Understanding the Role of Language in Management ... - Insead

Understanding the Role of Language in Management Forecast Press Releases

_______________ Stephen BAGINSKI Elizabeth DEMERS Chong WANG Julia YU 2011/28/AC

Understanding the Role of Language in Management Forecast Press Releases

Stephen Baginski* Elizabeth Demers** Chong Wang*** Julia Yu****

January 31, 2011

*We thank Ben Ayers, John Hassell, Eric Yeung and seminar participants at ESSEC for helpful comments. We appreciate the programming assistance provided by Rajiv Jayaraman of Knolscape as well as Rashid Ansari. We thank Mary Boldrini, Pascale Gadroy, and Cécile Maciulis for their research assistance, and Craig Carroll for processing our text files using the Diction software. Demers is grateful for the research funding provided by the INSEAD Alumni Fund. 

*

Miller Professor, J. M. Tull School of Accounting at Terry College of Business, University of Georgia, 255 Brooks Hall, Athens, Georgia 30602-6252, USA. Email: [email protected]

**

Assistant Professor of Accounting and Control at INSEAD, Boulevard de Constance 77305 Fontainebleau Cedex, France. Email: [email protected]

***

Assistant Professor, Graduate School of Business and Public Policy at Naval Postgraduate School, Monterey, California 93943, USA. Email: [email protected]

****

PhD Candidate in Accounting at J. M. Tull School of Accounting at Terry College of Business, University of Georgia, 255 Brooks Hall, Athens, Georgia 30602-6252, USA. Email: [email protected] du

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Abstract Using a sample of 2,254 voluntarily-provided “unbundled” management earnings forecast press releases, we investigate the role of linguistic sentiment and linguistic certainty in pricing. We provide evidence that sentiment in the forecast setting is directionally consistent with the simultaneously issued hard earnings forecast and plays a significantly greater role in the context of unstructured managerial forecasts than in the earnings announcement setting. We document that sentiment is less important when historical earnings are more informative for valuation, and that the pricing of sentiment varies with forecast specific characteristics of the accompanying hard news that are unique to the management forecast setting (forecast rounding and timeliness). We further document that, similar to the pricing of earnings, the pricing of sentiment is attenuated in the cross-section by a stronger pre-disclosure information environment, differential (s-curve) pricing of larger absolute magnitudes of sentiment, and by higher capitalization rates. Investigating sentiment’s relation with the second moment of returns, we find that negative sentiment is associated with higher stock return volatility and greater dispersion in analyst forecasts, incremental to the magnitude of the sentiment and hard news surprise, and we find some evidence that more certain language is associated with lower idiosyncratic volatility and a reduction in dispersion. Finally, we find that negative sentiment is delayed, incremental to the delay of hard forecast bad news that has been documented in prior research. JEL Classifications:

G14; D82; M41

Keywords: Management forecasts; Soft information; Linguistic sentiment; Linguistic certainty; Voluntary disclosure

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I. Introduction A growing body of literature seeks to understand the market price relevance of language contained in corporate filings, incremental to the simultaneously released quantitative or “hard” news. To our knowledge, extant research on the price relevance of management’s linguistic sentiment and linguistic certainty is limited to the context of mandatory filings, and prior studies consider primarily mean effects rather than the cross-sectional conditions under which the price response to language may be attenuated or amplified.1 We contribute to the literature by examining the role of language in the largely unstructured, voluntary disclosure context of management forecast press releases. Our evidence suggests that managerial sentiment (i.e., net positive words) in unbundled management forecast press releases is directionally consistent with the simultaneously issued hard (i.e., quantitative) earnings forecast, and thus that it is unlike other elements of managerial communication that disproportionately conflict when issued in combination. Linguistic sentiment plays a more significant role, while linguistic certainty is less price-relevant, in the context of voluntarily issued, flexible format managerial forecasts as compared to their respective roles when accompanying the more structured, mandatory earnings announcements. We exploit data attributes that are unique to management earnings forecasts (e.g., the provision of hard information in point form versus ranges of various widths, rounding, and timeliness) to provide evidence that language is differentially priced in the cross-section depending upon the characteristics of the accompanying hard information. Consistent with the notion that language is price-relevant via its link to earnings, we also document that the price response to sentiment is attenuated by factors that affect the mapping of earnings into prices. Finally, we find that negative sentiment affects stock price volatility and analyst dispersion, and that negative sentiment is delayed, incremental to previously documented hard news delay eventually released in management forecasts (Kothari, Wysocki and Shu (2009)).

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Prior financial linguistic studies have examined the price relevance of soft information in the context of mandatory filings such as earnings announcements (Davis, Piger and Sedor (2010); Demers and Vega (2010)), restatement announcements (Mangen and Durnev (2010)), IPO prospectuses (Balakrishnan and Bartov (2010)), and MD&A and other elements of the 10K reports (Davis and Tama-Sweet (2010); Feldman, Govindaraj, Livnat and Segal (2010); Li (2010)).

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Management forecast press releases are fundamentally different from mandatory filings that contain relatively well understood, more clearly defined, lower noise earnings or other (e.g., restatement) information. In the voluntary forecast setting, managers convey earnings expectations that are inherently less reliable than announcements of earnings realizations, and managers have many quantitative options available to express this uncertainty in their forecasts. Furthermore, because of the potential for unforeseen events to arise during the intervening period between the forecast date and the earnings realization date, it may be difficult or impossible to ever discern whether managers had truthfully revealed their expectations with the forecast. In addition to the greater noise in management forecasts, the sign of forecast news disproportionately conflicts with accompanying hard news (e.g., Waymire (1984); Rogers and VanBuskirk (2009)), while earnings realizations and linguistic sentiment are generally directionally consistent (Demers and Vega (2010). The difference in the amount of noise and the inconsistency in hard and soft signal correlations across settings raise questions about whether the relative independence between the hard and soft signals that has been assumed in prior linguistic studies involving mandatory filings extends to the more flexible voluntary forecast setting. Unique to the management forecast setting is the issue of the extent of dependence in the noise in language and the substantial noise in forecast news and its consequence for the pricing of language. Thus, the findings from prior financial linguistic studies do not explain the potential role of linguistic sentiment and uncertainty in the context of highly flexible, voluntary forecast disclosures. Using a Factiva search, we identify a sample of 2,254 voluntarily-provided “unbundled”2 management earnings forecast press releases during the 1997-2006 period from which we develop a database of linguistic parameters that we match with traditional (i.e., non-linguistic) forecast characteristics (e.g., point versus range, the numerical estimate, quarterly versus annual periodicity, etc.) from the First Call Management Issued Guidance database. We derive linguistic measures of sentiment and certainty, the “first and second moments” of the language distribution, respectively, from the Factiva-sourced text documents using the finance-oriented dictionaries of Loughran and

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Following the literature, we define “unbundled” management forecasts as those that are issued in isolation (i.e., not “bundled” with quarterly or annual earnings announcements).

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McDonald (2010).3 Aside from the uniqueness of the management forecast setting in its own right, management forecast press releases arguably provide a less confounded and thus more powerful setting in which to detect language effects for at least two reasons. First, because management forecasts are often issued in relative isolation (or one can sample them, as we have, to obtain this quality), the incremental effect of language and cross-sectional differences in that effect are detected with greater precision, and the interpretation of the quasi-experiment suffers far less from the potential correlated omitted variables problem. Second, the voluntary nature of, and costs associated with, public forecast disclosure increase the likelihood that information conveyed, both numerical and linguistic, is intended to change investor beliefs rather than to merely accompany mandated disclosures. We first document a positive correlation between sentiment and the unexpected earnings conveyed by the hard forecast, suggesting that managerial language is being used, on average, to reinforce, rather than to overturn, the information contained in the hard forecasts.

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announcement period and post-announcement drift returns regressions yield results that are consistent with this relation; sentiment is incrementally significant, in a directionally similar way, to the hard forecast surprise. This finding, when considered jointly with Demers and Vega’s (2010) similar finding of a positive correlation in the earnings press release setting, suggests that the incentives driving sentiment conveyance do not appear to be the same as the incentives that drive the strategic bundling of negatively correlated news discovered by Waymire (1984) (disproportionately more good news disclosures with bad news management forecasts) and Rogers and VanBuskirk (2009) (disproportionately more good news management forecasts with bad news earnings). Demers and Vega (2010) argue that linguistic sentiment is "cheap talk" (i.e., costless to convey, difficult to verify even ex post), but they document return and return volatility effects related to linguistic sentiment in earnings press releases. In the earnings release context, linguistic sentiment accompanies, and often refers to, a mandatory, precise, reasonably reliable, and generally wellunderstood earnings figure. Linguistic sentiment in management forecasts, on the other hand, while 3

We also rerun all of our tests using the Diction 6.0 algorithm adopted by some prior accounting studies, and we report instances where the results differ using Diction’s less powerful (in the financial context) measure of net optimism and/or more powerful measure of linguistic certainty.

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still cheap talk, is released with a voluntary, often imprecise, unaudited number, representing a forecast rather than a realization, and yielding ex-post management forecast errors which may be caused by post-forecast events rather than by non-credible disclosure of underlying unobservable management beliefs. We document that linguistic information plays a much more significant role in the context of these unbundled management forecasts than in the earnings announcement setting, consistent with the notion that investors do not view the lower perceived reliability of the accompanying hard forecast information (relative to realized earnings) as indicative of less reliable soft linguistic information. Rather, the soft linguistic data seems to complement the noisy hard forecast information. To fill a void in the literature relating to the cross-sectional determinants of linguistic sentiment’s price relevance, we pursue the notions that the relevance of historical earnings and the characteristics of release-specific quantitative forecast information modify the market’s response to linguistic sentiment information along several dimensions. First, we hypothesize and find the intuitive result that linguistic sentiment has a smaller effect on prices when earnings are historically more informative. Second, we exploit the availability of announcement characteristics that are unique to our setting to document that the market’s response to the sentiment accompanying rounded forecasts is attenuated. Thus, management’s provision of imprecise forecasts seems to taint the credibility of the accompanying soft information. We find that another characteristic of forecast press releases, timeliness (as measured by forecast horizon), magnifies the price reaction to sentiment. Finally, similar to cross-sectional differences in the mapping of earnings into prices, we document that a stronger pre-disclosure information environment attenuates the pricing of sentiment, larger absolute magnitudes of sentiment have less of an effect on prices (the s-curve effect), and sentiment’s pricing effect is attenuated by higher capitalization rates (the book-to-market effect). In contrast to the generally stronger role expected of sentiment in the management forecast setting relative to the context of earnings announcements or other mandatory disclosure situations, we predict weaker results for linguistic certainty given the many numerical parameters available to management to express this construct (e.g., by providing range rather than point estimates, wider versus narrower ranges, rounded forecasts, etc.). Linguistic certainty may compete with these alternative uncertainty

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measures derived directly from the hard earnings disclosure, and therefore may play far less of a role in the forecast press release context. Even if linguistic certainty does not compete with the alternative uncertainty measures, it may be a signal of the reliability of the quantitative forecast rather than linguistic sentiment, again yielding a diminished role for linguistic sentiment. In other words, an association between the first and second moment of stock returns and linguistic certainty in a management forecast release is not implied by the finding of similar associations in other contexts where the form of hard disclosure is regulated. Consistent with these arguments, we find a much reduced role for the linguistic certainty parameter in the context of forecasts relative to that which has been documented in mandated disclosure contexts. In extended tests examining the role of sentiment on the second moment of stock returns, we find that the magnitude and the negative sign of sentiment, as well as linguistic certainty, are each incrementally associated with announcement return volatility and help to predict post-announcement return volatility over and above the hard forecast news (Rogers, Skinner, and VanBuskirk 2009) and other descriptors of expected return volatility. Further, changes in analyst disagreement following the forecast announcement are increasing in both the bad hard forecast news and (incrementally) in negative sentiment. Finally, we document a negative sentiment delay that is incremental to the bad forecast news delay documented by Kothari, Li, and Short (2009), as evidenced by incrementally higher returns associated with negative sentiment relative to positive sentiment. In summary, we document that linguistic sentiment in unbundled management forecast press releases is a major contributor to the market’s response to a management forecast announcement. We find that sentiment generally confirms, rather than mitigates, simultaneously released hard news. Negative sentiment is associated with higher absolute returns, greater volatility, and greater analyst disagreement. All of these effects are incremental to past research findings on the same phenomenon for hard bad news management forecasts. The strength of the linguistic sentiment effect is heavily influenced by the quality of the accompanying hard information and by traditional determinants of the mapping of earnings into prices. Linguistic uncertainty, while a general measure of uncertainty (i.e., correlated with return volatility), is not a conditioning factor for the market response to linguistic

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sentiment in a management forecast context where alternative expressions of the precision of the hard forecast news are available to management. In Section II, we develop our hypotheses about the association of language and prices. In Section III, we describe our sample and language data. We present our empirical design and results in Section IV and conclude the paper in Section V.

II. Theory and Hypotheses The Noise in Linguistic Sentiment and Management Forecasts The informativeness of language for establishing stock prices is plausible if language is a sufficiently reliable signal of price-relevant information. Unlike a current-period, explicit earnings forecast, which is quasi-verifiable and linked theoretically to prices through expected future earnings and dividends, language is relatively costless to provide, difficult to verify, and linked to future earnings and dividends in a fairly noisy way. This lack of verifiability, in particular, calls the informativeness of the “cheap talk” soft information into question (Crawford and Sobel (1982), Benabou and Laroque (1992), Dye and Sridhar (2004), Demers and Vega (2010)). Although more ex post verifiable than language, the verifiability of hard management forecast news is nevertheless still difficult for several reasons. First, management forecasts can be expressed in imprecise terms, such as in ranges, in minimums or maximums, or rounded to the nearest nickel. Although such less precise forecasts have been shown to be less informative (e.g., Baginski, Conrad and Hassell (1993)) or to create uncertainty (Hirst, Koonce and Miller (1999)), the market still responds as though the forecasts contain price-relevant information.

Second, the potential for

unforeseen events between the forecast date and the realization of earnings confounds the ability of market participants to assess whether managers had truthfully revealed their expectations. Third, management discretion over accruals at the subsequent earnings release also confounds verifiability. Notwithstanding these considerations, research into the information content of management forecasts has long held that institutional arrangements (e.g., legal liability, the existence of information intermediaries) and reputational consequences create incentives for credible management forecasting

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(King, Pownall and Waymire (1990)), and numerous studies document the information content of hard management forecasts, both precise and imprecise.4 In summary, the management forecast press release contains two potentially relevant, but noisy, signals. In the sections that follow, we consider how the greater noise in the forecast setting might affect the pricing of sentiment under conditions in which the two signals are independent and in the event that noise in the hard signal and noise in the soft signal are interrelated.

Incremental Relevance of Linguistic Sentiment To confirm the basic result in the prior financial linguistics literature that linguistic sentiment is incrementally price relevant and to contribute to the management forecast literature by documenting an element in a management forecast press release that is priced incremental to the forecast itself, we begin by testing the following hypothesis as a baseline case:

H1: Price response to a management forecast press release is positively associated with the linguistic sentiment expressed in the press release (incremental to hard forecast news).

Although evidence consistent with this baseline hypothesis confirms that linguistic sentiment is also useful to investors in a voluntary disclosure setting, equal information content of linguistic sentiment across settings is not implied by prior research findings in which the hard information conveyed is highly reliable. In a management forecast press release, the quantitative forecast is less precise, less verifiable, and thus, less reliable relative to a realization conveyed in an earnings press release.

In the management forecast setting, two noisy signals are voluntarily released, the

quantitative management forecast and linguistic sentiment. In a standard Bayesian model with two independent, noisy signals, an increase in one signal’s noise leads to a greater reliance on the other signal. Thus, holding constant the reliability of linguistic sentiment at its already lower level relative to quantitative information, we would expect the effects of linguistic sentiment to be stronger in the context of unbundled management forecasts relative to other settings such as bundled or unbundled 4

Hirst, Koonce and Venkataraman (2008) provide an excellent review of this literature.

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earnings announcements. However, when moving from the mandatory settings in prior research to a voluntary setting where management incentives play a greater role and regulation plays less of a role, the amount of noise in the two signals could be correlated. For example, the noise in linguistic sentiment could also increase if managers have incentives to bias all of the information, both hard and soft, in the management forecast press release. The uniqueness of the pure voluntary disclosure setting causes the directionality of the association to be unclear, and it motivates an empirical analysis of the following hypothesis:

H2: The incremental information content of linguistic sentiment when issued with unbundled management forecasts differs from its incremental information content when it is issued in conjunction with earnings announcements.

Conditioning Effects on the Pricing of Linguistic Sentiment: Earnings Relevance We predict several conditioning effects on the pricing of linguistic sentiment in management forecast press releases. First, we predict that the reliance on linguistic sentiment is associated with the historical informativeness of earnings:

H3A: The market response to linguistic sentiment is associated with the historical informativeness of earnings realizations. H3A is related to, but distinct from, H2. In H2, the idea is that linguistic sentiment’s pricing role is dependent on the reliability of the accompanying disclosure, which varies due to the change in disclosure setting. H3A predicts a further cross-sectional effect within the setting of management forecast press releases. On the one hand, linguistic sentiment competes with a hard management earnings forecast that directly conveys an earnings number which is, historically, more relevant (i.e., has a stronger historical relation to security prices), thus diminishing the role of sentiment in pricing. On the other hand, to the extent that linguistic sentiment is a predictor of earnings (Davis, Piger and

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Sedor (2010)), and earnings are more relevant, the usefulness of linguistic sentiment may be amplified. 5

Conditioning Effects on the Pricing of Linguistic Sentiment: Management Forecast Characteristics Next, we consider the value relevance implications for linguistic sentiment of several hard earnings characteristics that are unique to the management forecast setting: the use of wide-range management forecasts, the rounding of management forecasts, and the forecast horizon.

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forecast characteristics are, at least partially, indicators of time-period specific uncertainty surrounding expected earnings. For example, management forecasts conveyed in wide ranges (Baginski, Conrad and Hassell (1993); Baginski, Hassell and Wieland (2010)) or that are rounded (Bamber, Hui and Yeung (2010)) reflect less precise earnings forecasts relative to narrow range, unrounded forecasts. Standard Bayesian learning models predict that belief revisions pursuant to a signal are decreasing in the imprecision of the signal (e.g., Kim and Verrecchia (1991)). When viewing management earnings forecasts in isolation, period-specific and horizonspecific conditions can render earnings difficult to predict, regardless of the strength of the historical association of earnings with security prices. Unlike in the earnings release setting, managers are free to use characteristics such as timing and forecast form (e.g., rounding and varying range widths) to signal their uncertainty in the management forecast setting. If the forecast is more uncertain, the hard forecast information is less reliable, suggesting a potentially greater role in pricing for linguistic sentiment if the latter is perceived to be an independent signal. However, to the extent that linguistic sentiment’s usefulness is its ability to help predict future earnings, and the signal provided by management’s uncertain forecast is that earnings are currently difficult to predict, then linguistic sentiment may be viewed by market participants as similarly equivocal and thus less informative for prices. That is, investors view the two signals as dependent because the signals are both voluntary and arise from the same set of economic conditions and voluntary disclosure incentives:

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Davis, Piger and Sedor (2010) show that sentiment is incrementally positively associated with future firm performance (measured as the average of four-quarters-ahead return on assets).

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H3B:

The market response to linguistic sentiment is associated with the width of the

accompanying management forecast.

H3C: The market response to linguistic sentiment is associated with the rounding of the accompanying management forecast.

Management earnings forecasts also have a timing element. Timeliness increases relevance, and therefore longer range forecasts should be more useful to investors, all other things being equal, leading to the following hypothesis:

H3D: The market response to linguistic sentiment is associated with forecast horizon.6

Finally, linguistic certainty is potentially an additional measure of the difficulty associated with earnings prediction (Li (2006); Demers and Vega (2010)). A unique feature of our setting is that there is considerable uncertainty inherent in the “hard” information being forecasted, which is quite unlike the case of historical earnings realizations. However, given the flexibility associated with these voluntary disclosures, numerous non-linguistic options are also available for management’s conveyance of uncertainty over their earnings prediction (e.g., wider ranges, rounding, etc.). Thus, the role of linguistic certainty is far less obvious in the management forecast setting. To the extent that management relies on the numerous non-linguistic options to convey certainty about their earnings prediction, linguistic certainty may be viewed by investors as too noisy, and thus not relied upon in the pricing of linguistic sentiment. To the extent that linguistic certainty conveys management confidence in the quantitative forecast, less reliance might be placed on linguistic sentiment in favor of greater reliance on the hard signal (i.e., certainty would have an attenuating effect on sentiment).

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To the extent that horizon is also a proxy for the difficulty of predicting earnings, our hypotheses H3B and H3C imply that we may not be able to document a positive univariate effect for horizon on the value relevance of sentiment unless we control for items such as range width and rounding. Accordingly, we present tests of H3D with and without these controls, and we state H3D as a two-tailed hypothesis.

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To the extent that linguistic certainty conveys management confidence in the linguistic sentiment, more reliance would be placed on linguistic sentiment in pricing (i.e., an amplifying effect). Thus, relative to prior research in the mandatory filing or earnings release setting, it is a largely empirical question of whether linguistic certainty attenuates, amplifies, or has no impact on the market response to linguistic sentiment:

H3E: The market response to sentiment is associated with the level of certainty in the management forecast language.

Conditioning Effects on the Pricing of Linguistic Sentiment: Other Characteristics Affecting the Earnings to Price Mapping If linguistic sentiment is linked to prices through earnings, then certain other characteristics of information and the information environment that have been shown in past literature to affect the mapping of earnings into security prices should also affect the mapping of linguistic sentiment into prices. First, Freeman and Tse (1992) detect a non-linear or “S-curve” price response to earnings information. Extreme earnings news has an attenuated effect on stock prices. Likewise, we predict that extreme values of linguistic sentiment attenuate the price response to sentiment:

H3F: The market response to sentiment is decreasing in the absolute magnitude of the sentiment.

Atiase (1985) and Freeman (1987) use firm size as a proxy for the firm’s pre-disclosure information environment. They empirically document a greater anticipation of earnings, and thus a smaller price reaction to earnings news in the subsequent earnings releases, for larger firms. We predict the analogous effect of firm size on the price response to linguistic sentiment:

H3G: The market response to sentiment is decreasing in the strength of the pre-disclosure information environment (for which we use firm size as a proxy).

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Book-to-market ratios measure the joint effects of growth, earnings persistence, and risk, and thus they provide a summary measure of the rate at which firms’ earnings are capitalized. Studies examining earnings response coefficients typically predict a negative relation between book-to-market ratios and response coefficients (e.g., Rogers and Stocken (2005)). We predict an analogous effect for linguistic sentiment: H3H: The market response to sentiment is decreasing in capitalization rates (for which we use the book-to-market ratio as a proxy).

The Effects of Language on Return Volatility and Analyst Disagreement We also examine the effects of language in management forecast press releases on return volatility and analyst disagreement. Stock price shocks increase return volatility and expected returns. Rogers and Van Buskirk (2009) argue that the absolute magnitude of disclosure news can affect the market uncertainty reflected in security prices in two ways, either by increasing the information asymmetry across agents and/or by causing uncertainty about whether the observation is an extreme earnings observation from a stable distribution or evidence of a fundamental shift in the underlying earnings distribution.

Furthermore, volatility feedback and leverage effects lead to asymmetric

volatility responses depending upon the sign of news (Black (1976), French, Schwert and Stambaugh (1987), Campbell and Hentschel (1992)). Kothari, Wysocki and Shu (2009), for example, show that stock price shocks are generally greater for bad news. Furthermore, bad earnings news reduces market values, and thus increases leverage, which in turn increases stock return volatility. Subramanyam, Marquardt and Zhang (2005) and Rogers et al. (2009) document the positive relation between the magnitude of hard news and volatility for earnings releases and management forecasts, respectively, while Rogers et al. (2009) document the effect of hard bad news in management forecasts on volatility. Kothari, Xu and Short (2009) document a “favorable/unfavorable” content volatility and financial analyst disagreement

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effect for press releases but do not separate hard from soft news or forecasts from actual earnings or other releases.7 If language represents an additional signal of the magnitude and direction of changes in management’s beliefs about their firm’s future prospects, then we expect to observe incremental effects of this information (i.e., over and above hard news) on both volatility and financial analyst disagreement. Thus, we predict that both the absolute magnitude of sentiment and negative sentiment are incrementally associated with announcement period volatility, post-announcement period volatility, and increases in financial analyst disagreement about forecasted earnings:

H4A: Idiosyncratic return volatility and increases in disagreement among financial analysts are positively associated with the magnitude of sentiment in the management forecast press release.

H4B: Idiosyncratic return volatility and increases in disagreement among financial analysts are greater for negative sentiment than for positive sentiment in the management forecast press release (incremental to bad hard news).

Dye and Sridhar (2004) predict an inverse relationship between the certainty of soft information and return volatility. If linguistic certainty relates to earnings forecast certainty, then analysts should likewise increase their agreement about forecasted earnings after the management forecast press release is issued (Morse, Stephan and Stice (1991)):

H4C: Idiosyncratic return volatility and increases in disagreement among financial analysts are lower for higher levels of linguistic certainty in the management forecast press release.

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Disagreement across financial analysts is a proxy for information asymmetry, which is presumed to decrease with voluntary disclosure (e.g. Coller and Yohn (1997)).

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Is Negative Linguistic Sentiment Delayed? As discussed by Verrecchia (2001), managers have several incentives to delay bad news disclosure.

For example, Hermelin and Weisbach (2007) formally model bad news delay as a

function of career concerns. Kothari, Wysocki and Shu (2009) argue that news arriving randomly to managers would result in symmetrically distributed stock returns unless managers delay the disclosure of bad news to a threshold where the costs or difficulty of further delay require bad news disclosure. They interpret disclosures of larger amounts of bad news relative to good news as evidence that bad news has been delayed. They document that price reactions to management earnings forecasts are more negative for bad news unexpected forecast news than positive for good news unexpected forecast news. Negative sentiment is the linguistic equivalent of bad news. Thus, to complete our analysis of the effects of sentiment in its role as a soft signal of a manager’s mean expectations, we predict that negative sentiment is delayed.

H5: Negative sentiment is delayed relative to positive sentiment.

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III. Sample and Language Data Sample Determination We use the Factiva database to individually identify and download candidate management earnings forecasts. We follow Baginski, Hassell and Kimbrough (2004) by using business newswires Dow Jones Business News (“DJBN”) and Press Release Newswire (“PRN”) to search for the following word strings – “expects earnings,” “expects net,” “expects income,” “expects losses,” “expects profits,” and “expects results” – in addition to three parallel lists where “expects” is replaced alternatively by “forecasts,” “predicts,” and “sees”). This search yielded 6,180 candidate earnings forecasts (3,577 for DJBN and 2,603 for PRN) for the period 1997 through 2006, downloaded in batches of 100 announcements per .txt file. We next created individual .txt files for each candidate management forecast article (i.e., converting 62 files into 6,180 .txt files), and extracted firm identifiers for the companies underlying each respective Factiva article in order to attempt to match the candidate observations into the CRSP, Compustat, and First Call databases.

In order to be included in our sample, each candidate

management forecast from the Factiva search and extraction process had to match up, within a threeday window surrounding the Factiva date, with a management forecast from the First Call Company Issued Guidance database. Finally, we delete observations having fewer than 100 words in the forecast announcement. This process yielded a total of 4,104 observations. Additional details related to the sample determination and matching procedures are summarized in Appendix A. Following the prior literature (e.g., Anilowski, Feng and Skinner (2007); Rogers and Van Buskirk (2009)), we define bundled management forecasts as those falling within two days of an earnings announcement date. As in Hutton, Miller and Skinner (2003) and Rogers et al. (2009), we discard bundled forecasts to create a sample of non-bundled management forecasts. Bundled forecasts have properties that distinguish them from unbundled forecasts on the dimensions of forecast tone and presence of conflicting hard earnings news (Rogers and Van Buskirk (2009)) as well as incremental information content (Atiase, Li, Supattarakul and Tse (2005)). Furthermore, it is not possible to cleanly distinguish which aspects of the language in the bundled press releases pertain to the historical

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earnings announcement versus the forward-looking management forecast, and the properties of the historical earnings announcements have been examined at length in prior studies. Our remaining sample of 2,254 unbundled forecasts consists of press releases in which the management forecasts are the “main event” as in Hutton et al. (2003).8

Data Sources We identify and extract text passages of the management forecasts from DJBN and PRN within the Factiva database as described above.

The non-linguistic characteristics of the management

forecasts (e.g., point versus range, the numerical estimate, annual versus quarterly, etc.) are derived from the First Call Management Issued Guidance database. Market prices and returns data are provided by the Center for Research in Security Prices (CRSP), while the Compustat database is our source for accounting data.

Measuring the Language Constructs Evidence from prior studies (Loughran and McDonald (2010); Demers and Vega (2010)) suggests that generic linguistic algorithms such as Diction or General Inquirer may yield noisy measures of “positive” and “negative” linguistic tone in the context of financially-oriented text passages. Consequently, for our primary tests we adopt the Loughran and McDonald (2010) (L&M) finance-oriented dictionaries (i.e., word lists), for capturing “positivity” and “negativity” in the management forecast textual passages, measures that L&M refer to as Fin-Pos and Fin-Neg, respectively.9 Demers and Vega (2010) provide evidence, however, that the L&M measures for

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Even some unbundled forecasts are released concurrently with other information, either within the forecast release or otherwise. It is our goal to measure the impact of one kind of this other information, language, in the relatively less confounded context of a management forecast release. 9 We also follow the “cleansing” procedure in Demers and Vega (2010). Corporate press releases on the newswire services often include several paragraphs at the end of the announcement that are not part of the body of the announcement that is of interest to our study. Specifically, the releases typically include a companystandard paragraph that describes the firm, often using very flattering language. In addition, most of the articles tend to include some form of “Safe Harbor” disclaimer paragraph related to the forward-looking information included in the press release. These latter paragraphs vary somewhat across firms, but are generally “boilerplate” in nature and are presumed to be drafted by the company’s legal advisers. Finally, the press releases typically end with company contact information such as a listing of the corporate website address, their investor relations contact names and numbers, and/or information related to upcoming conference calls. Since

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“uncertainty” are noisier and perhaps less complex than Diction’s measure of linguistic “certainty,” a finding that also seems to hold in our data. As a consequence, and also in order to facilitate the comparison of our results with the growing body of accounting research that has principally relied upon the Diction algorithms, we also replicate all of our analyses using the Diction set of linguistic measures.10 Except where specified, our results are consistent across the two algorithms, albeit systematically more robust for the L&M measures of positivity and negativity relative to the optimism and pessimism measures provided by Diction, similar to findings reported in other studies. Also following the prior linguistic literature (e.g., Davis, Piger and Sedor (2010), Demers and Vega (2010)), our principal measure of tone is defined as the difference between Fin-Pos and Fin-Neg (optimism and pessimism) for the L&M- (Diction-) based measures, a variable that we label as NetPositivity. Our measures of Certainty from each of the two algorithms are identical to those defined in detail in Demers and Vega (2010). Similar to most prior studies, our NetPositivity variable does not attempt to explicitly measure the “unexpected” portion of sentiment. Although some prior studies have examined the time-series properties of sentiment in earnings releases, leading them to adopt the change in net optimism as a proxy for the “unexpected” or “news” component of linguistic sentiment, we have opted not to do so for several reasons. First, as Loughran and McDonald (2010) point out, measuring “unexpected” sentiment in this way imposes a considerable amount of structure on the linguistic parameters; specifically, it presumes a considerable amount of processing capability on the part of investors in the cross-section. While this is conceivable in the context of regularly recurring, mandated, quarterly earnings announcements, it is much less likely to hold in the context of sporadically issued, nonstandardized management forecasts. Second, the primary study to take this more refined approach, all of these paragraphs contain textual and numerical data that may include optimism-, pessimism-, and/or certainty-related language that does not form part of the content portion of management’s press release per se, based upon manual review and the identification of keyword strings, we developed algorithms to “cleanse” the .txt files of such non-announcement content. These cleansed .txt files are ultimately used as the basis for the linguistic characteristics extracted from the firm’s earnings forecast press releases. 10 Notwithstanding the noisiness of the measures derived from Diction in the context of financial text, it is a well-established language processing algorithm that has been used extensively in prior research to measure the sentiment in earnings announcements, corporate annual reports, Federal Reserve Board Chairmen’s speeches, and other economic and political communications. See Davis, Piger and Sedor (2010), Demers and Vega (2010), Davis and Tama-Sweet (2010), or the listing provided at http://www.dictionsoftware.com/files/dictionresearch.pdf for a more extensive summary of published academic studies using the Diction software.

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Demers and Vega (2010), reports that their basic findings are unaffected by the use of net optimism rather than the change in net optimism. Finally, a sufficient time series of management forecasts is not available for the majority of our sample firms, making it impossible to model the time-series behavior of sentiment in the context of management forecasts. The consequences of not specifying an expectations model for our sentiment variable would generally be to introduce noise into the measure, thereby reducing the power of our cross-sectional tests. As we discuss below, however, the results in relation to the net positivity variable are generally quite strong and significant in conservative testing that employs clustered standard errors.

IV. Empirical Results In this section, we present descriptive statistics followed by the empirical research design and test results for each of our hypotheses in turn.

Descriptive Statistics and Pairwise Correlations Table 1 provides descriptive statistics on the variables used in subsequent regression analyses.11

Consistent with prior research (e.g., Rogers, Skinner and Van Buskirk (2009)), the

unbundled forecasts in our sample are, on average, bad news, as evidenced by the negative means and medians for AR3, FSURP, and the post-announcement drift (AR60), although relative to AR3 the post-announcement drift is close to zero. The mean and median of NetPositivity are similarly slightly negative.12 Table 2, Panel A presents pairwise Pearson product-moment correlations between NetPositivity and several management forecast characteristics.

Similar to prior findings on

management forecast news, NetPositivity is more positive for both longer forecast horizons and annual forecasts. NetPositivity is also positively correlated with FSURP (ρ = 0.14, p < 0.01), which suggests that, on average, linguistic sentiment is not packaged with management forecasts in a way that would seem to be intended to mitigate price reactions to the forecast. Table 2, Panel B presents similar results using a chi-square testing framework. As shown, almost two-thirds of the forecasts in 11 12

The distributions presented in Table 1 are after winsorizing every variable at both the 1st and 99th percentiles. Mean and median net optimism from the Diction measurement approach are slightly positive (not tabulated).

20

our sample contain linguistic sentiment that is directionally consistent with the hard earnings forecast surprise. In other words, good news forecasts tend to be accompanied by net optimistic language, while bad news forecasts are predominantly accompanied by net pessimistic language. A chi-square test rejects the null of no association between the sign of forecast news and the sign of linguistic sentiment. These findings contrast sharply with a general finding in prior research of strategic bundling of good news with bad news, presumably to reduce the price consequences of bad news (e.g., Waymire (1984); Rogers and VanBuskirk (2009)). Our findings are, however, consistent with the results of prior linguistic studies that have documented a uniformly positive correlation between sentiment and unexpected earnings in earnings press releases. Taken as a whole, linguistic sentiment appears to be directionally similar to the unexpected hard news in the management forecast, in contrast to the strategic bundling documented in prior management forecast studies, although hard and soft information in forecast announcements are far from perfectly correlated.

Is the Sentiment in Management Forecast Press Releases Associated with Stock Returns? We begin by documenting how the market responds to the sentiment expressed in the management forecast press releases by estimating the following pooled cross-sectional, time series, ordinary least squares model: ARjt = β0 + β1 FSURPjt + β2 NetPositivityjt + εjt

(1)

where j is a firm subscript, and t represents a management forecast press release at date t.13 AR is the size- and book-to-market-adjusted three-day announcement return (AR3). In order to investigate the relation between linguistic sentiment and post-announcement drift, we also estimate equation (1) using the 60-day post-announcement return beginning on day +2 (AR60) as the dependent variable. FSURP is the management forecast surprise measured as the management forecast minus the

13

We have 2,254 individual management forecasts; however, some of these are clustered in time and/or issued by the same firm, creating a dependence problem (see Petersen 2009). Accordingly, we cluster on a firm/date identifier when estimating our regressions, such that all reported tests are based upon independent observation clusters. This leads to very conservative standard errors. As an alternative approach, we discarded forecasts by a firm when it also released another forecast on the same day (for example, a firm issuing two interim forecasts in a single release) without any significant change in our results. In tests described later, we identify one coefficient which changed from marginally significant to insignificant using this alternative sample and one coefficient which changed from insignificant to marginally significant.

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preceding median consensus analyst forecast, all scaled by pre-announcement (day -2) security price. To compute the management forecast for non-point forecasts, we use the midpoint of ranges and the disclosed upper or lower bound for maximum and minimum forecasts, respectively, all consistent with the prior literature in this area. NetPositivity is the linguistic measure of sentiment derived from the management forecast press release as previously described.

H1 predicts a directionally

consistent, incremental effect of sentiment on security price response (i.e., β2 > 0). Table 3 presents the results from estimating equation (1) using three-day announcement period returns, and 60-day post-announcement drift period returns, respectively, as the dependent variable. In our main sample (the “Unbundled” column), we present separate results for regressions using hard forecast news (FSURP) only, sentiment (NetPositivity) only, and the hard and soft news variables together, enabling us to gauge the incremental effects of the soft and hard news. The coefficients on FSURP and NetPositivity are significantly positive when explaining both 3-day announcement and 60-day post-announcement returns in the univariate regressions.14 Most interestingly, the R2 from the sentiment only regression is substantially larger than that for the hard forecast news only regression when explaining 3-day returns (R2 = 11.45% as compared to 2.31% in the hard news regression). The R2’s are similar in the sentiment only and hard news only regressions when explaining post-earnings announcement drift. The last panel of Table 3 indicates that sentiment and hard forecast news are each incrementally significant to one another in the multiple regressions explaining 3-day and 60-day returns, respectively.15 Taken as a whole, Table 3 provides strong evidence to support the H1 prediction of an incremental price effect from sentiment.

What is most surprising is that our findings in the

14

Ng, Tuna and Verdi (2008) also document a delayed response to unexpected management forecast hard news. Market efficiency is a maintained assumption in our analysis. However, given the post-announcement drift literature and the Ng, Tuna and Verdi (2008) finding, we present the post-announcement results in a descriptive vein. 15 Conclusions using the Diction net optimism measure are the same. However, as has been documented in prior research, the Diction measure is noisy, and its association with security prices, while still significant both in a univariate sense and incrementally to hard news , is clearly weaker (5.3% R2 relative to 13.4% R2 using the L&M based net positivity measure in the univariate three-day announcement period return regression). Our sample includes forecast releases occurring close to the period end, which some studies refer to as “warnings.” The conclusions reported in Tables 2 and 3 are not affected by the discarding of management forecasts of less than 14 days before the earnings release date. Also, modification of the announcement period model in Table 3 to include intercept and slope shifts for these warnings yield insignificant coefficients on the intercept and slope shifts, indicating that the price reactions to hard and soft news do not differ when the observation is a warning.

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management forecast setting are substantially different from those reported by prior research in the context of earnings announcements (i.e., where the hard earnings news explained much more than sentiment). These preliminary results suggest that language may indeed have a special role to play when the simultaneously released “hard” news is less standardized, potentially less credible, and often deliberately issued in an imprecise manner relative to the structured, ultimately audited information released in the earnings announcements. We investigate this issue further in the sections that follow.

Does Sentiment’s Pricing Depend on the Reliability of the Accompanying Hard News? To examine whether the price informativeness of sentiment differs when the hard earnings news is less reliable (H2), we perform one informal and one formal analysis. Our informal analysis compares the percentage increase in total explanatory power in equation (1) when soft information is added to the management forecast press release model relative to the percentage increase in explanatory power when soft information is added to the earnings announcement model from the Demers and Vega (2010) study. The last row in Table 3 presents a ratio of the adjusted-R2 from the multiple regression to the adjusted-R2 from the hard forecast news only regression. We first consider the results for our primary sample of unbundled forecasts, for which we find that adding NetPositivity to the 3-day announcement period returns regression leads to an increase in the adjusted-R2 from 2.31% to 12.53%, or 5.42 times. In Demers and Vega (2010, Table 2), adding L&M-based soft information to a hard information regression increases the adjusted-R2 from 2.23% to 3.06% (i.e., only 1.37 times). The ratio for the 60-day post announcement regressions is directionally similar but less dramatic, with the sentiment increasing explanatory power by a factor of 1.41 (compared to a factor of 1.28 for earnings announcement in Demers and Vega (2010)). Taken as a whole, our informal analyses indicate that the sentiment in management forecast press releases adds far more explanatory power than the soft information included in earnings announcements, and especially so during the announcement window. The evidence is thus consistent with the notion that sentiment is more informative in settings where the competing hard news is perceived to be less reliable.

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To address this issue further and more formally, we estimate the following extended version of model (1) on the entire sample of management forecasts, including both those that are unbundled and bundled with earnings announcements:16 ARjt = β0 + β1 FSURPjt + β2 NetPositivityjt + β3 UEjt + β4 Bundledjt + β5 Bundledjt * NetPositivityjt + εjt

(2)

where: Bundled is an indicator variable set equal to one when the forecasts are bundled with earnings announcements; and UE is the unexpected earnings announced in the bundled press releases, defined as actual earnings per share minus the preceding median analyst forecast of earnings per share for bundled forecasts and zero for unbundled forecasts.

Our interest is in the coefficient on

Bundled*NetPositivity, β5, which captures the differential pricing of linguistic sentiment. The results appear in the second and fourth columns of Table 3 (the “All” columns) using 3-day announcement period and 60-day post-announcement returns, respectively.

Consistent with the results and

interpretation of the informal test, the coefficient β5 is reliably negative, indicating that the market responds less to soft information when the soft information is accompanied by hard historical earnings realizations rather than just their more subjective, often imprecise management-forecasted counterparts. Do Management Forecast Characteristics and Earnings Characteristics Condition the Pricing of Linguistic Sentiment? Hypothesis 3 predicts that a set of management forecast and earnings characteristics condition the price response to linguistic sentiment. To initially test the various sub-hypotheses, we append (in separate regressions) conditioning effects to the following baseline model, where the dependent variable is 3-day announcement period returns: Baseline:

ARjt = β10 + β11 FSURPjt + β12 NetPositivityjt

16

(3)

In subsequent analyses, we document additional conditioning effects on the pricing of sentiment. When these effects are included as control variables in equation (2), our conclusions on the effects of bundling are not affected (results not tabulated).

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Conditioning Effects: Earnings Informativeness:

+ β13A LaggedEarnInformjt + β14A LaggedEarnInformjt * NetPositivityjt

Width:

+ β13B DWidthjt + β14B DWidthjt * NetPositivityjt

Rounding:

+ β13C Roundingjt + β14C Roundingjt * NetPositivityjt

Timeliness (Horizon):

+ β13D Horizonjt + β14D Horizonjt * NetPositivityjt

Linguistic Certainty:

+ β13E Certaintyjt + β14E Certaintyjt * NetPositivityjt

S-Curve (Absolute News):

+ β13F |NetPositivity|jt+ β14F |NetPositivity|jt * NetPositivityjt

Information Environment (Size):

+ β13G Sizejt + β14G Sizejt * NetPositivityjt

Capitalization Rate (Book-toMarket):

+ β13H BTMjt + β14H BTMjt * NetPositivityjt

As described in greater detail in Table 1, laggedEarnInform is the historical price-relevance of earnings; DWidth is an indicator variable that is set equal to one for observations for which the forecast width is above the median; Rounding is an indicator set equal to one when the earnings forecast has been rounded to the nearest nickel; Horizon is the number of calendar days from the management forecast to the actual earnings release; Certainty is linguistic certainty; |NetPositivity| is the absolute magnitude of NetPositivity; Size is the log of the market value of equity; and BTM is the book-to-market ratio. Table 4 presents the estimations of equation (3) for each conditioning effect separately. Coefficients β14A - β14H, which are the interaction effects shown in the row entitled “Interaction of Main Effect with NetPositivity,” correspond to H3A – H3H. Recall that the expected coefficient on the NetPositivity variable is positive, indicating that greater positive sentiment is associated with more positive price reactions to the management forecast release. Therefore, a negative (positive) sign

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prediction on the interactions with NetPositivity imply attenuation (magnification) of the positive association between sentiment and price reaction.17 Relating to H3A, linguistic sentiment plays less of a role in pricing when historical earnings informativeness is greater (t = -2.44). Thus, summarizing the findings in H2 and H3A, linguistic sentiment is more useful 1) when issued in a management forecast press release (as opposed to an actual earning press release), 2) when issued with a management forecast in a non-bundled forecast press release, and 3) when earnings, in general (i.e., regardless of how earnings are disclosed during the period), are less informative for prices. However, at the time of the release and due to the fundamental nature of forecasting, earnings predictability of both hard and soft information varies within our sample. H3B predicts an effect of forecast range width on the pricing of sentiment.

The coefficient on the width by sentiment

interaction is negative and marginally insignificant at a conventional level (t = -1.63). H3C predicts an effect of forecast rounding on the pricing of sentiment. The coefficient on the rounding by sentiment interaction is significantly negative (t = -2.04). Taken together, these results suggest that forecastspecific conveyances of management uncertainty of earnings attenuate the pricing of linguistic sentiment. We are unable to reject the null for both the timeliness (horizon) effect (H3D) and the linguistic certainty effect (H3E). An interesting outcome of our tests is the stronger effect for rounding vis-à-vis forecast form. A potential ex post explanation is that rounding is also an indicator of likely bias. Bamber et al. (2010) document that rounded forecasts are not only ex post less accurate, they are also more optimistically biased. Thus, the discounting of sentiment we find might be the market’s response to the likelihood that self-serving incentives, rather than uncertainty, are driving voluntary disclosure of both hard and soft information. As predicted by H3F, the s-curve relation between earnings and security prices is also present for linguistic predictions of earnings embodied in linguistic sentiment. Large absolute values of sentiment attenuate the pricing of sentiment (t = -4.74).

17

As predicted by H3G, the stronger

We also include intercept shifts to guard against the possibility that the interaction term captures main effects. However, our hypotheses relate only to the interaction effects.

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predisclosure information environments of larger firms also attenuate the predictions of earnings embodied in linguistic sentiment (t = -2.17). We are unable to reject the null for the book-to-market effect (H3H). We conclude from these three tests that sentiment’s pricing effects are conditioned by the same phenomena that condition the price response to earnings, precisely what one would expect if sentiment is theoretically related to prices through the earnings fundamental. With respect to the conditioning role of certainty, however, the results are less convincing; in a test that is powerful enough to detect rounding effects in isolation, we are unable to detect a linguistic uncertainty effect on the mapping of unexpected hard or soft forecast news into prices.18 Table 2 shows that Certainty does not have consistent pairwise correlations with any of the management forecast characteristics except for a positive correlation with forecast horizon, which one would not expect for a credible conveyance of certainty. However, this lack of correlation raises the possibility that linguistic certainty is a phenomenon separate from the management earnings forecast characteristics previously studied, and perhaps that it relates more to general uncertainty rather than to the precision of the forecast. Notably, these results differ from the evidence presented in prior studies related to the effect of certainty on the market’s response to hard news in the context of earnings announcements (Demers and Vega (2010)), a more standardized and rigid setting in which there are fewer (if any) options outside of language for conveying uncertainty or imprecision. This contrast suggests that the numerous alternative levers available to management in the context of forecast announcements leave a lesser role for the certainty dimension of language to convey managerial imprecision.19 In Table 5, we examine incremental conditioning effects of the eight variables on three-day announcement returns in the following model which includes all intercept and slope shifts from Table 4:

18

The results are similar whether we use the L&M-based, or (in untabulated tests) the more powerful Dictionderived measure of linguistic certainty. 19 Our focus is on the pricing effects of language. In the context of certainty, we examine the effects of linguistic certainty on the pricing of linguistic sentiment. Although not our focus, linguistic certainty could have a cross-over effect on the pricing of the hard forecast news similar to the cross-over effect of width and rounding on the pricing of linguistic sentiment that we do test in our study. In untabulated tests, we are unable to document an enhancing effect of language on the pricing of hard forecast news for either the L&M or Dictionbased certainty measures.

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ARjt = β20 + β21 FSURPjt + β22 NetPositivityjt + β23 DWidthjt + β24 DWidthjt * NetPositivityjt + β25 Roundingjt + β26 Roundingjt * NetPositivityjt + β27 Horizonjt + β28 Horizonjt * NetPositivityjt + β29 Certaintyjt + β30 Certaintyjt * NetPositivityjt + β31 |NetPositivity|jt + β32 |NetPositivity|jt * NetPositivityjt+ β33 Sizejt + β34 Sizejt * NetPositivityjt + β35 BTMjt + β36 BTMjt * NetPositivityjt + β37 LaggedEarnInformjt + β38 LaggedEarnInformjt * NetPositivityjt + εij

(4)

When all effects enter the model, six of the eight moderating effects are significant. Two effects that were insignificant in Table 4 individual effects models are now significant.

The

timeliness (horizon) effect (H3D) is significantly positive (t = 1.99) after controlling for the other effects, many of which capture the uncertainty in long-horizon forecasts which render them less useful. The book-to-market effect (H3H) is also significantly negative as expected (t = -1.82). We therefore conclude that the hypothesized conditioning effects are incremental to each other.20 Although we have no specific hypotheses about intercept shifts, we note that the coefficients on Certainty and |NetPositivity| are positive (t = 3.59) and negative (t = -2.81), respectively, indicating that the price reactions to management forecasts are more positive when language is more certain and less extreme. Larger firms and firms with higher book-to-market ratios also have more positive price reactions.21

20

Belsley, Kuh and Welsch (1980) condition indices indicate that mild collinearity exists in equation (4). As is often the case, the potential problem’s source is the inclusion of firm size in the model. The signs of the coefficients in Table 5 are consistent, however, with the signs of the coefficients in the Table 4 regressions which do not include size. A re-estimation of the Table 5 regression after omitting size and the size interaction term changes our conclusions on one coefficient of interest; the coefficient on the BTM interaction becomes insignificant. Therefore, the incremental conditioning effect of this variable is dependent on a control for firm size. Also, we re-estimated the Table 5 regression with additional control variables, including interactions between FSURP and the eight conditioning variables, additional main effect and interaction terms with NetPositivity for predicted losses, lagged accruals, and the standard deviation of analyst forecasts prior to the management forecast. Our conclusions relating to the predictions of H3A – H3H are unaffected. We also considered whether special measurement rules for minimums and maximum type forecasts when calculating Rounding and DWidth affected our results. Discarding minimums and maximums do not affect our conclusions on the Rounding and DWidth interactions. Finally, we reach the same conclusions if we base our tests on the smaller sample of single forecasts by a single firm on a given date described earlier rather than clustering for standard error computation. 21 The significance of coefficients on the interaction variables in Table 5 are not affected by estimating a version of the model without intercept shifts.

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Is Management Forecast Press Release Language Associated with Idiosyncratic Return Volatility and Analyst Forecast Disagreement? To test our predictions about the associations of the magnitude of sentiment (H4A), the sign of sentiment (H4B), and linguistic certainty (H4C) with both idiosyncratic return volatility and analyst forecast disagreement, we estimate the following model: DependentVariablejt = γ0 + γ1 |NetPositivityjt| + γ2 NegativeSentimentjt + γ3 Certaintyjt + γ4 |FSURPjt| + γ5 BadNewsjt + γ6 DWidthjt + γ7 Roundingjt + γ8 Horizonjt + γ9 Annualjt + γ10 Sizejt + γ11 laggedVolatilityjt + γ12 LaggedEarnVolatilityjt + γ13 LaggedEarnPersistencejt + γ14 LaggedTotalAccrualsjt + γ15 PredictedLossjt + εjt

(5)

where DependentVariable is, in separate regressions, Volatility3, the log of the sum of the squared AR over the 3-day (i.e., day -1 to day +1) announcement period, Volatility60, the log of the sum of the squared AR over the 60-day post-announcement period beginning on day +2, and IncreaseAFSTD, the standard deviation of financial analyst EPS forecasts after the management earnings forecast press release, minus the standard deviation before the press release, scaled by price two days before the press release. The primary independent variables of interest are the language variables: absolute magnitude of sentiment (|NetPositivity|), the sign of the sentiment (NegativeSentiment, an indicator variable set equal to one if NetPositivity < 0, and zero otherwise) and linguistic certainty (Certainty). H4A – H4C predict that the coefficient on |NetPositivity| (γ1) > 0, the coefficient on NegativeSentiment (γ2) > 0, and the coefficient on Certainty (γ3) < 0, respectively. We also include several control variables in the model. First, characteristics of the hard forecast news are likely to affect return volatility and analyst forecast disagreement. Two variables control for the magnitude and sign of the hard forecasts news: |FSURP| is the absolute value of the hard management forecast surprise measured as the point (or midpoint of the range or disclosed minimum or maximum) forecast minus the preceding median consensus analyst forecast, scaled by price; and BadNews equals one if FSURP < 0, and zero otherwise. Four variables control for management’s explicit conveyance and expected uncertainty with regard to the forecasted hard news:

29

DWidth equals one for values of Width above the median;22 Rounding equals one if the management forecast is perfectly divisible by $0.05, and zero otherwise; Horizon equals the number of calendar days between the management forecast and the actual earnings release; and Annual equals one if the observation is an annual forecast and zero if it is a quarterly forecast. We also control for crosssectional differences in the general information environment that are likely to affect volatility and analyst disagreement: Size is the log of the firm’s market value prior to the forecast issuance date; and LaggedVolatility is the log of the sum of the squared AR over a 40-day pre-event window. In addition, we control for general earnings characteristics that are likely to be related to return volatility and analyst disagreement: LaggedEarnVolatility equals the standard deviation of annual realized earnings per share using up to 15 years of prior earnings realizations; LaggedEarnPersistence equals the first-order autocorrelation of realized earnings per share; and LaggedTotalAccruals equals Compustat annual data item IBC (formerly Data123) minus annual data item OANCF (formerly Data308), all from the prior year. Table 1 presents the descriptive statistics on each variable. Table 6 presents the results of estimating equation (5). Consistent with H4A, |NetPositivity| is positively associated with investor uncertainty in the 3-day announcement window (t = 3.06) as well as with post-announcement analyst forecast dispersion (t = 2.06), however it is not associated with 60day post-announcement abnormal volatility.23 The results suggest that more intensive sentiment is associated with higher announcement period volatility and post-announcement forecast disagreement, but it does not predict post-announcement volatility. The latter result is inconsistent with the findings of Demers and Vega (2010) in the context of earnings announcements, who document that the surprise in net optimism is associated with future abnormal idiosyncratic volatility. The results presented in Table 6 are also strongly supportive of H4B, which predicts a negative association between the sign of sentiment and the various measures of investor and analyst uncertainty. The coefficient on the NegativeSentiment indicator variable is significantly positive in all regressions (including untabulated results using the Diction-based measures), even after controlling 22

Width equals the high minus low endpoints of the management range forecast, divided by price; point forecasts are set to Width = 0; minimum and maximum forecasts are set to the highest value of Width in the sample. 23 Neither the 3-day returns nor analyst dispersion results hold if we use the noisier Diction measure of the magnitude of sentiment.

30

for other determinants of volatility and analyst forecast dispersion, respectively (t = 4.82, 3.52, and 3.28 when explaining announcement volatility, post-announcement volatility, and increase in analyst forecast disagreement, respectively). Notably, the negative sentiment effect is incremental to, and often of greater magnitude than, the highly significant hard bad news effect that is captured by the positive coefficients on the BadNews variable. Thus, bad hard forecast news is associated with greater uncertainty regarding both future projected earnings as well as current and future stock prices for the forecasts in our sample, consistent with the findings of prior studies.

We contribute to this

literature by documenting that the presence of net negative soft information plays an incremental, and often more significant, role in explaining the various measures of investor disagreement. The results in Table 6, however, do not support the prediction in H4C. The coefficient on Certainty is significantly negative as expected only when explaining three-day announcement period returns. Prior authors have found that Diction-based measures of linguistic certainty seem to be more complex and multi-faceted than L&M’s more restrictive metric, and generally the Diction-based measures are more significantly associated with various financial measures of risk and uncertainty (Demers & Vega (2010)). Consistent with these more favorable properties of the Diction measure of Certainty, in untabulated results we find that it is significantly negatively associated with announcement period (t = -3.20) and post-announcement (t = -2.50) abnormal idiosyncratic volatility as predicted by H4C. Taken as a whole, these results suggest that, although linguistic certainty does not condition the price reaction to hard forecast news or linguistic sentiment at the management forecast press release date (using either Diction or L&M measures), it is associated with general return volatility during the announcement period and it also serves as a leading indicator of post-announcement volatility when measured using the more complex and multi-faceted Diction certainty measure.

Is Negative Sentiment Delayed? The consequence of negative sentiment for returns is particularly strong if negative sentiment is delayed. Kothari et al. (2009) provide a design for testing the hypothesis that bad news is delayed,

31

which we modify to test the bad sentiment delay predicted by H5. We estimate various forms of the following regression to explain 3-day announcement period returns, which allows intercept and slope shifts for both bad news (FSURP