some people say immature information in corporate disclosures · 2020-01-07 · some people say...

Some People Say

Immature Information in Corporate Disclosures

J. Anthony Cookson, S. Katie Moon, and Joonki Noh∗

March 29, 2018

Abstract

We develop a measure of equivocation using sentences marked with the “weasel tag” onWikipedia. The weasel tag is used by Wikipedia to identify vague and unverifiable informa-tion. Consistent with this meaning, we show that corporate disclosures (10-K documents andparagraphs) with a high fraction of equivocation tend to exhibit greater uncertainty, and usemore words that express shades of possibility, particularly words that weaken (i.e., weak modalwords). Equivocation in 10-K disclosures increases when a firm faces heightened product mar-ket threats or when the firm faces greater financial constraints in equity markets. Firms also tendto use more equivocating language during periods of low and declining profitability. Consis-tent with equivocation reflecting immature information that would not otherwise be disclosed,equivocation in corporate disclosures is accompanied by an eventual positive market reaction.In addition, high-equivocation firms subsequently experience greater operating volatility thanotherwise similar firms, and invest more, particularly in intangible investments such as R&D.Collectively, these findings suggest that disclosing immature information can be valuable duringbad times.

∗Cookson and Moon are affiliated with the University of Colorado at Boulder’s Leeds School of Business and canbe contacted at [email protected] or [email protected]. Noh is affiliated with Case Western ReserveUniversity’s Weatherhead School of Management and can be contacted at [email protected]. The authors are gratefulto conference and seminar participants at the 2018 Midwest Finance Association Conference, Brigham Young University,Drexel University, Texas Christian University, University of Colorado brownbag, and the University of Utah, as well asGustaf Bellstam, Asaf Bernstein, Steve Billings, Brendan Daley, Naveen Daniel, Diego Garcia, Dave Ikenberry, RyanIsraelsen, Ralph Walkling, Brian Waters, and Jaime Zender for helpful comments and suggestions. In addition, theauthors are grateful to Jerry Hoberg and Bill McDonald for making their textual measures available on their respectivewebsites. All remaining errors are our own. First draft: August 31, 2017.

mailto:[email protected]



1 Introduction

A well functioning financial system depends critically on disclosure of financial information, yet

encouraging precise and accurate disclosure is challenging in practice (La Porta et al., 2006). The

vast majority of prior work on the precision and accuracy of disclosure tends to analyze numer-

ical measures that are straightforward to quantify (e.g., earnings manipulation following Dechow

et al., 1996), but textual information has become more important to financial markets over time

as financial texts become easier to access and process (e.g., scraping EDGAR filings). It has thus

become more important to understand the content of financial disclosures, particularly with respect

to the interpretation of the textual information. In this vein, an important concern is that qualita-

tive information in financial text is susceptible to contain vague information because regulators and

textual analysts have few reliable tools at their disposal to distinguish evasive text from verifiable

disclosure (Hwang and Kim, 2017; Hoberg and Lewis, 2017). This is a critical gap that needs to be

addressed, especially in light of recent evidence that the qualitative aspects of financial text matter

beyond quantitative disclosures (Huang et al., 2014).

In this context, we introduce to the finance literature a novel measure of linguistic imprecision,

developed from “weasel words” extracted from Wikipedia. Wikipedia defines a weasel word as “an

informal term for words and phrases aimed at creating an impression that a specific or meaningful

statement has been made, when instead only a vague or ambiguous claim has actually been commu-

nicated.” Wikipedia warns that “it can be used in advertising and in political statements, where it

can be advantageous to cause the audience to develop a misleading impression.” Moreover, the idea

of weasel words is not merely a construct within Wikipedia. Miriam-Webster dictionary defines the

term weasel word to mean, “a word used in order to evade or retreat from direct or forthright state-

ment or position.” From these definitions, weasel words are equivocating by nature and intentionally

imprecise.

Our core idea is to quantify these hallmarks of equivocation, and apply these insights to better

understand the implications of imprecise disclosure. Nevertheless, it is challenging to assemble a

reliable dictionary of weasel words (and phrases) to use as the basis for our equivocation measure.

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The primary challenge is in constructing a list that is free of researcher subjectivity. Language that

appears equivocating to one person may not appear so to another. A related issue is that there is

not a universally accepted list of weasel words. To address both of these issues, we analyze the

text of Wikipedia articles, and more importantly, the tags embedded into these articles to compile a

list of weasel keywords and phrases (henceforth “weasel keywords”) for use in our textual analysis

of corporate disclosures. Most usefully for our analysis, Wikipedia users are advised to use the

“weasel tag” when they encounter sentences or phrases in the text of Wikipedia articles that have

vague phrasing that accompanies biased or unverifiable information. Wikipedia uses this tagging

strategy to identify and crowdsource solutions to correct weasel language, which helps them provide

a more precise online encyclopedia. We use the language in these weasel-tagged sentences that are

outside of our textual corpus for main analysis to build a list of weasel keywords that is free of

researcher subjectivity.

Using our dictionary of weasel keywords, we generate a measure of equivocation at the firm-

year level (and at the paragraph level in some tests) by computing the fraction of words in each firm’s

annual 10-K filing that are weasel keywords. Consistent with equivocation capturing linguistic

imprecision, we find that 10-Ks that have a greater amount of equivocation tend to exhibit greater

uncertainty, and also contain more words that convey differing shades of meaning (weak modal

words) and positive sentiment. Yet, we note that the information contained in our equivocation

measure has distinctive and unique aspects compared to these other textual measures. For example,

even after controlling for uncertainty and (strong and weak) modality textual measures and also

other textual measures from the Loughran and McDonald (2011) dictionary, we find that disclosures

with a high amount of equivocation are more likely to be issued by young and small firms with low

profitability and low tangibility. These findings are consistent with the types of firms that would

gain most from or would face less risk from equivocation: firms when experiencing bad times (i.e.,

low or worsening profitability), firms that are difficult to quantify (i.e., young and intangible).

Next, we turn to exploring the product market conditions that lead to greater equivocation in

disclosures. We find that firms that face heightened product market threats – measured by the prod-

uct market fluidity of Hoberg et al. (2014) – increase the amount of equivocation in their subsequent

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10-K filing. We also find that firms use more equivocation when they face greater financial con-

straints (measured by the textual financial constraints of Hoberg and Maksimovic, 2015, and also

two non-textual measures introduced by Whited and Wu, 2006 and Hadlock and Pierce, 2010).

These findings suggest that disclosures with greater use of equivocation are more likely in the pres-

ence of adverse market conditions for the firm.

Digging deeper, we argue that the use of equivocation reflects the firm’s disclosure of imprecise

information that is nonetheless valuable. Consistent with this interpretation, we find that high-

equivocation firms subsequently have higher market valuations (measured by Tobin’s Q), and invest

more in both R&D and capital expenditures. Moreover, the prospects of high-equivocation firms

are more uncertain. Indeed, we find that a significant and robust increase in operating volatility

measured over the 12 quarters following a disclosure with greater equivocation. Taken together,

these findings suggest that the equivocating language in corporate disclosures reflects valuable, but

yet fundamentally uncertain earnings opportunities.

Finally, we perform several tests that complement these main findings. First, we split our tests

of financial constraints into equity constraints and debt constraints as in Hoberg and Maksimovic

(2015). Interestingly, we find that equity constraints are associated with greater equivocation, but

debt constraints are not. Second, we find that firms in which management has a greater ownership

stake in the firm’s stock use more equivocating language in their 10-K disclosures, particularly when

facing severe financial constraints. Third, we find that equivocating disclosure appears to dampen

the stock market reaction to information shocks (measured by earnings surprises, SUE, as in Livnat

and Mendenhall, 2006), with the dampening effect only for negative earnings surprises. We further

note that this dampening effect related to negative information shocks is not immediate and also

does not lead to reversals. In finding a slow, but eventual positive market reactions to equivocating

language in the 10-Ks, this result complements our interpretation that the equivocating language is

disclosure of imprecise and immature but fundamentally valuable information.

Our analysis makes several contributions, which should be of general interest. First, our evi-

dence on the use of equivocation relates to work on discretionary disclosure and persuasion through

information revelation. Discretionary disclosure leads to full disclosure in a perfect information

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environment, but not in the presence of asymmetric information, proprietary costs, or other market

frictions (Ross, 1979; Verrecchia, 1983; Kamenica and Gentzkow, 2011; Ely, 2017). Following this

line of research, recent empirical applications have focused on how the disclosure of bad news can

signal quality (Gormley et al., 2012; Gao et al., 2017). Our results that firms can mitigate the conse-

quences of negative information releases with equivocating disclosures (and hence, early revelation

of immature but valuable information) provide new perspective on this question.

Second, our analysis and identification of equivocation provides a unique perspective on the

SEC regulatory mandate to use plain English in firm disclosures, studied in Hwang and Kim (2017).

Equivocation is not especially discouraged in this SEC mandate to regulate the contextual clarity

of firm disclosures because weasel words accord with plain English, yet equivocation using weasel

words is imprecise language that affects the content of disclosures and thus firm value. In finding

that equivocation in corporate disclosures affects how investors react to negative earnings surprises,

our analysis suggests that more attention to the use of plain English and related equivocation may

be needed.

Third, our evidence on the role of product market threats (e.g., Hoberg et al., 2014; Cook-

son, 2017) and financial constraints (e.g., Hoberg and Maksimovic, 2015; Buehlmaier and Whited,

2017) in amplifying equivocation incentives provides a new perspective on the financial market con-

sequences of heightened competition and frictions in raising capital. Notably, these results suggest

that tighter constraints and tougher competition have effects on the precision of information disclo-

sures, which is a novel finding beyond kindred effects on the complexity of products (as in Celerier

and Vallee, 2017 and Carlin et al., 2012).

Finally, our work is part of a growing literature within finance and accounting that makes use

of text descriptions to study important aspects of corporate behavior. Recent text-based analyses in

corporate finance have examined linkages between firms and industries, corporate risk management,

the value of corporate culture, and innovation in mature firms (e.g., Popadak, 2013; Agarwal et al.,

2016; Hoberg and Moon, 2017; Bellstam et al., 2017). Within the broader literature on text analysis

in finance, our work is most closely related to applying textual analysis tools to analyze the tone

of financial information (Hanley and Hoberg, 2010; Dougal et al., 2012; Loughran and McDonald,

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2013; Garcia, 2013; Jegadeesh and Wu, 2017; Hoberg and Lewis, 2017). As we show in our re-

gression evidence later, our measure is sensibly related to, but distinct from the existing lexicon of

measures – many of which are available at the Loughran and McDonald (2011) master dictionary.

Relative to these other textual measures, our equivocation measure provides a useful description of

evasive language in financial disclosures, which is distinctive unto itself. Our equivocation mea-

sure provides a useful step toward quantifying evasive language in financial text, which relates to

significant interest in understanding misrepresentation in financial markets more broadly (Zingales,

2015). In this respect, we anticipate fruitful applications of the equivocation measure to understand

better market participants’ incentives to inject imprecision into the information environment.

The remainder of the paper proceeds as follows. Section 2 provides a description of Wikipedia’s

weasel tags, the construction of the weasel keyword list, and the development of our equivocation

measure. Section 3 describes the main results relating equivocation to other textual measures, firm

characteristics, and main findings on competitive threats and financial constraints. Section 4 exam-

ines mechanisms and robustness, particularly the role of equity constraints. Section 5 concludes

with future directions for research.

2 Data and Variable Construction

2.1 Wikipedia and Weasel Keywords

To construct our equivocation measure, we take the entire Wikipedia articles as our text corpus

to detect sentences of imprecise language and compile a list of weasel keywords. A study by

Wikipedia (Ganter and Strube, 2009) suggests three categories of weasel words that are 1) nu-

merically vague expressions (e.g., "many"), 2) the passive voice (e.g., "it is said"), and 3) adverbs

that weaken (e.g., "probably"). Examples of these weasel words directly given by Wikipedia as

style guidelines include “People are saying...”, “There is evidence that...”, and “It has been men-

tioned that.”1 Wikipedia users are then advised to avoid using weasel words and at the same time

to detect and mark excessive uses of such words by others using a special weasel tag, {{Weasel-

1See Wikipedia’s own article about weasel words for more details at https://en.wikipedia.org/wiki/Weasel_word.

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inline|{{subst:DATE}}}} for improvement. The examples below illustrate how the weasel tag is

used in a sentence of each article:

• “The Tic Tok Men”

Many{{weasel inline|date=March 2009}} consider this album to be the quintessential Tic Tok

sound.

• “Manu Parrotlet”

It has been said{{weasel inline|date=January 2014}} that the Manu parrotlet can be seen

along the Man on top of trees across from the Altamira beach about 25 minutes from the

Manu Resort.

• “Nathaniel Mather”

He finished his studies in England probably{{weasel inline|date=January 2014}} returning

with his brother [[Samuel Mather (Independent minister)|Samuel]] in 1650.

We process a recent Wikipedia dump completed on April 20, 2017 comprised of 17,483,910 articles

and extract sentences that contain weasel tags.2 To do so, we follow the methodology in Ganter and

Strube (2009) with the following modifications. While Ganter and Strube (2009) examine the five

words occurring right before each weasel tag, we consider all words in sentences that contain weasel

tags. We also further consider the frequencies of all those words and their bigrams and trigrams as

well to better identify potential weasel words and phrases. The bigrams and trigrams are particularly

useful to capture weasel phrases that use passive voice or appeal to anonymous authority.

Because weasel tags are removed after the language is edited and improved, the tags are not

frequently observed at any given snapshot of Wikipedia. Therefore, sentences containing weasel

tags are not pervasive, despite the large number of articles we process. We identify 433 sentences

with weasel tags in 367 articles after removing corrupt or redundant sentences. Our number of

weasel tags is slightly more than 328 weasel tags of Ganter and Strube (2009) who processed two

Wikipedia dumps with different completion dates.

2Wikipedia dumps are available for downloading at https://dumps.wikimedia.org/.

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The numbers of unique and total words in the extracted sentences containing weasel tags are

approximately 6,000 and 16,000, respectively. We sort the roughly 6,000 unique words and their

bigrams and trigrams by frequencies and assesses whether each word or phrase correctly qualifies

for a weasel word. In this raw word frequency sort, commonly used words tend to show up as most

frequent, despite not being weasel words themselves (e.g., words like “the”, “and”, and “that”). This

is a larger issue with the unigrams than it is with the bigrams or trigrams. For example, Panel (a) of

Table 1 displays the three separate lists of the top 10 most frequently mentioned unigrams, bigrams,

and trigrams in our weasel-tagged sentences.

[Insert Table 1 Here]

To ensure we do not merely pick up common language in our keyword lists, we extract a control

sample of sentences that occur three sentences later in the text of the same articles. Upon manually

inspecting these sentences, these control sentences are free of weasel language, and have the virtue

that they are on the same set of topics as the weasel text. Using these control sentences together with

the weasel-tagged sentences, we compute the saliency of the words in the weasel-tagged sentences

relative to control sentences from Goldsmith-Pinkham et al. (2016). The saliency measure captures

the degree to which the words are overused relative to common language, and is thus, appropriate

for screening our list of common language. Panel (b) of Table 1 shows how effective the saliency

screen is in filtering out common language from the list of words.

After filtering out common language using the saliency screen on unigrams, we compile our

final list of weasel keywords (unigrams, bigrams, trigrams). Further, we expand the list of weasel

keywords using variations on these words such as the singular and plural forms for nouns and the

past, present, and future tenses for verbs. We also manually eliminate redundancy in bigrams and

trigrams (especially) in cases where including both would count the same language twice.3

The dictionary of weasel keywords is distinct from notable alternatives. Specifically, Panel (c) of

Table 1 presents the top 10 most frequently used words in the 10-Ks using our dictionary of weasel3In addition, Wikipedia has published guidelines for weasel words, giving specific examples to help users identify

weasel language. Our methodology captures the vast majority of the example phrases offered by Wikipedia, but severalexample phrases in the guidelines are not in the Wikipedia dump we analyze. To maintain the most comprehensive listof weasel keywords, we also include these guideline weasel words in our final list. The complete list of weasel keywordscan be obtained by contacting the authors.

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keywords, and for comparison, the same list for uncertainty words and weak and strong modal

words taken from the Loughran and McDonald (2011) master dictionary. The most frequently

used words in each of these dictionaries have minimal overlap with one another, indicating that

our equivocation measure using weasel keywords provides unique information distinct from these

related measures. For example, numerically vague expressions such as “other”, “number of”, and

“various” are uniquely included in the top 10 most frequently used weasel words. Also a number of

passive expressions such as “said”, “considered”, and “found” are frequently used weasel keywords

in 10-Ks, although those are not included in the top 10 list. In robustness exercises, we construct

our equivocation measure purged of uncertainty and weak modal words, and show that all the main

conclusions of our analysis go through.

2.2 10-K Disclosure and Firm Equivocation Measure

The final step in our text processing procedure is to download all 10-K filings with report dates from

1997 to 2015 and extract the raw counts of how many times a given firm mentions each of the weasel

keywords in a given year. This generates a full panel of weasel vectors with 219,491 firm-year

observations. Our final sample is reduced to 80,893 firm-year observations (11,326 unique firms)

for which Compustat and CRSP data exist. We create our main equivocation measure, Equivocation

based on the weasel vectors of our weasel keywords. Equivocation is how many times the weasel

keywords are mentioned (i.e., the sum of all elements in the weasel vector) in a given firm’s 10-K

filing in a given year scaled by the total word count in the filing in the percentage term. Throughout

the paper, we focus on Equivocation as our main variable of interest.

To provide a contextual understanding of our equivocation measure, we briefly discuss several

firm and industry examples of high versus low equivocation. We find that the industry that most

uses weasel keywords is Security brokers, dealers and flotation industry at the 3-digit SIC code

level. Equivocation is also high in Membership organizations, Telegraph and other message com-

munications, Local and suburban transit and interurban passenger transportation, Legal services,

and Drugs industries. The industry that least uses weasel keywords is Cigarettes industry. Forestry,

Miscellaneous furniture and fixtures, Electronic and other electrical equipment and components, ex-

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cept computer equipment, Steam and air-conditioning supply, and Land subdividers and developers

industries follow.

We also examine which firms most or least use weasel keywords in their filings. xG Technology,

Inc., a company that sells communications equipments, has the highest Equivocation. Essendant

Inc, a wholesale company of paper and paper products, has the lowest Equivocation. We present

below short business descriptions of the two firms.

xG Technology, Inc. (the most equivocating firm)

The overarching strategy of xG Technology, Inc. (“xG Technology", “xG", the “Com-

pany", “we", “our", “us") is to design, develop and deliver advanced wireless commu-

nications solutions that provide customers in our target markets with enhanced levels of

reliability, mobility, performance and efficiency in their business operations and mis-

sions. xG’s business lines include the brands of Integrated Microwave Technologies

LLC (“IMT"), Vislink Communication Systems (“Vislink"), and xMax. There is con-

siderable brand interaction, owing to complementary market focus, compatible product

and technology development roadmaps, and solution integration opportunities. In ad-

dition to these brands, xG has a dedicated Federal Sector Group focused on providing

next-generation spectrum sharing solutions to national defense, scientific research and

other federal organizations.

Essendant Inc. (the least equivocating firm)

Essendant Inc. (formerly known as United Stationers, Inc.) is a leading national whole-

sale distributor of workplace items including janitorial, foodservice and breakroom sup-

plies (JanSan), technology products, traditional office products, industrial supplies, cut

sheet paper products, automotive products and office furniture.

Overall, the picture that emerges from these examples is that firms and industries that use more

equivocating language are more likely to involve business operations that require expressing shades

of possibility. In contrast, the firms and industries that use fewer equivocating language are more

likely to have business operations that are certain and unambiguous.

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Although the examples in this section are anecdotes drawn from the extremes of our data, they

are consistent with a more systematic analysis of the data. As evidence on this point, Table 2

presents sample splits and two-sample t-tests for various characteristics available in our data. Con-

sistent with these examples, high-equivocation disclosures tend to come from small and young firms

that are more difficult to quantify. In the following section, we show that the relationships and char-

acteristics that are important in these sample splits are also robust to industry and year fixed effects,

as well as controls for other important factors.


2.3 10-K Disclosures and Empirical Strategy

Before describing our empirical tests, it is important to comment on the meaning of equivocation

within the context of 10-K disclosures relative to other potential source texts. There are two notable

features to discuss: within-firm persistence and the care that goes into 10-K disclosures.

First, regarding within-firm persistence of the measure, it is well known that the 10-K disclo-

sures are highly persistent over time. In light of the persistence of 10-K disclosures, we expect the

extent of equivocation in the 10-K disclosures contains more information about cross-firm differ-

ences in characteristics than time-series changes. At the same time, recent work by Cohen et al.

(2016) has shown that there is information content in the minor changes from year to year that

eventually is capitalized into asset prices. In light of this nature of underlying variation in the 10-K

language, our main specifications that relate equivocation to economic considerations facing firms

use industry fixed effects (to focus on relevant cross-firm variation in equivocation, which comprises

the majority of the variation in our measure). In addition, we also estimate specifications with firm

fixed effects, recognizing that these specifications throw away most of the variation in language

from the 10-Ks. These specifications rely on changes in language from year to year, and thus, map

into similar variation studied by Cohen et al. (2016).

Second, we expect that the equivocation measure based on 10-K disclosures – which are re-

quired by Regulation S-K to include any information with material effects on the firm’s financial

condition or results of operations and carefully curated by the firm’s legal team– is likely different

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than other source texts that do not have the same degrees of difficulty of censoring and ex ante

scrutiny (e.g., the question and answer portion of the earnings call). Because of this high degree of

care in preparing the 10-Ks, equivocating language in the 10-Ks is more deliberate than other source

texts. With this background in mind, we expect our equivocation measure based on 10-K disclo-

sures to contain genuine information that – because of market conditions or timing – is not possible

to make precise at the time of the 10-K disclosure. This information is distinctively useful from the

standpoint of investors in evaluating the likely consequences of adverse conditions facing the firm.

It is important to keep this interpretation in mind when interpreting our tests on how equivocation

relates to product market threats and financial constraints.

3 Main Results

3.1 Relation to Other Textual Measures

Correlations with existing textual measures in the literature help validate our equivocation measure.

Although equivocating language is distinct from uncertainty and weak modal language, it should

be positively related to the use of uncertainty words and weak modal words in 10-Ks. Beyond

the partial overlap in the word dictionary, we expect firms to use equivocation more at times and

in situations in which there is greater uncertainty. For this reason, we anticipate equivocation to

positively associate with uncertainty and weak modal words, which both indicate environments

with greater uncertainty.

We validate this intuition of equivocation using data on uncertainty words, and modal words

from the Loughran and McDonald (2011) master dictionary. Portraying a series of univariate com-

parisons to the use of weasel words, Figure 1 presents side-by-side box plots of the amount of

equivocation in 10-Ks by whether uncertainty, weak modality, and strong modality are above versus

below the median. These plots show that equivocation is more commonly used in high uncertainty,

high weak modality, and low strong modality 10-Ks. Although these relationships are strong, there

is also useful residual variation in equivocation, holding constant other textual measures. The side-

by-side box plots in Figure 1 also show this substantial overlap in the distributions of equivocation

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for high and low uncertainty, weak modality, and strong modality.

[Insert Figure 1 Here]

To examine these associations more systematically, we estimate the following regression speci-

fication:

Equivocationit = α +β1Pct Uncertainit +β2Pct Weak Modalit +β3Pct Strong Modalit +δs + γt +ηX it−1 + εit

where Equivocationit is the percentage of weasel keywords (out of total words) used in firm i’s 10-K

disclosure in year t, Pct Uncertainit , Pct Weak Modalit , and Pct Strong Modalit are percentages of

uncertain words, weak modal words, and strong modal words taken from the Loughran and McDon-

ald (2011) master dictionary, δs are SIC3 industry fixed effects, γt are year fixed effects, and Xit are

control variables including textual measures for sentiment (positive words minus negative words),

interesting words, superfluous words, litigious words, constraining words, and fog words, and in

some specifications, controls for lagged firm characteristics taken from Compustat. To account for

serial correlation over time, the specifications cluster standard errors by firm.4

The multiple regression evidence in Table 3 shows that weak modality and uncertainty are each

positively associated with the use of equivocation, even in a regression controlling for the other tex-

tual measures. In addition, strong modality is negatively associated with equivocation, conditional

on fixed effects and controls for other textual measures. Interestingly, sentiment, measured by the

percentage of positive words minus the percentage of negative words is positively associated with

equivocation, being consistent with the view that firms use imprecise expressions when they discuss

positive prospects in negative situations. As the specifications in columns (2) through (4) show,

these associations are robust to accounting for other available textual measures (the fog measure,

interesting words, superfluous words, litigious words, and constraining words) and firm character-

istics (ROA, Tobin’s Q, Sales growth, R&D/Sales, CAPX/Sales, and Leverage). Furthermore, the

specification in column (5) shows that these associations are robust (in magnitude and significance)

4Variable definitions in detail are given in Appendix Table A.1.

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to including firm fixed effects.5


Taken together, these findings validate that the content of our equivocation measure applied to

the 10-K disclosures captures the underlying idea of equivocation. The evidence here suggests that

– consistent with the motivating idea of weasel words from Wikipedia – equivocation using weasel

language in the 10-Ks captures relatively positive tone with high uncertainty and high modality.

Because these aspects of the text are – to a large degree – part of the content of equivocating

language, we do not typically control for these measures in our main tests. We do, however, show

robustness to controlling for these previously understood aspects of language.

3.2 Relation to Firm Characteristics

Ex ante, equivocation ought to be more frequently used by firms that have more intangible assets,

business models, and those that otherwise difficult to quantify. We examine this intuition by relating

lagged firm characteristics to our equivocation measure.

Figure 2 presents 95% confidence intervals for several notable firm characteristics by each quar-

tile of the distribution of equivocation used in the firm’s 10-K disclosures. Consistent with the

notion that intangible firms are more likely to use equivocation, younger and smaller firms with

more growth opportunities (measured by higher Tobin’s Q and higher sales growth) tend to use

more equivocation. In a similar vein, firms that make more intangible investments, measured by

R&D/Sales, also tend to use equivocation with greater frequency. In addition, firms with lower

profitability tend to use more equivocation.


To examine these associations with firm characteristics more systematically, we estimate the

following regression specification:5We have conducted two additional tests for robustness for this specification. First, we have also conducted the

analysis at the paragraph level, reaching the same conclusions about how equivocation relates to uncertainty and modallanguage. In the paragraph-level analyses, we are able to control for firm-year (i.e., report level) fixed effects, identifyingonly on the variation within 10-K disclosure. Second, beyond normalizing by calculating the percentage of weaselkeywords in our equivocation measure, we have run all of the specifications controlling for the log of the total number ofwords in the report, and the findings are robust.

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Equivocationit = α +β1ROAit−1 +β2Firm Sizeit−1 +β3Tobin′s Qit−1 +δs + γt +ηX it−1 + εit

where Equivocationit is the percentage of weasel keywords (out of total words) used in firm i’s

10-K disclosure in year t, ROAit−1 is the return on assets for firm i in lagged one year (year t−1),

Firm Sizeit−1 is the market valuation of firm i in year t − 1, and Tobin′s Qit−1 is the market to

book ratio of firm i in year t − 1 (included as a measure of growth opportunities anticipated by

market investors), δs are SIC3 industry fixed effects, γt are year fixed effects, and Xit−1 are controls

for lagged firm characteristics taken from Compustat, as well as textual measures taken from the

Loughran and McDonald (2011) master dictionary. To account for serial correlation over time, the

specifications cluster standard errors by firm.

The multiple regression evidence in Table 4 shows broadly that the associations between prof-

itability, intangibility, growth opportunities and the use of equivocation indicated in Figure 2 are

also present in the regression specifications that control for other firm characteristics, industry and

year fixed effects, and the other textual measures present in the literature. Specifically, the estimate

on ROA in column (1) implies that a standard deviation increase in operating profitability is asso-

ciated with nearly a tenth of a standard deviation equivocation used. The association with proxies

for growth opportunities is similar, with a standard deviation increase in Tobin’s Q exhibiting a

coefficient estimate with a nearly identical magnitude.


In addition, columns (3) and (6) present specifications with firm fixed effects, which rely on

within-firm variation in the measure of equivocation (throwing out the majority of the variation,

which tends to be focused across firms). Using this within-firm variation, these specifications paint

a similar picture of the types of firms, though the magnitudes and statistical significance are weaker,

as expected. Although these regressions do not pin down causation precisely, they provide robust

evidence on the types of firms that equivocate and conditions under which firms equivocate. Two

major themes emerge from these results on firm characteristics and equivocation: (1) firms with

14

low profitability use more equivocation, and this finding is not explained by firm size, investment

opportunities, firm lifecycle, nor other aspects of the 10-K language, (2) proxies for intangibility

(small, young firms with R&D investments and high market -to-book ratios) are positively asso-

ciated with equivocation. We consider analogous tests using the MD&A sections of 10-K filings

only and present results in the Appendix Table A.2. Results using entire 10-K texts and the MD&A

section texts are qualitatively similar.

3.3 Product Market Threats and Financial Constraints

Moving beyond the descriptive analysis, we now turn to a more systematic analysis of the incentives

to use equivocation in financial disclosures by relating our equivocation measure to proxies for

product market threats and financial constraints. The analysis in this section is informative for an

important strand of the corporate finance literature that has paid particular attention to how corporate

policies relate to product markets and financial constraints. For example, Phillips (1995) showed

that industries with greater leverage exhibited less aggressive behavior, and Khanna and Tice (2000)

presented evidence that firms with greater leverage retrench upon facing a threat (in the context of

Walmart’s nationwide expansion). More recently, Cookson (2017) showed that leverage constrained

the investment opportunities of casino firms that were facing entry threats, and on the financial side,

Hoberg et al. (2014) showed important changes in payouts in response to an increase in product

market threats.

In this context, we examine how firms’ use of equivocating language changes upon facing

greater product market threats (via product market fluidity in the Hoberg et al. (2014) sense). Rel-

evant to this point, Panel (a) of Figure 3 presents a 95% confidence interval of product market

fluidity for each quartile of equivocation in the 10-K. As equivocation increases, product market

fluidity increases as well, indicating a strong, positive relation.


Related to this notion that product market threats place pressure on firms to increase equivo-

cating language, firms that use more equivocation also tend to face greater financial constraints.

15

Panel (b) shows this positive relation via plots of 95% confidence intervals of the financial con-

straints measure of Hoberg and Maksimovic (2015) by quartiles of our equivocation measure. As

with product market fluidity, there is a strong positive relation between financial constraints and

equivocation. These univariate comparisons on product market threats and financial constraints are

consistent with the view that greater use of equivocation is a consequence of tighter profit margins.

To examine this rationale critically, we estimate the following regression specification:

Equivocationit = α +β1Product Market Fluidityit−1 +β2Financial Constraintsit−1 +δs + γt +ηX it−1 + εit


10-K disclosure in year t, Product Market Fluidityit−1 is the measure of product market fluidity

from Hoberg et al. (2014), Financial Constraintsit−1 is the Hoberg and Maksimovic (2015) text-

based measure of financial constraints (including separate measures for equity constraints and debt

constraints), δs are industry fixed effects, γt are year fixed effects, and Xit−1 are controls for lagged

firm characteristics taken from Compustat, as well as textual measures taken from the Loughran and

McDonald (2011) master dictionary. To account for serial correlation over time, the specifications

cluster standard errors by firm.

The multiple regression evidence in Table 5 indicates that the simple associations between prod-

uct market fluidity, financial constraints, and equivocation indicated in Figure 3 are robust to a more

systematic approach that controls for industry and year fixed effects and firm characteristics. Ac-

cording to the specification in column (1) of Panel (a), a standard deviation increase in product

market fluidity is associated with an increase of approximately one fifth of a standard deviation

in equivocation (an effect size comparable to the association between positive words and weasel

words). This estimated relation is statistically significant at the one percent level, and is robust to

the inclusion of industry (SIC3 or SIC4) and year fixed effects. The magnitude and significance of

this estimated coefficient is also stable and robust to the inclusion of firm characteristics.


16

Similar to product market fluidity, financial constraints are similarly robustly related to equivo-

cation. From the specifications in Panel (a), a standard deviation increase in financial constraints is

associated with approximately one tenth of a standard deviation more equivocation. The specifica-

tions with firm fixed effects in columns (3) and (6) show that these relations are robust in statistical

significance – though slightly smaller in magnitude – when focusing only on the relatively sparse

within-firm variation. We also consider analogous tests using the MD&A sections of 10-K filings

only and present results in the Appendix Table A.3. Results using entire 10-K texts and the MD&A

section texts are qualitatively similar for these tests. As another robustness check, in Table A.4 in

the Appendix, we obtain similar results using the equivocation measure purged of uncertainty and

weak modal words.6

Digging deeper, we follow Hoberg and Maksimovic (2015) and examine how equity constraints

and debt constraints differ in their relation to equivocation. Interestingly, as the results in Panel

(b) of Table 5 show, the positive relation between constraints and equivocation comes from equity

constraints, not debt constraints. As in panel (a), these findings are robust in statistical significance

to using firm fixed effects in specifications (columns 3 and 6) that focus on within-firm changes to

equivocation. The differential response of equivocation to equity financial constraints versus debt

financial constraints suggests that the incentive to disclose equivocating text comes from the poten-

tial effects on equity valuations. This is a natural finding in light of the importance of information

disclosure in the 10-Ks for equity markets relative to debt markets.7

Further, as a contextual validation of these findings, we obtain several examples of the content

of sentences that contain both equivocating language and economic constraints. For example, when

faced by poor economic conditions in 1996-1997, Matson, Inc. disclosed the following in their

6One potential concern with our specification is that we employ a text-based proxy for financial constraints as anexplanatory variable for equivocation, which is also a text-based measure. As these proxies are built from the same10-Ks, it is a natural concern that the measures have a common source of error, which biases the regression estimates.We address this concern by relating our measure of equivocation to non-textual measures of financial constraints in theAppendix (specifically, we examine the SA Index of Hadlock and Pierce (2010) and the Whited and Wu (2006) financialconstraints index). As the results in the Appendix Table A.5 show, we obtain similar insights from this analysis, whichvalidates our use of the textual measure in the main analysis.

7In addition, we have run the analysis based on a re-weighted version of the measure (using text frequency, inversedocument frequency weights) that mirrors the intuition of the Goldsmith-Pinkham et al. (2016) saliency filter. Theestimates we obtain are nearly identical from this approach. Despite this robustness to a more sophisticated methodology,we prefer to use the main measure of equivocation because it is more transparent, and involves fewer researcher choices.

17

10-K:

Sales again will be challenged by adverse economic conditions, but it is reasonable to

anticipate some potential upside, based upon unplanned sales opportunities that may

present themselves, just as several did during 1996.

Another example comes from Claire’s Stores, Inc., which disclosed the following:

Although the Company faces competition from a number of small specialty store chains

and others selling fashion accessories, in addition to one chain of approximately 800

stores, the Company believes that its Fashion Accessory Stores comprise the largest

and most successful chain of specialty retail stores in the World devoted to the sale of

popular-priced women’s fashion accessories.

These examples are consistent with our bottom-line interpretation that product market threats and

financial constraints bring about deteriorating business conditions, while at the same time present-

ing the firm with an opportunity to defer responsibility to a third party. Given that the declining

performance of the firm needs to be disclosed, our evidence underscores the natural incentive to

use equivocating language (weakening tone, non-specific attribution, and overall imprecision) when

these conditions present themselves.

4 Mechanisms and Robustness

4.1 Equity Market Incentives

In this section, we investigate more in depth the role of equity market constraints in driving the

use of equivocation by examining how equivocation depends on the degree to which management

compensation is aligned with equity market performance of the firm’s stock. More specifically,

we consider these management incentives to equivocate by estimating variants on the following

regression specification:

18

Equivocationit = α +β1High Incentivesit−1 +β2High Incentivesit−1×Financial Constraintsit−1

+β3Financial Constraintsit−1 +β4Product_Market_Fluidityit−1 +δs + γt +ηX it−1 + εit


10-K disclosure in year t, High Incentivesit−1 is an indicator variable that equals one if the fraction

of the manager’s compensation that is paid in stock is in the top quartile,8 Product Market Fluidityit−1

is the measure of product market fluidity from Hoberg et al. (2014), Financial Constraintsit−1 is

the Hoberg and Maksimovic (2015) text-based measure of financial constraints (including separate

measures for equity constraints and debt constraints), δs are industry fixed effects, γt are year fixed

effects, and Xit−1 are controls for lagged firm characteristics taken from Compustat, as well as tex-

tual measures taken from the Loughran and McDonald (2011) master dictionary. To account for

serial correlation over time, the specifications cluster standard errors by firm.

In these specifications, the main effect β1 and the interaction β2 are informative of management’s

equity market incentives. In specifications without the interaction terms, β1 measures the degree to

which firm managers with stronger sensitivity to equity markets are more or less prone to using

equivocation. The interaction term informs the degree to which these equity market incentives

interact with the financing constraints measure from the previous section. If the equity market

incentives are important, we should expect β1 to be positive in non-interacted specifications and

β2 to be also positive in interactive specifications. Consistent with this prediction, the univariate

evidence in Figure 4 shows there is generally positive association between equivocation and the

fraction of the firm owned by firm managers. Managers in high-equivocation firms tend to have

much stronger equity market incentives.


The multiple regression evidence in Table 6 shows that there is a significant positive association

8We do not employ firm fixed effects in these specifications because the variation in management incentives is persis-tent (even more so than textual disclosures in the 10-Ks), leaving very little useful variation within firm.

19

between management incentives and the use of equivocation, even after accounting for industry

and year fixed effects, product market fluidity, and financial constraints. The estimated coefficient

on the high incentives dummy is approximately 0.05, which is similar in magnitude to change in

equivocation usage implied by a standard deviation increase in product market fluidity or financial

constraints, and this estimate is statistically significant at the one percent level. Moreover, the

interaction term with financial constraints is also positive and statistically significant. For a standard

deviation above the mean of financial constraints, the interaction magnitude is approximately one

third of the main effect of manager incentives. Beyond the main effect of management incentives

and the main effect of financial constraints, this interaction – which captures the additional response

to high financial constraints by managers with high equity incentives – suggests that managers

increase their usage of equivocation to partially mitigate the costs of high financial constraints.


4.2 Market Reactions to Equivocation

Thus far, our evidence indicates that firms that are performing worse (lower ROA), facing greater

competitive threats in the product market, and facing greater financial constraints tend to use more

equivocation in their 10-K disclosures. In addition, managers with stronger equity market incen-

tives are more likely to use equivocation in firm disclosures, especially when financial constraints

are high. These findings collectively suggest that equivocation can help mitigate the adverse con-

sequences to stock market valuation when a firm faces greater competitive threats and financial

constraints. Thus we now examine return reactions to equivocation in 10-K disclosures.

Specifically, for each 10-K release, we compute the buy and hold abnormal return (BHAR) in

a weekly window (calendar days) and relate these abnormal returns to our equivocation measure in

the following regression specification:

BHARitn =β1Equivocationit +ηXit + εitn

where BHARitn is the buy and hold abnormal return in the window of the nth week after the 10-K

20

release date in year t (following existing studies, the 1st week window starts from the four days

after the 10-K release date), Equivocationit is the percentage of weasel keywords out of the total

used in the 10-K disclosure in year t, and Xit is a vector of controls employed in prior work (e.g.,

Loughran and McDonald, 2011), including the percentage difference between positive words and

negative words, lagged book-to-market, lagged percentage of institutional holdings, pre-filing date

Fama-French alpha, lagged log of share turnover, firm size on the day prior to the 10-K filing date,

and most recent SUE. We estimate this BHAR regression model for each week separately from the

1st through 10th weeks after the 10-K release date. We use clustered standard errors by quarter to

account for cross-sectional correlation of returns.

The coefficient of interest in this specification is β1, which captures the extent to which the

market reacts to equivocation. We expect β1 > 0, and if the positive market reaction is based on

fundamental information, we predict that this effect is not immediate and does not revert afterwards.

The evidence from estimating the BHAR specification in Panel (a) of Table 7 confirms this intuition.

We find a robust and positive estimate of the coefficient on Equivocationit only in the 3rd to 5th

weeks. Although we do not report the results in the weeks after the 7th week to conserve space, we

find the coefficients on Equivocationit in the 6th to 10th weeks are all positive but insignificant. This

indicates that the positive market reactions to equivocation does not lead to reversals. Panel (b) of

Table 7 reports analogous test results over multiple-week BHAR windows to capture the cumulative

market reaction. The positive return effect of equivocation cumulatively emerges from the 3rd week

and does not revert. The economic interpretation of the positive return effect, for example as in

column (7), is that a one standard deviation shift in equivocation is associated with a nearly 0.8%

higher cumulative BHAR through the 7th week after the disclosure.


As an alternative presentation of these results, Figure 5 plots cumulative BHARs following

weeks with 10-K releases by highly versus less equivocating firms. The solid black line shows that

the cumulative returns increase approximately 5% over the 10-week period following highly equiv-

ocating 10-K releases, relative to a 3% increase for less equivocating 10-K releases. The difference

in cumulative returns between high and low equivocating disclosures is estimated as 1.8% at the

21

10th week point after the 10-K release dates. We also confirm in this graphical illustration that the

positive return effect is not temporary and does not lead to a return reversal. The positive and persis-

tent market reaction to equivocation disclosures suggests that equivocation provides fundamentally

valuable information that can help firms with adverse economic conditions in equity valuations, and

the findings deepen our understanding of the greater use of equivocation in reaction to high product

market threats and greater financial constraints.9


4.3 Subsequent Performance Volatility, Market Valuations, and Investment

The fact that the market eventually responds positively to the disclosure of equivocation, and the

market returns do not revert, suggests that there is long-term value signaled in the equivocating

disclosures. We now examine potential reasons for the positive market reactions following equivo-

cating disclosures, and specifically examine subsequent operating performance. If the mechanism

for the positive market reactions is that firms are releasing valuable but immature information dur-

ing bad times, we expect there to be greater volatility in operating performance, as well as better

performance and more investment in the future (conditional on other factors). We first examine the

relation to subsequent operating volatility using the following regression specification:

Yit = α +β1Equivocationit−1 +δs + γt +ηX it−1 + εit

where Yit is the operating volatility measure of interest (standard deviation of operating margin,

profit margin, and ROA using quarterly data for the subsequent 12 quarters) for firm i in year t and

the right-hand side variable of interest Equivocationit−1 is the percentage of weasel keywords (out9We also consider an event study of the market reaction especially to negative shocks and examine whether these

market reactions are dampened by the use of equivocation in the 10-K disclosure. In Appendix Table A.6, we computethe BHAR in a window from 0 to 180 days and relate these abnormal returns to the use of equivocation and the recentstandardized earnings surprise (SUE). We find a negative and significant slope coefficient on the interaction term betweenour equivocation measure and SUE, which opposes the estimated positive and significant main effect of SUE in sign.Examining the split sample results for negative versus positive SUEs, the estimated interaction is only statistically sig-nificant in the negative SUE subsample. These results reinforce our findings that equivocation can help insulate equityvaluations from negative information.

22

of total words) used in firm i’s 10-K disclosure in year t − 1, δs are industry fixed effects, γt are

year fixed effects, and Xit−1 are controls for lagged firm characteristics taken from Compustat. To

account for serial correlation over time, the specifications cluster standard errors by firm.

The multiple regression evidence in Table 8 is consistent with the immature information hy-

pothesis. Namely, we find a robust, significant, and positive relation between current equivocation

and subsequent operating volatility. The findings are robust to the use of firm fixed effects and time-

varying firm characteristics. Although the raw correlation explains roughly 0.1 to 0.2 of a standard

deviation in operating performance (regardless of measure), the estimate conditional on fixed ef-

fects and controls is more modest. Specifically, a standard deviation increase in equivocation is

associated with an increase of 0.025 to 0.048 of a standard deviation in operating volatility.


As a complement to the evidence on operating performance volatility, we now examine the relation

between the use of equivocation and subsequent Tobin’s Q and investment outcomes (both R&D

intensity and capital expenditures). Specifically, we estimate variants on the following regression

specification:

Yit = α +β1Equivocationit−1 +δs + γt +ηX it−1 + εit

where Yit is the corporate outcome of interest (Tobin’s Q, R&D, or capital expenditures) for firm

i in year t and the right-hand side variable of interest Equivocationit−1 is the percentage of weasel

keywords (out of total words) used in firm i’s 10-K disclosure in year t− 1, δs are industry fixed

effects, γt are year fixed effects, and Xit−1 are controls for lagged firm characteristics taken from

Compustat. To account for serial correlation over time, the specifications cluster standard errors by

firm.

The multiple regression evidence in Table 9 is consistent with the event study evidence in that

it shows that firms that use more equivocation today have higher long term valuations as measured

23

by Tobin’s Q. Specifically, a standard deviation increase in equivocation is associated with 0.048

higher Tobin’s Q. In addition, high-equivocation firms tend to also invest more in R&D and capital

expenditures in subsequent years. These estimates control for lagged values of firm characteristics,

as well as industry and year fixed effects. The findings are robust to the use of firm fixed effects

for Tobin’s Q and R&D, but the relation to capital expenditures becomes weaker and statistically

insignificant with firm fixed effects. Although there are alternative interpretations of these findings,

the positive relation of our equivocation measure to market valuations and investment – together

with the previous evidence on market reactions and operating performance volatility – suggests that

there are useful real consequences to equivocating disclosure.


4.4 Robustness to Other Textual Measures

Controlling for other textual measures – as we discussed earlier – is partly over-controlling in the

sense that it holds constant factors that partly define what equivocation using weasel words mean.

From the standpoint of the literature, however, it is useful to show that, our equivocation measure

has content unto itself that is not contained in a recombination of previously understood textual

measures. To evaluate this robustness to using alternative textual measures, we revisit the product

market fluidity and financial constraints results from Table 5, but we control for the full complement

of other textual measures used in the literature (not just sentiment, uncertainty, weak modality, and

strong modality).

The results from this exercise are reported in Table 10. Regardless of the set of controls, we

find that product market fluidity and financial constraints each exhibits a positive and statistically

significant relation with the use of equivocation after controlling for the full suite of other textual

measures. These findings indicate that our equivocation measure contributes useful information

beyond existing textual measures of tone. This conclusion was foreshadowed by the fact that the

equivocation distributions exhibit substantial residual variation beyond related textual measures (see

Figure 1 box plots), but the findings here show that the additional information from our equivocation

measure is economically meaningful, and thus, motivates its use beyond our setting. Further sup-

24

porting this interpretation, we find in column (5) that the relation between equivocation and adverse

market conditions (product market threats and financial constraints) is present even after accounting

for other textual measure, time-varying firm characteristics, and firm fixed effects.


5 Conclusions

In this paper, we introduce a novel textual measure to the finance and accounting literature, which

captures the degree of equivocation in firm disclosures. The measure is distinct from uncertainty,

sentiment, and other textual measures of obfuscation. Also, on its own, our equivocation measure

has significant explanatory power for identifying when the qualitative information in firm disclo-

sures is disconnected from the underlying quantitative information.

We find that firms equivocate when faced with product market threats and financial constraints,

and that the long-term reaction to equivocating disclosure is positive. These findings suggest that

firms use equivocation to convey valuable information that is nonetheless too immature to make

precise. Paradoxically, linguistic imprecision allows firms to make valuable disclosures. In contrast

to other textual measures of uncertainty or obfuscation, we find that equivocation disclosure is

valuable for firms and valued by investors. Collectively, our findings and approach suggest that

there is much to learn from the qualitative content of firm disclosures.

25

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Figure 1: Equivocation: Uncertainty and Modality

Note: This figure presents plots of notable textual measures from the Loughran and McDonald (2011) master dictionaryin comparison to the propensity of firms to use equivocation in their firm disclosures. Each panel presents side-by-sidebox plots of the distribution of equivocation using weasel words by above and below the median of each textual measurefrom Loughran and McDonald (2011). The difference in means in each panel is statistically significant at the 1% level.To help describe the content of the equivocation measure, the textual measures we consider are (a) uncertainty, (b) weakmodal words, and (c) strong modal words.

(a) Uncertainty Words (Loughran and McDonald 2011) (b) Weak Modal Words (Loughran and McDonald 2011)

(c) Strong Modal Words (Loughran and McDonald 2011)

30

Figure 2: Firm Characteristics versus Propensity to Equivocate

Note: This figure presents plots of notable firm characteristics taken from Compustat as they individually relate to thepropensity of firms to use equivocation in their firm disclosures. Each panel presents a 95% confidence interval for thefirm characteristic for firms in the first, second, third, and fourth quartile of the equivocation distribution, respectively. Inthis figure, the firm characteristics we consider are R&D/Sales, ROA, Sales Growth, Firm Age in Years, Firm Size, andTobin’s Q.

(a) ROA (b) Firm Size

(c) Tobin’s Q (d) Firm Age

(e) Sales Growth (f) R&D/Sales

31

Figure 3: Product Market Fluidity and Financial Constraints versus Propensity to Equivocate

Note: This figure presents plots of notable characteristics related to product market threats and financial constraints asthey individual relate to the propensity of firms to use equivocation in their firm disclosures. Each panel presents a 95%confidence interval for the firm characteristic for firms in the first, second, third, and fourth quartile of the equivocationdistribution respectively. Product market threats and financial constraints that we consider are from Hoberg et al., 2014and Hoberg and Maksimovic, 2015, respectively.

(a) Product Market Fluidity (Hoberg-Phillips-Prabhala)

(b) Financial Constraints (Hoberg-Maksimovic)

32

Figure 4: Management Incentives versus Propensity to Equivocate

Note: This figure presents a plot of management stock market incentives as it relates to the propensity of firms to useequivocation in firm disclosures. The plotted ranges are 95% confidence intervals for stock ownership in the first, second,third, and fourth quartile of the equivocation distribution, respectively.

33

Figure 5: Equivocation and Buy and Hold Abnormal Returns

Note: This figure presents plots of cumulative buy and hold abnormal returns (BHARs) over weekly windows after 10-Kfiling dates for high vs low equivocation firms. The cumulative BHARs are measured from the fourth days after the 10-Krelease date until the end of each week. High Equivocation and Low Equivocation are disclosures with above and belowmedian equivocation, respectively.

34

Table 1: Frequently Used and Salient Words in Sentences with Weasel Tags

Note: Panel (a) of the table lists the top 30 most frequently mentioned unigrams, bigrams, and trigrams in the 433 sen-tences that have weasel tags ({{Weasel-inline|{{subst:DATE}}}}) from an Wikipedia dump completed on April 20, 2017.The Wikipedia dump comprises of 17,483,910 articles and is available at https://dumps.wikimedia.org/. To illustrate theinfluence of our saliency screen, Panel (b) of the table presents the top 10 unigrams and the bottom 10 unigrams sortedon the saliency measure of Goldsmith-Pinkham et al. (2016). Panel (c) lists the top 10 most frequently mentioned weaselkeywords, uncertain words, and weak and strong modal words. The uncertainty, weak modality, and strong modalityword lists are from the Loughran and McDonald (2011) master dictionary.

(a) Top 10 Unigrams, Bigrams and Trigrams in Sentences withWeasel Tags

Rank Unigrams Bigrams Trigrams1 the of the one of the2 and in the it has been3 some it is considered by many4 that to be is considered by5 was has been of the most6 many to the is one of7 for for the it can be8 with one of may have been9 has and the according to some10 have that the be one of

(b) Top 10 Unigrams and Bottom 10 Unigrams, Sorted onSaliency

Rank Top 10 Unigrams Bottom 10 Unigrams1 some the2 many and3 although for4 considered was5 may from6 said their7 have new8 argued united9 believed also10 often first

(c) Top 10 Weasel, Uncertain, and Modal Words

Rank Weasel Words Uncertain Words Weak Modal Words Strong Modal Words1 other hidden may will2 may may could must3 clear could possible best4 could approximately might highest5 would risk depend never6 number of intangible uncertain lowest7 can believe depending always8 well assumptions depends clearly9 however risks appears strongly10 various believes appearing undisputed

35

Table 2: Summary and Sample Splits by High versus Low Equivocation

Note: This table presents averages of several notable variables in our analysis, separately by disclosures with belowmedian equivocation (“Low Equivocation”) and disclosures with above median equivocation (“High Equivocation”). Allfirm characteristics are standardized and wisorized at the 1% level. The t-test column reports the two-sample t-test, usingstandard errors that are clustered by firm. * denotes significance at the 10% level, ** at the 5% level, and *** at the 1%level.

Low Equivocation High Equivocation t-statProduct Markets & Financial ConstraintsProduct Market Fluidity 5.8696 7.5492 32.743***Financial Constraints -0.0260 0.0031 20.098***Equity Constraints -0.0323 0.0024 26.132***Debt Constraints 0.0104 -0.0034 -17.53***

Notable Firm CharacteristicsROA 0.1449 -0.2274 -25.483***Tobin’s Q -0.1041 0.1619 19.152***Log Market Value 0.0443 -0.2791 -20.587***Log Age 0.3590 -0.0779 32.141***

Other Textual MeasuresSentiment (Pct Positive - Pct Negative) -0.7489 -0.9405 -29.958***Pct Uncertain 0.8058 1.1599 80.593***Pct Weak Modal 0.3410 0.5462 88.210***Pct Strong Modal 0.2400 0.3185 44.713***Pct Fog 29.8136 29.6796 -2.495**

36

Table 3: Relation of Equivocation to Other Measures of Textual Information

Note: This table presents OLS regressions of equivocation used in 10-K filings on other textual measures. Modal words,uncertainty words, positive words, constraining words, superfluous words, interesting words and litigious words arefrom the Loughran and McDonald (2011) master dictionary. The fog words are the complex words based on the fogmeasure initially proposed by Robert Gunning in 1952, and used extensively in quantifying the lack of plain English (seeHwang and Kim, 2017). Variable definitions are given in Appendix Table A.1. (Z) indicates that the variable has beenstandardized to have mean 0 and standard deviation 1 for ease of interpretation. Standard errors that are clustered by firmare reported in parentheses. * denotes significance at the 10% level, ** at the 5% level, and *** at the 1% level.

(1) (2) (3) (4) (5)Pct Uncertain (Z) 0.179*** 0.184*** 0.172*** 0.170*** 0.176***

(0.004) (0.004) (0.006) (0.006) (0.007)Pct Weak Modal (Z) 0.133*** 0.126*** 0.125*** 0.118*** 0.118***

(0.004) (0.004) (0.004) (0.004) (0.005)Pct Strong Modal (Z) -0.010*** -0.001*** -0.011*** -0.019*** -0.024***

(0.002) (0.002) (0.002) (0.002) (0.002)Sentiment (Z) 0.0010*** 0.041*** 0.041*** 0.042*** 0.044***

(0.002) (0.002) (0.002) (0.002) (0.003)Log(Total Words in 10-K) 0.014*** 0.006*** 0.000 0.020*** 0.007

(0.003) (0.003) (0.004) (0.004) (0.005)Additional ControlsPct Constraining, Pct Litigious no yes yes yes yesPct Superfluous, Pct Interesting, Pct Fog no no yes yes yesLagged Firm Characteristics no no no yes yesObservations 44207 44207 44207 44207 44207Adjusted R2 0.724 0.741 0.745 0.765 0.844Fixed Effects SIC3, year SIC3, year SIC3, year SIC3, year firm, year

37

Table 4: Relation of Equivocation to Firm Characteristics

Note: This table presents OLS regressions of equivocation used in 10-K filings on firm characteristics. Textual measuresare from the Loughran and McDonald (2011) master dictionary. Firm characteristics are taken from Compustat andwinsorized at the 1% level. Variable definitions are given in Appendix Table A.1. (Z) indicates that the variable has beenstandardized to have mean 0 and standard deviation 1 for ease of interpretation. Standard errors that are clustered by firmare reported in parentheses. * denotes significance at the 10% level, ** at the 5% level, and *** at the 1% level.

(1) (2) (3) (4) (5) (6)ROAt−1 (Z) -0.037*** -0.029*** -0.008** -0.028*** -0.028*** -0.005*

(0.003) (0.003) (0.003) (0.002) (0.002) (0.003)Firm Sizet−1 (Z) -0.052*** -0.036*** -0.057*** -0.058*** -0.052*** -0.075***

(0.004) (0.004) (0.011) (0.003) (0.003) (0.008)Tobin’s Qt−1 (Z) 0.045*** 0.027*** 0.020*** 0.031*** 0.025*** 0.020***

(0.003) (0.003) (0.003) (0.002) (0.002) (0.002)Firm Aget−1 (Z) -0.070*** -0.070*** -0.022*** -0.010

(0.003) (0.008) (0.002) (0.007)Sales Growtht−1 (Z) 0.012*** 0.004** 0.005*** 0.002

(0.002) (0.002) (0.001) (0.002)R&D/Salest−1 (Z) 0.019*** 0.005 0.001 0.002

(0.004) (0.004) (0.003) (0.003)CAPX/Salest−1 (Z) 0.004 -0.003 0.004 -0.002

(0.003) (0.003) (0.002) (0.002)Leveraget−1 (Z) -0.026*** -0.011*** -0.010*** -0.006**

(0.003) (0.004) (0.002) (0.003)Log(Total Words in 10-K) 0.000 -0.005*** -0.035*** 0.023*** 0.020*** 0.007

(0.004) (0.004) (0.006) (0.004) (0.004) (0.005)Other Textual Controls no no no yes yes yesObservations 44207 44207 44207 44207 44207 44207Adjusted R2 0.53 0.550 0.682 0.763 0.765 0.843Fixed Effects SIC3, year SIC3, year firm, year SIC3, year SIC3, year firm, year

38

Table 5: Equivocation, Competitive Threats, and Financial Constraints

Note: This table presents OLS regressions of equivocation in the firm’s 10-K on measures of competitive threats, fi-nancial constraints and one year lagged firm characteristics. The product market fluidity measure is taken from Hoberget al. (2014). Financial constraints is the text-based financial constraints measure developed by Hoberg and Maksimovic(2015). Specifications in columns 3 and 4 control for standard firm characteristics taken from Compustat lagged oneyear (firm size, firm age, ROA, Tobin’s Q, sales growth, R&D/Sales, CAPX/Sales, and leverage). Variable definitions aregiven in Appendix Table A.1. (Z) indicates that the variable has been standardized to have mean 0 and standard deviation1 for ease of interpretation. Firm characteristics are all lagged one year and winsorized at the 99% level. Standard errorsthat are clustered by firm are reported in parentheses. * denotes significance at the 10% level, ** at the 5% level, and ***at the 1% level.

(a) Product Market Fluidity and Financial Constraints

(1) (2) (3) (4) (5) (6)Product Market Fluidityt−1 (Z) 0.085*** 0.080*** 0.027*** 0.066*** 0.063*** 0.026***

(0.004) (0.004) (0.005) (0.003) (0.003) (0.005)Financial Constraintst−1 (Z) 0.047*** 0.046*** 0.019*** 0.034*** 0.034*** 0.016***

(0.003) (0.003) (0.003) (0.003) (0.003) (0.003)Log(Total Words in 10-K) -0.039*** -0.039*** -0.036*** -0.012*** -0.013*** -0.035***

(0.004) (0.004) (0.006) (0.004) (0.004) (0.004)Observations 44207 44207 44207 44207 44207 44207Adjusted R2 0.535 0.541 0.679 0.564 0.568 0.683Lagged Firm Characteristics no no no yes yes yesFixed effects SIC3, year SIC4, year firm, year SIC3, year SIC4, year firm, year

(b) Equity and Debt Constraints Separately

(1) (2) (3) (4) (5) (6)Product Market Fluidityt−1 (Z) 0.079*** 0.075*** 0.027*** 0.064*** 0.062*** 0.026***

(0.004) (0.004) (0.005) (0.003) (0.004) (0.005)Equity Constraintst−1 (Z) 0.058*** 0.057*** 0.023*** 0.041*** 0.040*** 0.020***

(0.003) (0.003) (0.003) (0.003) (0.003) (0.003)Debt Constraintst−1 (Z) -0.014*** -0.012*** -0.001 -0.006** -0.005** -0.000

(0.002) (0.002) (0.002) (0.003) (0.002) (0.006)Log(Total Words in 10-K) -0.038*** -0.038*** -0.036*** -0.013*** -0.013*** -0.035***

(0.004) (0.004) (0.006) (0.004) (0.004) (0.006)Observations 44207 44207 44207 44207 44207 44207Adjusted R2 0.540 0.545 0.680 0.566 0.569 0.683Lagged Firm Characteristics no no no yes yes yesFixed effects SIC3, year SIC4, year firm, year SIC3, year SIC4, year firm, year

39

Table 6: Equivocation and Equity Market Ownership

Note: This table presents OLS regressions of equivocation used in 10-K filings on measures of product market threats,financial market constraints, and an indicator for whether the CEO is in the top quartile of fraction of ownership (“HighIncentives”). Firm characteristics are taken from Compustat and winsorized at the 1% level. Variable definitions aregiven in Appendix Table A.1. (Z) indicates that the variable has been standardized to have mean 0 and standard deviation1 for ease of interpretation. Standard errors that are clustered by firm are reported in parentheses. * denotes significanceat the 10% level, ** at the 5% level, and *** at the 1% level.

(1) (2) (3)High Incentives 0.019** 0.021** -0.005

(0.009) (0.009) (0.009)High Incentives × Fin Constraintst−1 (Z) 0.016* 0.016**

(0.009) (0.008)Product Market Fluidityt−1 (Z) 0.071*** 0.070*** 0.056***

(0.006) (0.006) (0.006)Financial Constraintst−1 (Z) 0.038*** 0.034*** 0.029***

(0.004) (0.005) (0.005)Log(Total Words in 10-K) -0.139*** -0.139*** -0.119***

(0.011) (0.011) (0.011)Lagged Firm Characteristics no no yesObservations 16551 16551 16551Adjusted R2 0.601 0.601 0.613Fixed Effects SIC3, year SIC3, year SIC3, year

40

Table 7: Equivocation and Buy and Hold Abnormal Returns (BHARs)

Note: This table presents OLS regressions that regress buy and hold abnormal returns (BHARs) over various weeklywindows after the 10-K filing date on our equivocation measure. The specifications control for the percentage differencebetween positive and negative words, lagged firm size (the log of market capitalization one day prior to the 10-K filingdate), lagged book-to-market, lagged log(share turnover), pre-filing-date Fama-French alpha, lagged percentage of in-stitutinoal holdings, and the most recent SUE. The definitions of these variables are given in Appendix Table A.1. Theclustered standard errors by quarter are employed to account for cross-sectional correlation of BHARs and correspondingt-statistics are reported in parentheses. * denotes significance at the 10% level, ** at the 5% level, and *** at the 1%level.

(a) Weekly BHAR Windows

Week1 Week2 Week3 Week4 Week5 Week6 Week7(1) (2) (3) (4) (5) (6) (7)

Equivocation -0.002 0.002 0.003*** 0.003*** 0.003** 0.002 0.002(-1.34) (0.90) (4.10) (2.65) (2.19) (1.26) (1.47)

Observations 53735 53699 53667 53619 53578 53538 53472Adjusted R2 0.000 0.000 0.000 0.000 0.001 0.000 0.000Other Controls yes yes yes yes yes yes yes

(b) Multiple-week BHAR Windows

Week1 Week1-2 Week1-3 Week1-4 Week1-5 Week1-6 Week1-7(1) (2) (3) (4) (5) (6) (7)

Equivocation -0.001 0.001 0.004** 0.007** 0.009** 0.012** 0.016***(-1.12) (0.81) (2.13) (2.58) (2.50) (2.55) (2.90)

Observations 43768 43786 43792 43792 43793 43793 43793Adjusted R2 0.001 0.001 0.002 0.002 0.003 0.004 0.004Other Controls yes yes yes yes yes yes yes

41

Table 8: Equivocation and Subsequent Operating Volatility

Note: This table presents OLS regressions that relate measures of future firm operating volatility during the subsequent12 quarters to current usage of equivocation in corporate disclosures. Variable definitions are given in Appendix TableA.1. Standard errors that are clustered by firm are reported in parentheses. * denotes significance at the 10% level, ** atthe 5% level, and *** at the 1% level.

SD(Operating Margin) SD(Profit Margin) SD(ROA)(1) (2) (3) (4) (5) (6)

Equivocation (Z) 0.027*** 0.017*** 0.052*** 0.016*** 0.062*** 0.019***(0.007) (0.006) (0.009) (0.005) (0.007) (0.006)

Observations 67935 67935 67935 67935 67935 67935Adjusted R2 0.372 0.684 0.370 0.693 0.442 0.700Date t Firm Characteristics yes yes yes yes yes yesFixed Effects SIC3, year firm, year SIC3, year firm, year SIC3, year firm, year

42

Table 9: Equivocation and Subsequent Valuation and Investment

Note: This table presents OLS regressions that relate subsequent firm valuations (Tobin’s Q) and corporate outcomes tocurrent usage of equivocation in corporate disclosures. Variable definitions are given in Appendix Table A.1. Standarderrors that are clustered by firm are reported in parentheses. * denotes significance at the 10% level, ** at the 5% level,and *** at the 1% level.

Tobin’s Qt+1 R&D/Salest+1 CAPX/Salest+1(1) (2) (3) (4) (5) (6)

Equivocation (Z) 0.048*** 0.033*** 0.025*** 0.010* 0.030*** 0.008(0.007) (0.008) (0.006) (0.006) (0.007) (0.007)

Observations 35213 35213 35072 35072 35072 35072Adjusted R2 0.406 0.568 0.600 0.722 0.427 0.597Date t Firm Characteristics yes yes yes yes yes yesFixed Effects SIC3, year firm, year SIC3, year firm, year SIC3, year firm, year

43

Table 10: Determinants of Equivocation – Accounting for Other Textual Measures

Note: This table presents OLS regressions of differences in the usage of equivocation and modal words over time onlagged differences in product market fluidity (Panel a) and lagged differences in financial constraints (Panel b). Theproduct market fluidity measure is taken from Hoberg et al. (2014). Financial constraints is the text-based financialconstraints measure developed by Hoberg and Maksimovic (2015). Modal words, uncertainty words, positive words,constraining words, superfluous words, interesting words and litigious words are from the Loughran and McDonald(2011) master dictionary. The fog words are the complex words based the fog measure initially proposed by RobertGunning in 1952, and used extensively in quantifying the lack of plain English (see Hwang and Kim, 2017). Variabledefinitions are given in Appendix Table A.1. (Z) indicates that the variable has been standardized to have mean 0 andstandard deviation 1 for ease of interpretation. These specifications do not control for firm characteristics, but resultsare similar for those specifications. Standard errors that are clustered by firm are reported in parentheses. * denotessignificance at the 10% level, ** at the 5% level, and *** at the 1% level.

(1) (2) (3) (4) (5)Product Market Fluidity (Z) 0.023*** 0.022*** 0.023*** 0.022*** 0.008**

(0.003) (0.003) (0.003) (0.003) (0.003)Financial Constraints (Z) 0.018*** 0.018*** 0.013*** 0.013*** 0.008***

(0.002) (0.002) (0.002) (0.002) (0.002)

Notable Textual ControlsPct Uncertainty (Z) 0.172*** 0.171*** 0.166*** 0.166*** 0.174***

(0.005) (0.005) (0.005) (0.005) (0.007)Pct Weak Modal (Z) 0.123*** 0.123*** 0.120*** 0.120*** 0.122***

(0.004) (0.004) (0.004) (0.004) (0.005)Pct Strong Modal (Z) -0.017*** -0.018*** -0.028*** -0.028*** -0.026***

(0.002) (0.002) (0.002) (0.002) (0.002)Sentiment (Z) 0.041*** 0.042*** 0.040*** 0.041*** 0.044***

(0.002) (0.002) (0.002) (0.002) 0.003Controls for Other Textual Measures yes yes yes yes yesLagged firm characteristics no no yes yes yesObservations 44207 44207 44207 44207 44207Adjusted R2 0.757 0.760 0.773 0.775 0.842Fixed Effects SIC3, year SIC4, year SIC3, year SIC4, year firm, year

44

Appendix to:

Some People SayImmature Information in Corporate Disclosures

Table A.1: Variable Definitions

Equivocation is the number of weasel keywords scaled by the total word count in the filing in the per-centage term.

Pct Positive is the number of positive words from the master dictionary by Loughran and McDonald(2011) scaled by the total word count in the filings in the percentage term.

Pct Uncertain is the number of uncertain words from the master dictionary by Loughran and McDonald(2011) scaled by the total word count in the filings in the percentage term.

Pct Modal is the number of modal words from the master dictionary by Loughran and McDonald(2011) scaled by the total word count in the filings in the percentage term.

Pct Constraining is the number of constraining words from the master dictionary by Loughran and McDonald(2011) scaled by the total word count in the filings in the percentage term.

Pct Litigious is the number of litigious words from the master dictionary by Loughran and McDonald(2011) scaled by the total word count in the filings in the percentage term.

Pct Superfluous is the number of superfluous words from the master dictionary by Loughran and McDonald(2011) scaled by the total word count in the filings in the percentage term.

Pct Interesting is the number of interesting words from the master dictionary by Loughran and McDonald(2011) scaled by the total word count in the filings in the percentage term.

Pct Fog is the number of words of three or more syllables that are not hyphenated words or two-syllable verbs made into three with -es and -ed endings, scaled by the total word count inthe filing in the percentage term.

Product Market Fluidity is a measure of the competitive threats faced by a firm in its product market that captureschanges in rival firms’ products relative to the firm, from Hoberg, Phillips and Prabhala(2014).

Financial Constraints is a financial constraints measure from Hoberg and Maksimovic (2015) with higher val-ues indicating that firms are more at risk of delaying their investments due to issues withliquidity.

Equity Constraints is a financial constraint measure from Hoberg and Maksimovic (2015) with higher valuesindicating that the firms are more at risk of delaying their investments due to issues withliquidity and have plans to issue equity in their financial statements.

Debt Constraints is a financial constraint measure from Hoberg and Maksimovic (2015) with higher valuesindicating that the firms are more at risk of delaying their investments due to issues withliquidity and have plans to issue debt in their financial statements.

High Incentives is an indicator for whether the CEO is in the top quartile of fraction of equity ownership.Equity ownership is the number of shares own excluding options by the CEO (shrown exclopts in Execucomp data) divided by total number of shares outstanding.

SUE1 is standardized (by price) earnings surprise based on a rolling seasonal random walk modelin Livnat and Mendenhall (2006).

SUE2 is standardized (by price) earnings surprise based on a rolling seasonal random walk modelin Livnat and Mendenhall (2006) accounting for exclusion of special items.

Firm Size is the log of market value of total assets (market value of common equity plus book valueof preferred stock, long-term and short-term debt, and minority interest).

Firm Age is the log of one plus firm age based on first appearance in Compustat.Tobin’s Q is market value of assets divided by book value of assets.ROA is net operating income divided by total assets in the prior year.Leverage is the ratio of total debt to the market value of assets.CAPX/Sales is capital expenditures divided by sales.R&D/Sales is research and development expenditures divided by sales.Sales Growth is the percentage growth in sales in a given year.

ii

Table A.2: Relation of Equivocation to Firm Characteristics – MD&A Section Only

Note: This table presents OLS regressions of the use of equivocation in the MD&A section of 10-K filings on firmcharacteristics. Textual measures are from the Loughran and McDonald (2011) master dictionary. Firm characteristicsare taken from Compustat and winsorized at the 1% level. Variable definitions are given in Appendix Table A.1. (Z)indicates that the variable has been standardized to have mean 0 and standard deviation 1 for ease of interpretation.Standard errors that are clustered by firm are reported in parentheses. * denotes significance at the 10% level, ** at the5% level, and *** at the 1% level.

(1) (2) (3) (4)ROAt−1 (Z) -0.041*** -0.013* -0.033*** -0.013*

(0.006) (0.007) (0.007) (0.008)Firm Sizet−1 (Z) -0.040*** -0.016 -0.026*** -0.015

(0.010) (0.023) (0.010) (0.023)Tobin’s Qt−1 (Z) 0.038*** 0.023*** 0.021*** 0.016**

(0.006) (0.007) (0.007) (0.008)Firm Aget−1 (Z) -0.060*** -0.096***

(0.008) (0.024)Sales Growtht−1 (Z) 0.005 0.003

(0.004) (0.004)R&D/Salest−1 (Z) 0.023*** 0.014

(0.008) (0.009)CAPX/Salest−1 (Z) 0.003 -0.003

(0.006) (0.007)Leveraget−1 (Z) -0.030*** -0.020**

(0.007) (0.010)Log(Total Words in 10-K) 0.033*** -0.002 0.029*** -0.003

(0.009) (0.008) (0.009) (0.008)Observations 40259 40259 40259 40259Adjusted R2 0.069 0.508 0.077 0.510Fixed Effects SIC3, year firm, year SIC3, year firm, year

iii

Table A.3: Equivocation, Competitive Threats, and Financial Constraints – MD&A Section Only

Note: This table presents OLS regressions of the use of equivocation in the MD&A section of the firm’s 10-K filings onmeasures of competitive threats, financial constraints and one year lagged firm characteristics. The measure of equivo-cation in this table only contains weasel keywords that are distinctive from uncertain and weak modal words from theLoughran and McDonald (2011) master dictionary. Aside from the change in the dependent variable, the specificationsare the same as in Table 5. The product market fluidity measure is taken from Hoberg et al. (2014). Financial constraintsis the text-based financial constraints measure developed by Hoberg and Maksimovic (2015). Specifications in columns3 and 4 control for standard firm characteristics taken from Compustat lagged one year (firm size, firm age, ROA, Tobin’sQ, sales growth, R&D/Sales, CAPX/Sales, and leverage). Variable definitions are given in Appendix Table A.1. (Z)indicates that the variable has been standardized to have mean 0 and standard deviation 1 for ease of interpretation. Firmcharacteristics are all lagged one year and winsorized at the 99% level. Standard errors that are clustered by firm arereported in parentheses. * denotes significance at the 10% level, ** at the 5% level, and *** at the 1% level.


(1) (2) (3) (4)Product Market Fluidityt−1 (Z) 0.043*** 0.036*** 0.022*** 0.017***

(0.009) (0.009) (0.009) (0.009)Financial Constraintst−1 (Z) 0.078*** 0.078*** 0.065*** 0.065***

(0.006) (0.006) (0.006) (0.006)Log(Total Words in 10-K) -0.004 -0.004 0.022*** 0.020***

(0.009) (0.008) (0.008) (0.008)Observations 40259 40259 40259 40259Adjusted R2 0.074 0.088 0.085 0.097Lagged Firm Characteristics no no yes yesFixed effects SIC3, year SIC4, year SIC3, year SIC4, year


(1) (2) (3) (4)Product Market Fluidityt−1 (Z) 0.036*** 0.030*** 0.020** 0.015*

(0.009) (0.008) (0.009) (0.009)Equity Constraintst−1 (Z) 0.090*** 0.090*** 0.073*** 0.074***

(0.007) (0.007) (0.007) (0.007)Debt Constraintst−1 (Z) 0.004 0.007 0.013** 0.015**

(0.006) (0.006) (0.006) (0.006)Log(Total Words in 10-K) -0.004 -0.004 0.021** 0.019***

(0.009) (0.008) (0.008) (0.008)Observations 40259 40259 40259 40259Adjusted R2 0.077 0.090 0.086 0.099Lagged firm characteristics no no yes yesFixed effects SIC3, year SIC4, year SIC3, year SIC4, year

iv

Table A.4: Equivocation, Competitive Threats, and Financial Constraints – Purged Measure

Note: This table presents OLS regressions of the use of equivocation in the firm’s 10-K on measures of competitivethreats, financial constraints and one year lagged firm characteristics. The measure of equivocation in this table onlycontains weasel keywords that are distinctive from uncertain and weak modal words from the Loughran and McDonald(2011) master dictionary. Aside from the change in the dependent variable, the specifications are the same as in Table 5.The product market fluidity measure is taken from Hoberg et al. (2014). Financial constraints is the text-based financialconstraints measure developed by Hoberg and Maksimovic (2015). Specifications in columns 3 and 4 control for standardfirm characteristics taken from Compustat lagged one year (firm size, firm age, ROA, Tobin’s Q, sales growth, R&D/Sales,CAPX/Sales, and leverage). Variable definitions are given in Appendix Table A.1. (Z) indicates that the variable has beenstandardized to have mean 0 and standard deviation 1 for ease of interpretation. Firm characteristics are all lagged oneyear and winsorized at the 99% level. Standard errors that are clustered by firm are reported in parentheses. * denotessignificance at the 10% level, ** at the 5% level, and *** at the 1% level.



(0.002) (0.002) (0.002) (0.002)Financial Constraintst−1 (Z) 0.014*** 0.014*** 0.007*** 0.007***

(0.002) (0.002) (0.002) (0.002)Log(Total Words in 10-K) -0.026*** -0.026*** -0.012*** -0.012***




(0.002) (0.002) (0.002) (0.002)Equity Constraintst−1 (Z) 0.024*** 0.024*** 0.014*** 0.014***

(0.002) (0.002) (0.002) (0.002)Debt Constraintst−1 (Z) -0.011*** -0.011*** -0.006*** -0.006***

(0.001) (0.001) (0.001) (0.001)Log(Total Words in 10-K) -0.026*** -0.025*** -0.012*** -0.012***

(0.002) (0.002) (0.003) (0.003)Observations 44207 44207 44207 44207Adjusted R2 0.098 0.105 0.130 0.136Lagged firm characteristics no no yes yesFixed effects SIC3, year SIC4, year SIC3, year SIC4, year

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Table A.5: Equivocation, Competitive Threats, and Financial Constraints – Size-Age Index andWhited Wu Index

Note: This table presents OLS regressions of the use of equivocation in firm’s 10-K on measures of competitive threats,financial constraints and one year lagged firm characteristics. The measure of equivocation in this table only containsweasel keywords that are distinctive from uncertain and weak modal words from the Loughran and McDonald (2011)master dictionary. Aside from the change in the dependent variable, the specifications are the same as in Table 5. Theproduct market fluidity measure is taken from Hoberg et al. (2014). In contrast to the main specifications, this tablepresents results using two financial constraints indexes that are not textual, found in the literature: the size-age index (SAIndex) of Hadlock and Pierce (2010) and the Whited and Wu (2006) financial constraints index (WW index). Specifica-tions in columns 3 and 4 control for standard firm characteristics taken from Compustat lagged one year (firm size, firmage, ROA, Tobin’s Q, sales growth, R&D/Sales, CAPX/Sales, and leverage). Variable definitions are given in AppendixTable A.1. (Z) indicates that the variable has been standardized to have mean 0 and standard deviation 1 for ease of inter-pretation. Firm characteristics are all lagged one year and winsorized at the 99% level. Standard errors that are clusteredby firm are reported in parentheses. * denotes significance at the 10% level, ** at the 5% level, and *** at the 1% level.


(0.004) (0.009) (0.004) (0.004)SA Index (Z) 0.087*** 0.060***

(0.003) (0.005)WW Index (Z) 0.081*** 0.072***

(0.003) (0.010)Log(Total Words in 10-K) -0.013*** -0.010** -0.010** -0.010***


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Table A.6: Equivocation Mitigates Negative Market Information: Buy and Hold Abnormal Returns

Note: This table presents Fama-Macbeth regressions (using 74 quarters of data from 1997Q4 to 2016Q1) that regressbuy and hold abnormal returns from 0 to 180 trading days after the 10-K filing date on our equivocation measure, themost recent standardized earnings surprise (SUE), and their interaction term. The specifications in odd columns constructthe SUE as in Livnat and Mendenhall (2006) using a rolling seasonal random walk model (SUE1). The specificationsin even columns also account for exclusion of special items (SUE2). The specifications also control for firm size (thelog of market capitalization one day prior to the 10-K filing date), lagged book-to-market, lagged log(share turnover),pre-filing date Fama-French alpha, and the most recent percentage of institutional holdings. Variable definitions aregiven in Appendix Table A.1. Newey-West corrected standard errors that account for serial correlations are reported inparentheses. * denotes significance at the 10% level, ** at the 5% level, and *** at the 1% level.

All 10-Ks Negative SUE Positive SUE(1) (2) (3) (4) (5) (6)

Equivocation × SUE -0.533** -0.893** -0.700** -1.014** -0.432 -0.8288(0.219) (0.364) (0.297) (0.502) (0.357) (0.5324)

SUE 0.800** 1.455** 0.799* 1.256 0.779 1.689*(0.368) (0.641) (0.471) (0.832) (0.578) (0.987)

Equivocation 0.026 0.026 0.029 0.024 0.024 0.031(0.022) (0.022) (0.030) (0.032) (0.020) (0.022)

Observations 39739 39739 15556 15334 24233 24455# of Cross-sections (Quarters) 74 74 74 74 74 74Event-level Controls yes yes yes yes yes yes

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some people say immature information in corporate disclosures · 2020-01-07 · some people say...

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