0.0000829** 0.0000743 0.000429***

(3.56) (2.36) (1.23) (3.23)

Edu_ACOM 0.000252** 0.0000388 0.0000730 0.000235

(2.41) (0.89) (1.03) (1.46)

CFO_Exp_Outside 0.00204*** 0.000458 0.00135*** 0.00155

(2.70) (1.56) (2.63) (1.32)

CFO_Exp_ACOM 0.000116 -0.000140 0.0000672 -0.000981*

(0.31) (-0.95) (0.27) (-1.76)

Attrition_Inside 0.00000488*** -0.0000000798 -0.00000102* 0.00000260*

(5.83) (-0.24) (-1.76) (1.82)

Attrition_Outside 0.00000755*** 0.000000219 -0.00000347*** 0.0000131***

(4.08) (0.30) (-2.67) (4.20)

Attrition_ACOM 0.00000343*** 0.000000348 -0.00000134* 0.00000517**

(3.52) (0.87) (-1.88) (2.57)

Size 0.000237*** 0.00000280 -0.0000434 0.000615***

(4.59) (0.13) (-1.30) (8.04)

ROA -0.00276*** -0.000139 -0.000187 -0.0000677

(-3.76) (-0.62) (-0.56) (-0.09)

StdevROA 0.000749 -0.0000222 -0.0000523 0.000332**

(1.08) (-0.26) (-0.32) (2.07)

Leverage -0.00159*** -0.00102*** -0.00146*** 0.00125**

(-4.92) (-6.90) (-6.83) (2.48)

48

BTM -0.000460*** -0.000231*** -0.0000976 -0.000293*

(-4.63) (-3.95) (-1.17) (-1.92)

Big_N 0.000462*** 0.000299*** 0.000412*** -0.000442*

(2.66) (4.25) (3.20) (-1.66)

Board_Size -0.000185*** 0.0000716*** -0.000143*** 0.0000424

(-5.27) (4.28) (-5.87) (0.72)

Ned_ratio -0.000999 0.00110*** -0.00171** 0.00693***

(-0.95) (2.59) (-2.39) (4.60)

Constant 0.0164*** 0.00658*** 0.0177*** -0.00320

(12.21) (11.87) (19.34) (-1.60)

Observations 15,016 15,016 15,016 15,016

Adjusted R-squared 0.132 0.128 0.187 0.087

This table reports regression results for the determinants of negative, positive, uncertain, and litigious tone in firms’

annual reports. Year and industry fixed effects are included and standard errors are clustered by firm. All variables

are winsorized at the 1 percent and 99 percent level. ***, **, and * denote statistical significance at the 1 percent, 5

percent, and 10 percent levels respectively. Variables are defined in the Appendix.

49

Table 4b. Regression using subsample of loss firm-years

(1) (2) (3) (4)

Negativity_ratio Positivity_ratio Uncertainty_ratio Litigious_ratio

Age_Inside -0.0000274*** -0.00000631* -0.0000266*** -0.00000508

(-3.25) (-1.66) (-4.05) (-0.40)

Age_Outside -0.0000121 -0.00000451 -0.0000176*** 0.00000795

(-1.52) (-1.26) (-2.85) (0.58)

Age_ACOM -0.0000103 -0.00000382 -0.0000117 -0.00000627

(-0.89) (-0.78) (-1.35) (-0.35)

Male_Inside 0.00132** 0.000134 0.000854* 0.00183*

(2.31) (0.49) (1.83) (1.82)

Male_Outside 0.00120*** 0.000552*** 0.000752** 0.000491

(2.97) (3.26) (2.50) (0.78)

Male_ACOM 0.00103*** 0.000205 0.000686*** 0.000390

(3.25) (1.51) (2.83) (0.77)

Edu_Inside 0.000117* 0.0000783** 0.0000963* 0.000122

(1.67) (2.43) (1.90) (1.08)

Edu_Outside 0.000149* 0.000188*** 0.000314*** -0.0000212

(1.66) (4.93) (4.78) (-0.16)

Edu_ACOM 0.000190* 0.0000776 0.0000563 0.000152

(1.78) (1.61) (0.67) (0.91)

CFO_Exp_Outside 0.00149** 0.000232 0.000450 0.000748

(1.99) (0.72) (0.81) (0.60)

CFO_Exp_ACOM 0.000530 0.0000539 0.000429 -0.000387

(1.37) (0.33) (1.56) (-0.65)

Attrition_Inside 0.00000446*** -0.00000158*** -0.00000188*** 0.00000299*

(5.20) (-4.35) (-2.91) (1.86)

Attrition_Outside 0.00000907*** 0.000000115 -0.00000102 0.0000128***

(4.67) (0.14) (-0.67) (3.63)

Attrition_ACOM 0.00000118 0.000000218 -0.000000101 -0.000000487

(1.04) (0.46) (-0.11) (-0.20)

Size 0.000281*** -0.000148*** -0.000132*** 0.000611***

(5.72) (-7.42) (-3.69) (7.87)

ROA -0.000201* -0.000102* 0.000137 -0.000143

(-1.93) (-1.92) (1.50) (-0.95)

StdevROA -0.0000260 -0.000135*** -0.000172* 0.000291**

(-0.29) (-3.11) (-1.95) (2.02)

Leverage -0.00102*** -0.000580*** -0.000746*** 0.000662

(-4.14) (-5.14) (-3.88) (1.50)

BTM -0.0000380 -0.0000280* 0.0000866*** -0.0000639

50

(-1.08) (-1.88) (3.38) (-0.84)

Big_N 0.00107*** 0.000469*** 0.000914*** -0.000197

(6.51) (6.86) (7.37) (-0.78)

Board_Size -0.000103** 0.0000531*** -0.000105*** 0.000138*

(-2.42) (2.91) (-3.55) (1.90)

Ned_ratio 0.00273** 0.000546 0.0000537 0.00445**

(2.54) (1.04) (0.06) (2.32)

Constant 0.0147*** 0.00671*** 0.0156*** 0.00404*

(10.79) (11.01) (14.43) (1.73)

Observations 7,732 7,732 7,732 7,732

Adjusted R-squared 0.161 0.300 0.154 0.069

This table reports regression results for the determinants of negative, positive, uncertain, and litigious tone in firms’

annual reports. Year and industry fixed effects are included and standard errors are clustered by firm. All variables

are winsorized at the 1 percent and 99 percent level. ***, **, and * denote statistical significance at the 1 percent, 5

percent, and 10 percent levels respectively. Variables are defined in the Appendix.

51

Table 5. Impact threshold of a confounding variable

Outcome variable Treatment variable ITCV-index th_corr

Negative_ratio Age_Inside -0.050 0.223

Age_Outside -0.016 0.126

Male_Outside 0.052 0.228

Male_ACOM 0.060 0.245

Edu_Inside 0.040 0.201

Edu_Outside 0.054 0.233

Edu_ACOM 0.042 0.205

CFO_Exp_Outside 0.034 0.185

Attrition_Inside 0.069 0.263

Attrition_Outside 0.054 0.232

Attrition_ACOM 0.030 0.172

Positive_ratio Age_Inside -0.032 0.178

Male_Outside 0.029 0.170

Edu_Outside 0.069 0.264

Edu_ACOM 0.039 0.198

Attrition_Inside -0.010 0.102

Uncertainty_ratio Age_Inside -0.045 0.211

Age_Outside -0.026 0.163

Age_ACOM -0.014 0.118

Male_Inside 0.023 0.151

Male_Outside 0.048 0.219

Male_ACOM 0.062 0.249

Edu_Inside 0.036 0.190

Edu_Outside 0.052 0.228

CFO_Exp_Outside 0.026 0.162

Attrition_Inside -0.011 0.104

Attrition_Outside -0.007 0.082

Litigious_ratio Age_Outside 0.030 0.172

Age_ACOM 0.026 0.160

Edu_Inside 0.045 0.211

Edu_Outside 0.036 0.189

Edu_ACOM 0.029 0.169

CFO_Exp_ACOM -0.002 0.041

Attrition_Inside 0.029 0.169

Attrition_Outside 0.047 0.217

Attrition_ACOM 0.023 0.152 This table reports the ITCV-index and threshold correlation (th_corr) for each statistically significant board characteristic from

each of the four tone regressions in Table 3. ITCV-index is the lowest product of the partial correlation between the outcome

variable and the confounding variable and the partial correlation between the treatment variable and the confounding variable

which makes the coefficient on the treatment variable statistically insignificant at the 5% level. th_corr is the square root of the

absolute value of ITCV-index, which gives the minimum correlations necessary between the confounding variable and both the

outcome and treatment variables for the relation between the treatment variable and outcome variable to become statistically

insignificant.