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Some People Say Immature Information in Corporate Disclosures Table A.1: Variable Definitions

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Some People Say Immature Information in Corporate Disclosures Table A.1: Variable Definitions 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
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