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” on Wikipedia. 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 and paragraphs) with a high fraction of equivocation tend to exhibit greater uncertainty, and use more words that express shades of possibility, particularly words that weaken (i.e., weak modal words). 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 tend to 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 than otherwise similar firms, and invest more, particularly in intangible investments such as R&D. Collectively, these findings suggest that disclosing immature information can be valuable during bad times.

∗Cookson and Moon are affiliated with the University of Colorado at Boulder’s Leeds School of Business and can be contacted at [email protected] or [email protected]. Noh is affiliated with Case Western Reserve University’s Weatherhead School of Management and can be contacted at [email protected]. The authors are grateful to 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 as Gustaf Bellstam, Asaf Bernstein, Steve Billings, Brendan Daley, Naveen Daniel, Diego Garcia, Dave Ikenberry, Ryan Israelsen, Ralph Walkling, Brian Waters, and Jaime Zender for helpful comments and suggestions. In addition, the authors are grateful to Jerry Hoberg and Bill McDonald for making their textual measures available on their respective websites. All remaining errors are our own. First draft: August 31, 2017. 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.

1 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

2 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

3 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,

4 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.

5 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/.

6 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

Table1 displays the three separate lists of the top 10 most frequently mentioned unigrams, bigrams, and trigrams in our weasel-tagged sentences.

[Insert Table1 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 Table1 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

Table1 presents the top 10 most frequently used words in the 10-Ks using our dictionary of weasel

3In 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 several example phrases in the guidelines are not in the Wikipedia dump we analyze. To maintain the most comprehensive list of weasel keywords, we also include these guideline weasel words in our final list. The complete list of weasel keywords can be obtained by contacting the authors.

7 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-

8 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.

9 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, Table2 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.

[Insert Table2 Here]

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

10 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, Figure1 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 Figure1 also show this substantial overlap in the distributions of equivocation

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

[Insert Figure1 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 + ηXit−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 Table3 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.

12 to including firm fixed effects.5

[Insert Table3 Here]

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.

Figure2 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.

[Insert Figure2 Here]

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 modal language. In the paragraph-level analyses, we are able to control for firm-year (i.e., report level) fixed effects, identifying only on the variation within 10-K disclosure. Second, beyond normalizing by calculating the percentage of weasel keywords in our equivocation measure, we have run all of the specifications controlling for the log of the total number of words in the report, and the findings are robust.

13 0 Equivocationit = α + β1ROAit−1 + β2Firm Sizeit−1 + β3Tobin s Qit−1 + δs + γt + ηXit−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),

0 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 Table4 shows broadly that the associations between prof- itability, intangibility, growth opportunities and the use of equivocation indicated in Figure2 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.

[Insert Table4 Here]

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 Figure3 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.

[Insert Figure3 Here]

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 + ηXit−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, 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 Table5 indicates that the simple associations between prod- uct market fluidity, financial constraints, and equivocation indicated in Figure3 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.

[Insert Table5 Here]

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 Table5 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 an explanatory variable for equivocation, which is also a text-based measure. As these proxies are built from the same 10-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 the Appendix (specifically, we examine the SA Index of Hadlock and Pierce(2010) and the Whited and Wu(2006) financial constraints index). As the results in the Appendix Table A.5 show, we obtain similar insights from this analysis, which validates 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, inverse document frequency weights) that mirrors the intuition of the Goldsmith-Pinkham et al.(2016) saliency filter. The estimates 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 + ηXit−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, High Incentivesit−1 is an indicator variable that equals one if the fraction

8 of the manager’s compensation that is paid in stock is in the top quartile, 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 Figure4 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.

[Insert Figure4 Here]

The multiple regression evidence in Table6 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.

[Insert Table6 Here]

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 Table7 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

Table7 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.

[Insert Table7 Here]

As an alternative presentation of these results, Figure5 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

[Insert Figure5 Here]

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 + ηXit−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 (out

9We 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 compute the BHAR in a window from 0 to 180 days and relate these abnormal returns to the use of equivocation and the recent standardized earnings surprise (SUE). We find a negative and significant slope coefficient on the interaction term between our 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 equity valuations 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 Table8 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.

[Insert Table8 Here]

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 + ηXit−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 Table9 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.

[Insert Table9 Here]

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 Table5, 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

Figure1 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.

[Insert Table 10 Here]

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 References

Agarwal, S., S. Gupta, and R. D. Israelsen (2016). Public and private information: Firm disclosure,

sec letters, and the JOBS Act. Working Paper SSRN 2891089.

Bellstam, G., S. Bhagat, and J. A. Cookson (2017). Innovation in Mature Firms: A Text-Based

Analysis. Working Paper.

Buehlmaier, M. M. M. and T. M. Whited (2017). Are financial constraints priced? Evidence from

Textual Analysis. Working Paper.

Carlin, B. I., S. W. Davies, and A. Iannaccone (2012). Competition, comparative performance and

market transparency. American Economic Journal: Microeconomics 4, 202–237.

Celerier, C. and B. Vallee (2017, August). Catering to investors through security design: Headline

rate and complexity. Quarterly Journal of Economics 132(3), 1469–1508.

Cohen, L., C. Malloy, and Q. H. Nguyen (2016). Lazy prices. Working Paper SSRN 1658471.

Cookson, J. A. (2017, February). Leverage and Strategic Preemption: Lessons from Entry Plans

and Incumbent Investments. Journal of Financial Economics 123(2), 292–312.

Dechow, P. M., R. G. Sloan, and A. P. Sweeney (1996). Causes and consequences of earnings

manipulation: An analysis of firms subject to enforcement actions by the SEC. Contemporary

Accounting Research 13(1).

Dougal, C., J. Engelberg, D. Garcia, and C. A. Parsons (2012, March). Journalists and the stock

market. Review of Financial Studies 25(3), 639–679.

Ely, J. C. (2017). Beeps. American Economic Review 107(1), 31–53.

Ganter, V. and M. Strube (2009). Finding Hedges by Chasing Weasels: Hedge Detection Using

Wikipedia Tags and Shallow Linguistic Features. In Proceedings of the ACL-IJCNLP 2009 Con-

ference Short Papers, Number 2044, pp. 173–176. Association for Computational Linguistics.

26 Gao, J., C. Liang, K. Merkley, and J. Pacelli (2017). Do lenders promote the revelation of bad news?

Evidence from Lender-side loan defaults. Working Paper.

Garcia, D. (2013). Sentiment during recessions. Journal of Finance 68(3), 1267–1300.

Goldsmith-Pinkham, P., B. Hirtle, and D. Lucca (2016). Parsing the content of bank supervision.

Working Paper FRBNY Staff Report No. 770.

Gormley, T. A., B. H. Kim, and X. Martin (2012, March). Do firms adjust their timely loss recog-

nition in response to changes in the banking industry? Journal of Accounting Research 50(1),

159–196.

Hadlock, C. J. and J. R. Pierce (2010). New Evidence on Measuring Financial Constraints: Moving

Beyond the KZ Index. Review of Financial Studies.

Hanley, K. W. and G. Hoberg (2010). The information content of IPO prospectuses. Review of

Financial Studies 23(7), 2821–2864.

Hoberg, G. and C. Lewis (2017, April). Do fraudulent firms produce abnormal disclosure? Journal

of Corporate Finance, Forthcoming.

Hoberg, G. and V. Maksimovic (2015, May). Redefining financial constraints: A text-based analy-

sis. Review of Financial Studies 28(5), 1312–1352.

Hoberg, G. and S. K. Moon (2017, August). Offshore Activities and Financial vs Operational

Hedging. Journal of Financial Economics 125(2), 217–244.

Hoberg, G., G. Phillips, and N. Prabhala (2014). Product market threats, payouts, and financial

flexibility. Journal of Finance 69(1), 293–324.

Huang, A., A. Zang, and R. Zheng (2014, November). Evidence on the information content of text

in analyst reports. The Accounting Review 89(6), 2151–2180.

Hwang, B.-H. and H. H. Kim (2017). It pays to write well. Journal of Financial Economics,

Forthcoming.

27 Jegadeesh, N. and D. Wu (2017). Deciphering Fedspeak: The information content of FOMC meet-

ings. Working Paper SSRN 2939937.

Kamenica, E. and M. Gentzkow (2011). Bayesian Persuasion. American Economic Review 101,

2590–2615.

Khanna, N. and S. Tice (2000). Strategic Responses of Incumbents to New Entry: The Effect of

Ownership Structure, Capital Structure, and Focus. The Review of Financial Studies 13, 749–779.

La Porta, R., F. L. de Silanes, and A. Shleifer (2006). What works in securities laws? Journal of

Finance 61(1), 1–32.

Livnat, J. and R. R. Mendenhall (2006). Comparing the Post-Earnings Announcement Drift for Sur-

prises Calculated from Analyst and Time Series Forecasts. Journal of Accounting Research 44(1),

177–205.

Loughran, T. and B. McDonald (2011). When is a liability not a liability? Textual analysis, dictio-

naries, and 10-Ks. Journal of Finance 66(1), 35–65.

Loughran, T. and B. McDonald (2013, August). IPO first-day returns, offer price revisions, volatil-

ity, and form S-1 language. Journal of Financial Economics 109(2), 307–326.

Phillips, G. M. (1995). Increased Debt and Industry Product Markets: An Empirical Analysis.

Journal of Financial Economics 37, 189–238.

Popadak, J. A. (2013). A corporate culture channel: How increased shareholder governance reduces

firm value. Working Paper SSRN 2345384.

Ross, S. A. (1979). Disclosure Regulation in Financial Markets: Implications of Modern Finance

Theory and Signaling Theory. Issues in Financial Regulation. McGraw-Hill New York.

Verrecchia, R. E. (1983). Discretionary disclosure. Journal of Accounting and Economics 5, 179–

194.

28 Whited, T. M. and G. Wu (2006). Financial Constraints Risk. Review of Financial Studies 19(2),

531–559.

Zingales, L. (2015). Does Finance Benefit Society? Journal of Finance 70(4), 1327–1363.

29 Figure 1: Equivocation: Uncertainty and Modality

Note: This figure presents plots of notable textual measures from the Loughran and McDonald(2011) master dictionary in comparison to the propensity of firms to use equivocation in their firm disclosures. Each panel presents side-by-side box plots of the distribution of equivocation using weasel words by above and below the median of each textual measure from 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) weak modal 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 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 equivocation distribution, respectively. In this figure, the firm characteristics we consider are R&D/Sales, ROA, Sales Growth, Firm Age in Years, Firm Size, and Tobin’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 as they 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 equivocation distribution respectively. Product market threats and financial constraints that we consider are from Hoberg et al., 2014 and 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 use equivocation 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-K filing dates for high vs low equivocation firms. The cumulative BHARs are measured from the fourth days after the 10-K release date until the end of each week. High Equivocation and Low Equivocation are disclosures with above and below median 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 the influence of our saliency screen, Panel (b) of the table presents the top 10 unigrams and the bottom 10 unigrams sorted on the saliency measure of Goldsmith-Pinkham et al.(2016). Panel (c) lists the top 10 most frequently mentioned weasel keywords, uncertain words, and weak and strong modal words. The uncertainty, weak modality, and strong modality word lists are from the Loughran and McDonald(2011) master dictionary.

(a) Top 10 Unigrams, Bigrams and Trigrams in Sentences with Weasel Tags Rank Unigrams Bigrams Trigrams 1 the of the one of the 2 and in the it has been 3 some it is considered by many 4 that to be is considered by 5 was has been of the most 6 many to the is one of 7 for for the it can be 8 with one of may have been 9 has and the according to some 10 have that the be one of

(b) Top 10 Unigrams and Bottom 10 Unigrams, Sorted on Saliency Rank Top 10 Unigrams Bottom 10 Unigrams 1 some the 2 many and 3 although for 4 considered was 5 may from 6 said their 7 have new 8 argued united 9 believed also 10 often first

(c) Top 10 Weasel, Uncertain, and Modal Words Rank Weasel Words Uncertain Words Weak Modal Words Strong Modal Words 1 other hidden may will 2 may may could must 3 clear could possible best 4 could approximately might highest 5 would risk depend never 6 number of intangible uncertain lowest 7 can believe depending always 8 well assumptions depends clearly 9 however risks35 appears strongly 10 various believes appearing undisputed 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 below median equivocation (“Low Equivocation”) and disclosures with above median equivocation (“High Equivocation”). All firm characteristics are standardized and wisorized at the 1% level. The t-test column reports the two-sample t-test, using standard 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-stat Product Markets & Financial Constraints Product 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 Characteristics ROA 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 Measures Sentiment (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 are from the Loughran and McDonald(2011) master dictionary. The fog words are the complex words based on the fog measure initially proposed by Robert Gunning in 1952, and used extensively in quantifying the lack of plain English (see Hwang and Kim, 2017). 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 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 Controls Pct Constraining, Pct Litigious no yes yes yes yes Pct Superfluous, Pct Interesting, Pct Fog no no yes yes yes Lagged Firm Characteristics no no no yes yes Observations 44207 44207 44207 44207 44207 Adjusted R2 0.724 0.741 0.745 0.765 0.844 Fixed 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 measures are from the Loughran and McDonald(2011) master dictionary. Firm characteristics are 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 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 yes Observations 44207 44207 44207 44207 44207 44207 Adjusted R2 0.53 0.550 0.682 0.763 0.765 0.843 Fixed 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 Hoberg et 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 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 been standardized to have mean 0 and standard deviation 1 for ease of interpretation. Firm characteristics are all lagged one year and winsorized at the 99% level. Standard errors that 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 44207 Adjusted R2 0.535 0.541 0.679 0.564 0.568 0.683 Lagged Firm Characteristics no no no yes yes yes Fixed 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 44207 Adjusted R2 0.540 0.545 0.680 0.566 0.569 0.683 Lagged Firm Characteristics no no no yes yes yes Fixed 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 (“High Incentives”). Firm characteristics are 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 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 yes Observations 16551 16551 16551 Adjusted R2 0.601 0.601 0.613 Fixed 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 weekly windows after the 10-K filing date on our equivocation measure. The specifications control for the percentage difference between positive and negative words, lagged firm size (the log 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, lagged percentage of in- stitutinoal holdings, and the most recent SUE. The definitions of these variables are given in Appendix Table A.1. The clustered standard errors by quarter are employed to account for cross-sectional correlation of BHARs and corresponding t-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 53472 Adjusted R2 0.000 0.000 0.000 0.000 0.001 0.000 0.000 Other 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 43793 Adjusted R2 0.001 0.001 0.002 0.002 0.003 0.004 0.004 Other 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 subsequent 12 quarters to current usage of equivocation in corporate disclosures. Variable definitions are given in Appendix Table A.1. Standard errors that are clustered by firm are reported in parentheses. * denotes significance at the 10% level, ** at the 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 67935 Adjusted R2 0.372 0.684 0.370 0.693 0.442 0.700 Date t Firm Characteristics yes yes yes yes yes yes Fixed 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 to current usage of equivocation in corporate disclosures. Variable definitions are given in Appendix Table A.1. Standard errors 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 35072 Adjusted R2 0.406 0.568 0.600 0.722 0.427 0.597 Date t Firm Characteristics yes yes yes yes yes yes Fixed 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 on lagged differences in product market fluidity (Panel a) and lagged differences in financial constraints (Panel b). The product market fluidity measure is taken from Hoberg et al.(2014). Financial constraints is the text-based financial constraints 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 Robert Gunning in 1952, and used extensively in quantifying the lack of plain English (see Hwang and Kim, 2017). 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. These specifications do not control for firm characteristics, but results are similar for those specifications. Standard errors that are clustered by firm are reported in parentheses. * denotes significance 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 Controls Pct 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) 0.003 Controls for Other Textual Measures yes yes yes yes yes Lagged firm characteristics no no yes yes yes Observations 44207 44207 44207 44207 44207 Adjusted R2 0.757 0.760 0.773 0.775 0.842 Fixed Effects SIC3, year SIC4, year SIC3, year SIC4, year firm, year

44 Appendix to:

Some People Say Immature 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 in the filing in the percentage term. Product Market Fluidity is a measure of the competitive threats faced by a firm in its product market that captures changes 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 with liquidity. Equity Constraints is a financial constraint measure from Hoberg and Maksimovic (2015) with higher values indicating that the firms are more at risk of delaying their investments due to issues with liquidity and have plans to issue equity in their financial statements. Debt Constraints is a financial constraint measure from Hoberg and Maksimovic (2015) with higher values indicating that the firms are more at risk of delaying their investments due to issues with liquidity 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 excl opts in Execucomp data) divided by total number of shares outstanding. SUE1 is standardized (by price) earnings surprise based on a rolling seasonal random walk model in Livnat and Mendenhall (2006). SUE2 is standardized (by price) earnings surprise based on a rolling seasonal random walk model in 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 value of 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 firm characteristics. Textual measures are from the Loughran and McDonald(2011) master dictionary. Firm characteristics are 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 the 5% 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 40259 Adjusted R2 0.069 0.508 0.077 0.510 Fixed 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 on measures 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 the Loughran and McDonald(2011) master dictionary. Aside from the change in the dependent variable, the specifications are the same as in Table5. The product market fluidity measure is taken from Hoberg et 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 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 been standardized to have mean 0 and standard deviation 1 for ease of interpretation. Firm characteristics are all lagged one year and winsorized at the 99% level. Standard errors that 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)

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 40259 Adjusted R2 0.074 0.088 0.085 0.097 Lagged Firm Characteristics no no yes yes Fixed effects SIC3, year SIC4, year SIC3, year SIC4, year

(b) Equity and Debt Constraints Separately (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 40259 Adjusted R2 0.077 0.090 0.086 0.099 Lagged firm characteristics no no yes yes Fixed 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 competitive threats, financial constraints and one year lagged firm characteristics. The measure of equivocation in this table only contains 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 Table5. The product market fluidity measure is taken from Hoberg et 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 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 been standardized to have mean 0 and standard deviation 1 for ease of interpretation. Firm characteristics are all lagged one year and winsorized at the 99% level. Standard errors that 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)

Product Market Fluidityt−1 (Z) 0.017*** 0.015*** 0.009*** 0.009*** (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) (0.003) (0.003) Observations 44207 44207 44207 44207 Adjusted R2 0.087 0.096 0.003 0.133 Lagged Firm Characteristics no no yes yes Fixed effects SIC3, year SIC4, year SIC3, year SIC4, year

(b) Equity and Debt Constraints Separately (1) (2) (3) (4)

Product Market Fluidityt−1 (Z) 0.013*** 0.012*** 0.007*** 0.007*** (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 44207 Adjusted R2 0.098 0.105 0.130 0.136 Lagged firm characteristics no no yes yes Fixed effects SIC3, year SIC4, year SIC3, year SIC4, year

v Table A.5: Equivocation, Competitive Threats, and Financial Constraints – Size-Age Index and Whited 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 contains 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 Table5. The product market fluidity measure is taken from Hoberg et al.(2014). In contrast to the main specifications, this table presents results using two financial constraints indexes that are not textual, found in the literature: the size-age index (SA Index) 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, 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 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 clustered by firm are reported 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) 0.087*** 0.092*** 0.080*** 0.083*** (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*** (0.004) (0.004) (0.004) (0.004) Observations 44207 44207 44207 44207 Adjusted R2 0.548 0.545 0.555 0.552 Lagged Firm Characteristics no no yes yes Fixed effects SIC3, year SIC3, year SIC3, year SIC3, year

vi 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 regress buy and hold abnormal returns from 0 to 180 trading days after the 10-K filing date on our equivocation measure, the most recent standardized earnings surprise (SUE), and their interaction term. The specifications in odd columns construct the SUE as in Livnat and Mendenhall(2006) using a rolling seasonal random walk model (SUE1). The specifications in even columns also account for exclusion of special items (SUE2). The specifications also control for firm size (the log 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 are given in Appendix Table A.1. Newey-West corrected standard errors that account for serial correlations are reported in parentheses. * 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 74 Event-level Controls yes yes yes yes yes yes

vii