Research Division Federal Reserve Bank of St. Louis Working Paper Series

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1 Research Dvson Federal Reserve Bank of St. Lous Workng Paper Seres Beyond the Numbers: An Analyss of Optmstc and Pessmstc Language n Earnngs Press Releases Angela K. Davs Jeremy M. Pger and Lsa M. Sedor Workng Paper A January 2006 FEDERAL RESERVE BANK OF ST. LOUIS Research Dvson P.O. Box 442 St. Lous, MO The vews expressed are those of the ndvdual authors and do not necessarly reflect offcal postons of the Federal Reserve Bank of St. Lous, the Federal Reserve System, or the Board of Governors. Federal Reserve Bank of St. Lous Workng Papers are prelmnary materals crculated to stmulate dscusson and crtcal comment. References n publcatons to Federal Reserve Bank of St. Lous Workng Papers (other than an acknowledgment that the wrter has had access to unpublshed materal) should be cleared wth the author or authors.

2 Beyond the Numbers: An Analyss of Optmstc and Pessmstc Language n Earnngs Press Releases Angela K. Davs Washngton Unversty, Oln School of Busness, St. Lous, MO Jeremy M. Pger Federal Reserve Bank of St. Lous, St. Lous, MO Lsa M. Sedor Unversty of Notre Dame, Mendoza College of Busness, Notre Dame, IN January 2006 We thank Frankln Wllams, Mkhal Pevzner and Mchelle Armesto for research assstance. We thank Bob Bowen, Rch Frankel, Greg Hess, Ron Kng, Dawn Matsumoto, James Morley and workshop partcpants at the Unversty of Oregon, Unversty of Utah, and the 2006 Washngton Unversty Accountng Research Conference for helpful comments. The vews expressed n ths paper should not be nterpreted as reflectng the vews of the Federal Reserve Bank of St. Lous or the Federal Reserve System.

3 Beyond the Numbers: An Analyss of Optmstc and Pessmstc Language n Earnngs Press Releases Abstract: In ths paper, we examne whether managers use optmstc and pessmstc language n earnngs press releases to provde nformaton about expected future frm performance to the market, and whether the market responds to optmstc and pessmstc language usage n earnngs press releases after controllng for the earnngs surprse and other factors lkely to nfluence the market s response to the earnngs announcement. We use textual-analyss software to measure levels of optmstc and pessmstc language for a sample of approxmately 24,000 earnngs press releases ssued between 1998 and We fnd a postve (negatve) assocaton between optmstc (pessmstc) language usage and future frm performance and a sgnfcant ncremental market response to optmstc and pessmstc language usage n earnngs press releases. Results suggest managers use optmstc and pessmstc language to provde credble nformaton about expected future frm performance to the market, and that the market responds to managers language usage. Keywords: Dsclosure, Press release, Language JEL Classfcatons: G14, L25, M41

4 1. INTRODUCTION Earnngs press releases have been characterzed as, the major news event of the season for many companes as well as nvestors, analysts, fnancal meda, and the market (Lews and Mahoney 2004). Requred by the NYSE and NASDAQ, earnngs press releases comprse an mportant element of a frm s overall dsclosure strategy and communcate nformaton to nvestors n both numercal and narratve forms. Recent academc research demonstrates that the nformaton content of earnngs press releases has ncreased n recent decades (Collns et al. 2005; Francs et al. 2002a; Francs et al. 2002b; Landsman and Maydew 2002; Lo and Lys 2001; Kross and Km 2000). However, ths work has focused prmarly on elements of numercal dsclosures (e.g., the announcement of earnngs per se) rather than on elements of narratve dsclosures (e.g., the language used) n earnngs press releases. 1 Although the role that language usage plays n the percepton and understandng of narratve dsclosures (e.g., Katz 2001) and n the formaton of expectatons (e.g., Morrs et al. 2005) has been examned n other contexts, accountng researchers have yet to study the role that language usage n earnngs press releases plays (f any) n the credble communcaton of nformaton to nvestors. The purpose of ths paper s to examne whether managers use optmstc and pessmstc language n earnngs press releases to provde nformaton to market partcpants about expected future frm performance, and whether the market responds to optmstc and pessmstc language usage n earnngs press releases after 1 An excepton s Hoskn et al. (1986). Although language usage s not the focus of ther study, these authors examne the subject matter of offcer quotatons n addton to the numercal dsclosures contaned n earnngs press releases. Francs et al. (2002b) also examne the nformaton content of offcer quotatons n earnngs press releases. In contrast to these studes whch focus solely on offcer quotatons and employ manual technques for codng quotaton subject matter (and thus analyze relatvely small samples), we focus on the language used n all narratve dsclosures ncluded n earnngs press releases and employ an establshed textualanalyss software program to systematcally measure levels of optmstc and pessmstc language for a relatvely large sample of earnngs press releases.

5 controllng for the earnngs surprse and other factors lkely to nfluence the market s response to the earnngs announcement. Pror research demonstrates that market partcpants respond to the subject matter of narratve dsclosures. For nstance, the market responds to the subject matter of offcer quotatons n earnngs press releases (Hoskn et al. 1986) and causal attrbutons n management earnngs forecasts (.e., reasons underlyng managers earnngs expectatons) (Bagnsk et al. 2000) even after controllng for the underlyng determnants of the attrbutons (Bagnsk et al. 2004). Informaton about forecast precson s related to stock prce reactons to management earnngs forecasts (Bagnsk et al. 1993; Pownall et al. 1993; and Bagnsk et al 1994), and narratve dsclosures contaned n Management s Dscusson and Analyss (MD&A) nfluence fnancal analysts forecasts (Barron et al. 1999; Clarkson et al. 1999; Bryan 1997). The nformaton communcated va narratve dsclosures, however, lkely extends beyond the subject matter to other elements of the dsclosure. For example, expermental evdence shows that analysts annual earnngs forecasts are nfluenced by the structure of managers narratve dsclosures, holdng the nformaton content of those dsclosures constant (Sedor 2002). Further, holdng nformaton about stock prce trends constant, nvestors expectatons of trend contnuaton are nfluenced by the language used to descrbe the trend (Morrs et al. 2005). Thus, t s possble that managers use of optmstc and pessmstc language (n addton to dsclosure subject matter and structure) provdes nformaton to market partcpants about expected future frm performance. In fact, sgnfcant varatons n language usage across frms has led nvestor relatons professonals to debate how language used n earnngs press releases (whch can range from straght 2

6 forward to promotonal) s nterpreted by nvestors, analysts, and others (Lews and Mahoney 2004). The crux of ths debate s consstent wth the vew that the language used n a press release s lkely to communcate values and sentments that are not neutral (e.g., Katz 2001). We focus our nvestgaton on the levels of optmstc and pessmstc language n the narratve dsclosures of earnngs press releases because managers have sgnfcantly more dscreton avalable to them when communcatng n narratve versus numercal forms. Therefore, t s lkely that managers use the language n earnngs press releases to communcate nformaton to nvestors about managers expectatons for future frm performance beyond that communcated va the numercal dsclosure of earnngs alone. Current regulaton does not explctly address the language used n earnngs press releases and the federal antfraud provsons general requrement that dsclosures be accurate and complete so as not to mslead (Trautmann and Hamlton 2003) s lkely more dffcult to enforce n the context of language usage. Further, t s unclear ex-ante f managers language usage credbly communcates nformaton to nvestors about expected future frm performance because language usage, unlke numercal dsclosures, s not subject to ex-post verfcaton (e.g., Healy and Palepu 2001). Recent research n the area of management earnngs forecasts demonstrates that the market responds to narratve dsclosures accompanyng management earnngs forecasts only when those narratve dsclosures are verfable ex-post (Hutton et al. 2003). We analyze a sample of approxmately 24,000 quarterly earnngs press releases publshed on PR Newswre between 1998 and A dstnctve feature of our study s the use of an establshed textual-analyss software program to analyze the narratve dsclosures contaned n earnngs press releases and obtan systematc measures of the levels of 3

7 optmstc and pessmstc language used theren. In partcular, we use textual-analyss software that has been employed extensvely to analyze contemporary dscourse ncludng: speeches of poltcans (Hart 1984, 2000a, b; Hart and Jarvs 1997; Blgh et al and 2004); speeches of Federal Reserve polcymakers (Blgh and Hess 2005a; 2005b); annual reports to stockholders (Yuthas et al. 2002) 2 ; and other busness communcatons (Ober et al. 1999). Because the textual-analyss software program counts words characterzed as optmstc (e.g., best, confdent, mprovement) and pessmstc (e.g., bad, conflct, don t) based on lngustcs theory (Hart 1984, 1987, 2000a b, and 2001), our measures of optmstc and pessmstc language are complementary to, but separable from, the subject matter of the earnngs press release. To capture the effects of optmstc and pessmstc language usage alone, we nclude the earnngs surprse and other varables lkely to nfluence the market response to the earnngs announcement as controls for subject matter. 3 Our key results are as follows: (1) Levels of optmstc and pessmstc language used by managers n earnngs press releases relably predct future frm performance suggestng that managers use language to communcate nformaton to nvestors about managers future earnngs expectatons. (2) There s a sgnfcant market response to the levels of optmstc and pessmstc language n earnngs press releases that s ncremental to the current perod earnngs surprse and other factors such as whether the frm beats analysts expectatons, experenced negatve earnngs, or ncluded a management forecast n the earnngs press 2 In ther study, Yuthas et al. (2002) analyze annual-report narratves to assess the ethcal characterstcs of the dsclosures by reference to Habermas norms whch requre communcatons to be comprehensble, truthful, sncere, and legtmate. Therefore, they do not examne assocatons between narratve dsclosures and ether future frm performance or the market s response to the narratve dsclosures. 3 The control varables may not account for all subject matter n the narratve of the press release, partcularly for forward-lookng dsclosures. To nvestgate the potental mportance of subject matter reflected n forwardlookng dsclosures, we also conduct senstvely analyses n whch we control for the presence of a management forecast n the earnngs press release. 4

8 release. Ths result suggests that managers credbly communcate nformaton to nvestors va optmstc and pessmstc language usage. (3) The market appears to form expectatons, pror to the earnngs announcement, regardng the levels of optmstc and pessmstc language used n earnngs press releases. In partcular, we employ a random walk expectaton model and fnd that the unexpected porton of optmstc and pessmstc language s prced, whereas the expected porton s not prced. Ths fndng suggests that managers lkely have reputatons for optmstc and pessmstc language use, and that the market reacts to levels of optmstc and pessmstc language usage n managers dsclosures that dffer from nvestors expectatons. 4 Our results contrbute to the lterature on voluntary dsclosure by demonstratng that language usage n narratve dsclosure s an mportant component of earnngs press releases and s used by managers to provde nformaton about expected future frm performance to the market. To our knowledge, ths s the frst study to examne optmstc and pessmstc language usage n earnngs press releases, ncludng whether such language usage s assocated wth future frm performance and whether the stock market reacts to managers language usage. Further, our large sample sze and use of establshed textual-analyss software to obtan systematc measures of the levels of optmstc and pessmstc language used by managers n earnngs press releases dfferentates our study from prevous studes of narratve dsclosure n earnngs press releases that employ subjectve, manual codng of narratve nformaton (e.g., Hoskn et al. 1986; Francs et al. 2002b). 4 For example, Mcrosoft s executves treat analysts to a constant patter of cautonary and even downbeat words about the future. After a typcally grm presentaton by CEO Bll Gates and sales chef Steve Ballmer at an analysts metng two years ago, Goldman Sachs analyst Rck Sherlund ran nto the par outsde and sad, Congratulatons. You guys scared the hell out of people. Ther response? They gave each other a hgh fve, Sherlund recalls (Fox 1997). 5

9 The remander of the paper s organzed as follows. Secton 2 provdes nsttutonal background regardng earnngs press releases and motvates our research questons. Secton 3 dscusses the sample, presents varable defntons, and descrbes our measures of optmstc and pessmstc language usage n earnngs press releases. Secton 4 presents descrptve evdence on the narratve dsclosures n earnngs press releases and presents results of future performance and returns tests along wth related senstvty analyses. Secton 5 concludes and dscusses future research possbltes. 2. BACKGROUND AND MOTIVATION OF RESEARCH QUESTIONS Earnngs press releases are requred by New York Stock Exchange (NYSE) and NASDAQ rules, and both the Fnancal Executves Insttute (FEI) and the Natonal Investor Relatons Insttute (NIRI) have ssued best-practce gudelnes for earnngs press release preparaton (Trautmann and Hamlton 2003). Earnngs press releases prepared n accordance wth best-practce gudelnes should contan: hstorcal data; analyses of operatng results; dscussons of postve and negatve factors affectng key fnancal ndcators; the outlook for upcomng quarters (wth approprate Safe Harbor language); and other nformaton (Trautmann and Hamlton 2003). NIRI and FEI best-practce gudelnes state that earnngs press releases should present a reasonably balanced perspectve of operatng performance. Consstent wth ths, NYSE rules requre that press releases place news n the proper perspectve statng that companes should avod overly optmstc forecasts, exaggerated clams, and unwarranted promses (NYSE Manual). All press releases and publc announcements fall wthn the scope of the antfraud requrements of federal securtes laws, whch state that the nformaton dsclosed should be accurate and complete so as not to 6

10 mslead (Trautmann and Hamlton 2003). Although language usage s regulated n other market contexts (e.g., U.S. Federal Trade Commsson, U.S. Food and Drug Admnstraton), current regulaton n securtes markets does not explctly address language usage n earnngs press releases. Thus, t s lkely more dffcult to enforce the antfraud requrements for language usage n narratve dsclosures, partcularly when compared to numercal dsclosures (whch are prepared n accordance wth GAAP and can be traced to SEC flngs). 5 Anecdotal evdence suggests that language usage n earnngs press releases vares substantally across frms. In ther examnaton of hundreds of quarterly earnngs press releases Wllam Mahoney and John Lews, authors of The IR Book, fnd that language n earnngs press releases can range from straght-forward rectatons of numbers to beng qute promotonal wth nformaton ether presented n a fact-based, no-frlls-added manner or cast n postve-to-superlatve terms (Mahoney and Lews 2004). Gven the nherently subjectve nature of language, the dscreton exstng regulatons allow managers when wrtng earnngs press releases, and managers ncentves to make self-servng voluntary dsclosures (Healy and Palepu 2001), t s possble that market partcpants vew the optmstc or pessmstc language used n earnngs press releases as lackng credblty and thus gnore t when assessng the nformaton content of earnngs press releases. Further, the levels of optmstc and pessmstc language n narratve dsclosures are nether subject to assurance by a thrd-party ntermedary (e.g., audtor) nor subject to ex-post verfcaton (n 5 Numercal dsclosures n earnngs press releases are typcally prepared n accordance wth Generally Accepted Accountng Prncples (GAAP). However, n some cases, frms may also nclude non-gaap metrcs. The SEC s Regulaton G, ssued n November 2002, requres frms to reconcle any non-gaap metrcs dsclosed n earnngs press releases to GAAP-based earnngs. Although Regulaton G apples to dsclosure n earnngs press releases, ts focus s on the dsclosure of non-gaap metrcs n earnngs press releases, not on language usage n earnngs press releases. 7

11 contrast to management earnngs forecasts, for example, whch can be verfed usng actual earnngs realzatons). Therefore, language usage lacks the characterstcs of other voluntary dsclosures whch are subject to mechansms that enhance the credblty of managers dsclosures (Healy and Palepu 2001). Ths argument s consstent wth Frost (1997) who fnds that market partcpants dscount postve-tone dsclosures made by UK frms that receved modfed audt reports 6 and Hutton et al. (2003) who fnd no evdence that narratve dsclosures accompanyng management earnngs forecasts affect securty prces unless those dsclosures are verfable ex-post. Alternatvely, t s possble that managers language usage n earnngs press releases credbly communcates nformaton ncremental to that contaned n the numercal dsclosures of the earnngs press release to help market partcpants develop more accurate expectatons of future frm performance. 7 Ths possblty s consstent wth pror research that has demonstrated a market reacton to causal attrbutons n management earnngs forecasts (.e., reasons underlyng managers earnngs expectatons) (Bagnsk et al. 2000) even after controllng for the underlyng determnants of the attrbutons (Bagnsk et al. 2004); that nformaton about forecast precson s related to stock prce reactons to management earnngs forecasts (Bagnsk et al. 1993; Pownall et al. 1993; and Bagnsk et al 1994); and that the narratve dsclosures contaned n Management s Dscusson and Analyss 6 Frost (1997) nvestgates how managers respond to the nformaton and credblty challenges faced by 81 fnancally-dstressed UK frms that receved audt report modfcatons from She subjectvely codes dsclosure content as: restructurng, fnancng, prospectve, and postve steps, and subjectvely codes dsclosure tone as postve, negatve, or neutral. The nature of the research queston; codng methodology; tme perod examned; characterstcs of sample frms; and sample sze all dfferentate our study from Frost (1997). 7 Although nconsstent wth anecdotal evdence, a thrd alternatve s that managers are non-strategc n ther language usage and use smlar language to dscuss fnancal results, regardless of the favorablty of those results. Untabulated results suggest that although language usage s sgnfcantly correlated over tme, the levels of optmstc and pessmstc language n earnngs press releases do vary wth the favorablty of the nformaton reported for a specfc quarter, such as measures of frm performance. 8

12 (MD&A) nfluence fnancal analysts forecasts (Barron et al. 1999; Clarkson et al. 1999; Bryan 1997). However, unlke MD&A, language usage n earnngs press releases, nformaton about forecast precson, and casual attrbutons are not requred dsclosures, but rather, are ncluded n manageral communcatons presumably to enhance market partcpants understandng of the dsclosures. Language usage n earnngs press releases s further dfferentated from narratve dsclosures accompanyng management earnngs forecasts (.e., causal attrbutons and nformaton about forecast precson). Causal attrbutons and nformaton about forecast precson are sgnals drectly related to the subject matter of managers dsclosure (.e., the management earnngs forecast). In contrast, language usage can be nterpreted as a seres of sgnals about managers expectatons for future frm performance that complement the subject matter of managers dsclosure (.e., the earnngs announcement). Ths nterpretaton s consstent wth research that examnes the use of descrptve language to sgnal product qualty n compettve markets (e.g., Stvers 2005). Our frst research queston nvestgates whether optmstc and pessmstc language usage n earnngs press releases s related to future frm performance. Evdence of a postve (negatve) assocaton between optmstc (pessmstc) language and future frm performance would be consstent wth managers usng levels of optmstc and pessmstc language to provde nformaton to market partcpants regardng managers expectatons of future earnngs. Our second research queston addresses whether the market responds to the levels of optmstc and pessmstc language n earnngs press releases after controllng for the earnngs surprse and other factors ncludng whether the frm beats analysts expectatons or experences negatve earnngs. Evdence of a postve (negatve) relaton between optmstc 9

13 (pessmstc) language and the market s response to earnngs press releases would suggest that nvestors consder managers language usage as a credble dsclosure relevant to developng expectatons for future frm performance and react accordngly. 3. DATA AND SAMPLE SELECTION 3.1 Quarterly Earnngs Press Releases Our ntal sample conssts of 73,758 quarterly earnngs press releases publshed by PR Newswre between January 1, 1998 and December 31, 2003, whch we accessed electroncally usng PR Newswre for Journalsts. We rely on PR Newswre s classfcaton of press releases by subject to dentfy earnngs press releases. To further ensure that our sample ncludes only earnngs press releases, however, we read electronc fles wth sze of less than 2 klobytes and elmnated those fles contanng conference call announcements or other non-earnngs related announcements, resultng n the elmnaton of 1,659 observatons Accountng and Fnancal Market Varables For each earnngs press release n our sample, we requre several accountng and fnancal market varables for use n our analyses. We measure stock returns around the earnngs press release (CAR) as the 3-day (-1 to +1) CRSP sze-adjusted cumulatve return surroundng the earnngs announcement date. We measure the current quarter earnngs 8 It s possble that larger electronc fles are not earnngs press releases. However, when we collect Compustat data, we requre that frms have a report date that falls wthn 3 days of the press release date. Thus, any nonearnngs related press releases that have been msclassfed by PR Newswre wll reman n our fnal sample only f the press release date s wthn 3 days of the report date, whch generally corresponds to the earnngs announcement date. Ths data restrcton ensures that non-earnngs related press releases are unlkely to be ncluded n our fnal sample and thus, unlkely to nfluence our results. 10

14 surprse (SURP) as the scaled dfference between I/B/E/S actual earnngs and the most recent consensus analyst earnngs forecast made pror to the earnngs announcement, where the scalar s the stock prce measured at the begnnng of the current quarter. We defne the dummy varable BEAT to ndcate frms that announced earnngs for the current quarter that met or exceeded analysts expectatons, defned as 1 f SURP 0 and 0 otherwse. For our measures of current and future frm performance, we collect return on assets (ROA) for the current and four subsequent quarters, defned as Compustat earnngs scaled by total assets measured at the begnnng of the quarter for whch ROA s beng measured. To dentfy frms wth negatve earnngs, we defne the dummy varable LOSS to be 1 f Compustat earnngs are negatve and 0 otherwse. Fnally, we collect current quarter Compustat sales (REV) and use ts natural logarthm (LOGREV) as a measure of frm sze. We elmnate any press releases for whch we do not have necessary data avalable on Compustat, CRSP, or I/B/E/S, whch elmnates an addtonal 44,112 observatons. 3.3 Measures of Optmstc and Pessmstc Language Our analyses requre measures of optmstc and pessmstc language usage n each of the quarterly earnngs press releases n our sample. To avod the subjectvty ntroduced by manual codng and to maxmze the sample sze of earnngs press releases to be examned, we employ computerzed textual-analyss software to obtan systematc measures of the levels of optmstc and pessmstc language used n earnngs press releases. In partcular, we use DICTION 5.0 (Hart 2000a, 2001) whch has been used extensvely to analyze narratve dscourse ncludng: speeches of poltcans (Hart 1984, 2000a, 2000b; Hart and Jarvs 1997; Blgh et al and 2004); speeches of Federal Reserve polcymakers (Blgh 11

15 and Hess 2005a; 2005b); annual reports to stockholders (Yuthas et al. 2002); and other busness communcatons (Ober et al. 1999). DICTION s a dctonary-based content analyss program that contans the types of words most frequently encountered n contemporary Amercan publc dscourse (Hart 1984). The use of DICTION has several advantages over human codng of narratve dsclosures n the context of earnngs press releases. Frst, textual analyss technques based on pre-exstng search rules and algorthms are systematc and relable and thus, free from crtcsms of researcher subjectvty and bas that mght be leved aganst human codng. Second, DICTION was desgned for the analyss of poltcal dscourse and as such, s wellsuted for analyzng managers narratve dsclosures whch often share common themes wth poltcal dscourse (e.g., dscussng past, present, and future; dscussng goals and plans; etc.). In partcular, the program s desgned to dentfy subtle aspects of language that even the traned human eye mght not readly perceve (Blgh et al. 2004) and thus, the measures of optmstc and pessmstc language obtaned are lkely to be better calbrated than those subjectvely determned by researchers. Thrd, the use of DICTION allows for a sgnfcantly larger sample sze than would be possble f each earnngs press release was manually read and coded. The prncple dsadvantage of usng DICTION s that although the program counts words characterzed as optmstc or pessmstc based on lngustc theory (Hart 1984, 1987, 2000a, 2000b, 2001), t s ncapable of provdng analyss of language condtonal on the context of the partcular statement. The omsson of context lkely leads to a nosy measure of optmstc and pessmstc language, whch makes detecton of any nformaton content of optmstc and pessmstc language more dffcult. 12

16 We begn our analyss wth pre-exstng word lsts developed for DICTION These word lsts are grounded n lngustc theory and have been used extensvely n academc research n appled felds (e.g., Hart 1984, 2000a, b; Hart and Jarvs 1997; Blgh et al and 2004; Blgh and Hess 2005a; 2005b; Yuthas et al. 2002). DICTION dentfes three word lsts as optmsm-ncreasng, labeled Prase, Satsfacton and Inspraton, and three word lsts as optmsm-decreasng, labeled Blame, Hardshp and Denal. For each earnngs press release, we defne the varable OPT as the percentage of words n the press release (numercal characters are excluded from the calculaton) that are optmsm ncreasng, and the varable PESS as the percentage of words n the press release that are optmsm decreasng. 10 We defne a varable NETOPT as the dfference between our OPT and PESS varables (OPT PESS) to provde a measure of the net optmsm of the language used n the earnngs press release. We also develop an expectatons model for optmstc and pessmstc language, whch requres that we measure OPT, PESS, and NETOPT n the quarter mmedately precedng the current quarter. We label these lagged values LAGOPT, LAGPESS, and LAGNETOPT, respectvely. Hoskn et al. (1986) and Francs et al. (2002b) document that offcer quotatons ncluded n earnngs press releases provde nformaton ncremental to other components of the press release. These fndngs suggest that offcer quotatons may be an mportant component of the narratve dsclosures provded n an earnngs press release. To nvestgate 9 To obtan ncdence counts of the DICTION word lsts and total word counts for our sample of earnngs press releases, as well as to perform all other codng and processng of the earnngs press releases, we used QDA Mner 1.1, wth the Wordstat 4.0 module. 10 The DICTION word lsts used n the computaton of OPT and PESS are summarzed n the Appendx. We made one modfcaton to the DICTION word lsts, whch was to remove the word loss from DICTION s Hardshp word lst. Ths was done to prevent the PESS varable for an earnngs press release from beng mechancally correlated wth whether or not the press release announced negatve earnngs. Our results are qualtatvely smlar when loss s ncluded n the Hardshp word lst. 13

17 the extent to whch our results may be drven by language usage n offcer quotatons, we code all occurrences of a quotaton n each earnngs press release usng computerzed search and codng tools. We then compute OPT, PESS, NETOPT, LAGOPT, LAGPESS, and LAGNETOPT for only those subsamples of narratve dsclosures n earnngs press releases that do not contan offcer quotatons. The requrement that LAGOPT, LAGPESS and LAGNETOPT be measured for each press release elmnates an addtonal 5,262 observatons from our sample. Fnally, we trm any observatons greater than fve standard devatons from the mean for each of the fnancal-market, accountng, and textual-analyss varables used n our analyses, elmnatng a further 762 observatons. 11 The fnal sample used n our analyses s 23,622 frm quarters. 4. RESULTS 4.1 Descrptve Evdence We begn by provdng descrptve evdence regardng the amount of narratve dsclosure contaned n earnngs press releases. To measure the length of press releases, we compute the total number of words (WORDCOUNT) n each press release n our sample. Francs et al. (2002b) document a sgnfcant ncrease n the average length of earnngs press releases from Thus, of partcular nterest s whether ths trend contnues durng our sample perod, Fgure 1 plots the medan value of WORDCOUNT for each year n our sample and shows a large and steady ncrease n the average length of earnngs press releases. The medan value of WORDCOUNT rses approxmately 90% over our sample perod, from We also conducted all analyss usng rank regressons estmated usng the full (untrmmed) sample and obtaned qualtatvely smlar results. 14

18 n 1998 to 1,679 n Table 1 detals the results of a regresson of WORDCOUNT on the tme-trend varable. The trend varable (TREND) records the number of months that have passed between January 1998 (the begnnng of our sample perod) and the date a press release was ssued. 12 Consstent wth Fgure 1, the coeffcent on TREND s postve and hghly statstcally sgnfcant ndcatng that, on average, press releases grew n length by 15 words per month over our sample perod. Table 2 provdes descrptve statstcs on the varables used n our analyses. Our sample ncludes relatvely large frms as ndcated by the mean and medan of REV, $732 mllon and $132 mllon, respectvely. The dstrbuton of REV s also hghly skewed, so we use the natural logarthm of REV n our analyses. The means (medans) of OPT and PESS are 1.28 (1.18) and 0.46 (0.42) respectvely, ndcatng that on average, 1.28% of the words used n earnngs press releases are ncluded n the word lsts consdered to be optmsmncreasng whereas 0.46% of the words are ncluded n the optmsm-decreasng word lsts. Descrptve data also ndcates that 70.7% of our sample frms report earnngs that meet or beat analysts forecasts, whereas 25.5% of our sample frms report negatve earnngs. Table 3 presents the correlaton matrx for varables n our sample. Several of the varables used n our analyses are sgnfcantly correlated wth each other, ndcatng that a multvarate analyss s approprate to nvestgate our research questons. 4.2 Tests of the assocaton between language usage and future frm performance In ths secton we examne whether managers use optmstc and pessmstc language n earnngs press releases to provde nformaton about expected future frm performance to 12 All regresson coeffcent estmates reported n ths paper are based on least squares estmaton, whle reported coeffcent standard errors are heteroskedastcty consstent computed as n Whte (1980). 15

19 the market. We nvestgate ths research queston by testng whether the optmstc and pessmstc language n the current quarter earnngs press release s assocated wth performance metrcs n future quarters. In partcular, we employ a baselne multvarate regresson model for explanng future performance based on that used n Core, Holthausen and Larcker (1999), Bowen, Rajgopal and Venkatachalam (2005), and Koh, Matsumoto and Rajgopal (2005). Future performance s measured as the average of ROA n the four quarters followng the current quarter (FUTROA). The followng model s then used to explan FUTROA: FUTROA = β + β ROA + β σ 5 + β BEAT + β LOSS j β 2 7 j ROA, ID j + + β LOGREV + β SURP k 3 β YEAR 8k k 4 + ε, (1) where σ ROA, s the standard devaton of ROA over the four quarters subsequent to the current quarter, ID j s an ndcator varable takng the value 1 f the press release represented n observaton s for a frm n the j th two-dgt SIC ndustry and 0 otherwse, and YEAR k s an ndcator varable takng the value 1 f the press release represented n observaton was released n year k and 0 otherwse. In equaton (1), ROA s ncluded to capture potental mean reverson n performance metrcs, whle σ ROA, and LOGREV control for the effects of rsk and sze on future performance. SURP, BEAT and LOSS are ncluded to capture the predctve power of other promnent performance benchmarks ncluded n the earnngs press release for future frm performance. Fnally, ID and YEAR capture any ndustry and year fxed effects. To evaluate whether optmstc and pessmstc language contans addtonal predctve power for future performance, we augment equaton (1) wth OPT and PESS: 16

20 FUTROA = β + β ROA + β σ + β LOSS j β 1 7 j ID j + k 2 8k ROA, β YEAR + β LOGREV + β SURP + β BEAT k 3 + β OPT + β PESS ε. (2) In equaton (2), the null hypothess of no predctve power of OPT and PESS for future performance s specfed as the parameter restrcton β = β = Table 4 contans estmaton results for equaton (2), where we have suppressed the estmated coeffcents on the ndustry and year dummy varables for presentaton purposes. The coeffcent on ROA s estmated to be postve and less than 1, consstent wth pror research documentng mean reverson n performance metrcs (e.g., Barber and Lyon 1997). Also consstent wth pror research, e.g. Core, Holthausen and Larcker (1999), the estmated coeffcent on σ ROA, s negatve, whle the estmated coeffcent on LOGREV s postve and statstcally sgnfcant. The estmated coeffcents on SURP and LOSS are also statstcally sgnfcant, and suggest that earnngs surprses and the occurrence of negatve earnngs are both negatvely correlated wth future frm performance. The estmated coeffcents on both OPT and PESS are also ndvdually and jontly sgnfcant, wth hgher values of OPT predctng hgher future performance, and hgher values of PESS predctng lower future performance. Thus, the evdence suggests that optmstc and pessmstc language usage n earnngs press releases s sgnfcantly assocated wth future performance, and that ths assocaton s ncremental to that of a number of other explanatory varables also known to be assocated wth future performance. Next, we test whether OPT and PESS contan dfferental explanatory power for future frm performance. Ths s accomplshed by testng the restrcton β 9 = β10 n 17

21 equaton (2). 13 A Wald test of ths restrcton has a p-value of 0.18 and thus cannot be rejected at conventonal sgnfcance levels. Ths suggests the followng alternatve specfcaton of equaton (2) n whch ths symmetry restrcton s mposed: FUTROA = β + β ROA + β σ + β LOSS j β 1 7 j ID j + k 2 8k ROA, β YEAR + β LOGREV + β SURP k 3 + β NETOPT + ε β BEAT 5 (3) Table 4 also presents the estmaton results for equaton (3), whch are consstent wth those for equaton (2). We construct the measure of future performance, FUTROA, usng over-lappng wndows for a gven frm over tme. Ths lkely ntroduces seral correlaton n model resduals whch would render the coeffcent standard errors mplct n Table 4 nvald. To address ths ssue, we estmate equaton (3) on a subset of our sample obtaned by randomly selectng only a sngle observaton for each frm. The resultng sample contans 3,105 frmquarter observatons. The estmaton results for ths sub-sample are presented n the fnal column of Table 4 whch presents results consstent wth those obtaned usng the full sample. In partcular, all estmated coeffcents are smlar to ther values n the larger sample and reman statstcally sgnfcant at conventonal levels. 4.3 Tests of the market response to optmstc and pessmstc language The results of the prevous secton demonstrate that managers use optmstc and pessmstc language n earnngs press releases to provde nformaton about expected future 13 Note that ths test does not necessarly address whether optmstc or pessmstc language has dfferental mplcatons for future performance. Ths s because OPT and PESS are not necessarly equally accurate n ther measurement of optmstc and pessmstc language. That s, a one unt ncrease n OPT need not capture the same amount of ncrease n underlyng optmstc language as does a one unt ncrease n PESS for pessmstc language. The purpose of ths analyss s then prmarly as a specfcaton test. 18

22 frm performance to the market. In partcular, optmstc and pessmstc language n the current quarter earnngs press release s assocated wth performance metrcs n future quarters. Our second research queston addresses whether the market responds to optmstc and pessmstc language usage n earnngs press releases after controllng for other varables lkely to nfluence the market response to the earnngs announcement. In ths sub-secton we test the null hypothess that there s no ncremental market reacton to the levels of optmstc and pessmstc language n earnngs press releases. We estmate a multvarate regresson model n whch sze-adjusted stock returns n a three-day wndow around the earnngs announcement date are regressed on our measures of optmstc and pessmstc language. To measure the ncremental response to optmstc and pessmstc language, we also nclude control varables n our analyses that are known to have nformaton content ncludng: the earnngs surprse; an ndcator varable dentfyng frms that beat analysts earnngs expectatons; and an ndcator varable dentfyng frms that reported negatve earnngs. The formal specfcaton of the regresson model s as follows: CAR = β 0 + β1surp + β 2BEAT + β 3LOSS + β 4OPT + β 5PESS + ε, (4) where ndexes the frm-quarter observaton and all varables are defned as n Secton Our null hypothess of no market response to optmstc and pessmstc language s then gven as the parameter restrcton β = β = Table 5 presents the estmaton results for the parameters of equaton (4). Consstent wth extant lterature, the coeffcents on SURP and BEAT are both postve and statstcally sgnfcant, whle the coeffcent on LOSS s negatve and statstcally sgnfcant. Further, a Wald test of the null hypothess of no ncremental market response to optmstc and 14 All results presented n ths sub-secton are robust to the ncluson of two-dgt SIC ndustry and year dummy varables n the regresson. 19

23 pessmstc language, that s β = β 0, has a p-value less than 0.01, ndcatng that ths 4 5 = null hypothess can be rejected at the 1% sgnfcance level. Further, the coeffcent on OPT s postve and statstcally sgnfcant at below the 1% level, suggestng that the market responds postvely to the amount of optmstc language contaned n earnngs press releases. The coeffcent on PESS s negatve, although not statstcally sgnfcant at standard levels of sgnfcance. To the extent that managers have reputatons for makng certan types of dsclosures, the market lkely forms an expectaton regardng managers usage of optmstc and pessmstc language n earnngs press releases. If ths s the case, equaton (2) s msspecfed n that t does not dstngush the language usage tself from the language surprse contaned n the earnngs press release. To provde evdence on ths potental msspecfcaton, we use a smple random walk expectatons model to measure the expected and unexpected components of optmstc and pessmstc language. That s, we measure the expected components of optmstc and pessmstc language as: E ( OPT ) = LAGOPT, E ( PESS ) = LAGPESS, where E ndcates an expectaton measured just pror to the earnngs announcement date. The unexpected components of optmstc and pessmstc language are then gven by: OPT E( OPT ) = OPT LAGOPT, PESS E( PESS ) = PESS LAGPESS. Based on ths expectaton model, we reformulate equaton (4) to dstngush between the market response to the expected and unexpected components of optmstc and pessmstc language: 20

24 CAR = β + β SURP + β BEAT + β LOSS + β ( PESS LAGPESS ) + β LAGOPT β ( OPT 4 + β LAGPESS 7 LAGOPT ) + ε. (5) Under the assumpton that equty prces fully reflect the expected porton of optmstc and pessmstc language pror to the earnngs press release, and assumng that a random walk adequately captures market expectatons, we expect that any market reacton to optmstc and pessmstc language would be confned to β 4 and β 5, the coeffcents on the unexpected porton of the language. Table 5 also presents estmaton results for equaton (5). As expected, the estmated coeffcents on the expected porton of OPT and PESS, β 6 and β 7, are statstcally nsgnfcant. By contrast, the estmated coeffcents on the unexpected porton of OPT and PESS, β 4 and β 5, are hghly statstcally sgnfcant and exceed the estmates of β 6 and β 7. Further, whereas the estmated coeffcent on PESS n equaton (4) was statstcally nsgnfcant, the estmated coeffcent on the unexpected porton of PESS n equaton (5) s sgnfcant at the 1% level. We next test whether the documented market response to the unexpected porton of OPT and PESS s symmetrc. In other words, we test the restrcton that β 4 = β 5 usng the estmaton results from equaton (5). A Wald test cannot reject ths restrcton (p-value = 0.90). Our preferred specfcaton s then equaton (6), n whch we mpose the restrcton β = and elmnate the expected portons of optmstc and pessmstc language, whch 4 β 5 were statstcally nsgnfcant, from the model: CAR = β + β SURP + β BEAT + β LOSS + β NETOPT LAGNETOPT ) + ε. (6) ( In equaton (6), the coeffcent β 4 captures the market response to the unexpected porton of the net level of optmstc language n the earnngs press release. The fnal column of Table 21

25 5 presents the estmated parameters from ths regresson. Consstent wth prevously reported results, the estmated coeffcent on β 4 s postve and statstcally sgnfcant, suggestng a postve ncremental market response to the unexpected porton of the net level of optmstc language n the earnngs press release. Taken as a whole, these results suggests that the optmstc and pessmstc language used n the narratve dsclosures of earnngs press releases contans nformaton about future frm performance ncremental to other factors that are commonly assocated wth future earnngs. Ths result suggests that market partcpants consder optmstc and pessmstc language usage to be a credble (at least to some extent) source of nformaton about managers future earnngs expectatons. 15 Fnally, the assocaton between market returns and the unexpected porton of optmstc and pessmstc language s substantally stronger than the assocaton between market returns and the expected porton of optmstc and pessmstc language. Ths result suggests that managers lkely have reputatons for routnely provdng optmstc or pessmstc dsclosures and that the market responds to language usage that dffers from those ntal expectatons. 15 A postve (negatve) and sgnfcant coeffcent on our measures of optmstc and pessmstc language ndcates that there s at least some nformaton gleaned from such language n earnngs announcements that s ncremental to SURP, BEAT and LOSS. It does not rule out the possblty that managers may also use optmstc language opportunstcally n attempt to mslead nvestors and other stakeholders. 22

26 4.4 Robustness Checks and Addtonal Analyss Effects of Language Usage n Offcer Quotatons In ther analyss of addtonal dsclosures n earnngs press releases, Hoskn et al. (1986) fnd an ncremental market response to prospectve offcer quotatons durng the sample perod (.e., 1984). In a study of the ncreased nformatveness of earnngs announcements over tme, Francs et al. (2002b) confrm an ncremental market response to prospectve offcer quotatons for ther sample perod ( ). These fndngs suggest that offcer quotatons are an mportant narratve dsclosure n earnngs press releases. Thus, t s possble that our results are drven by optmstc and pessmstc language usage n offcer quotatons. To assess the extent to whch the language n drect offcer quotatons nfluences our man results, we perform separate analyses on the porton of the earnngs press releases that are not drect quotatons from managers (.e., the non-quote sample). Results from these addtonal analyses are not tabulated, but nferences reman unchanged the coeffcent on NETOPT s n the future performance regresson (equaton 3) and n the market response regresson (equaton 6), and both coeffcents reman sgnfcant (p-values = 0.000). Therefore, optmstc and pessmstc language usage n the non-quote porton of narratve dsclosures n earnngs press releases does not dffer from that used n drect offcer quotatons n terms of ether predctve power for future frm performance or nformaton content Effects of Management Forecasts Included n Earnngs Press Releases Another potental ssue s the extent to whch management earnngs forecasts ncluded n earnngs press releases nfluence our results. Hoskn et al. (1986) fnd that 31% 23

27 of earnngs announcements n ther sample nclude management earnngs forecasts and there s extensve pror research documentng a market response to the news n management forecasts (e.g., Patell 1976; Penman 1980; Waymre 1984; Jennngs 1987; Pownall and Waymre 1989; Pownall et al. 1993; Bagnsk et al 1993, Sknner 1994; Hutton et al. 2003; Bagnsk et al. 2004). If the news n management forecasts s correlated wth optmstc or pessmstc language usage n narratve dsclosures n the earnngs press release, then ncluson of management earnngs forecasts n our sample could lead to a correlated omtted varable n our future performance and returns models. To assess the extent to whch management forecasts are ncluded n the earnngs press releases n our sample, we search the narratve dsclosures of all earnngs press releases ssued n 2003 and classfy earnngs press releases as ncludng management forecasts f the press releases nclude the word gudance. 16 We then test the senstvty of our results to the ncluson of management forecasts by performng analyses on the 1,482 frm quarters n 2003 that we dentfy as contanng management forecasts separately from the 2,693 frm-quarters that do not contan management forecasts. 17 Results from the future performance regressons (equaton 3) ndcate a postve and sgnfcant coeffcent on NETOPT for gudance ( β 9 = and p-value = 0.018) and 16 We base our selecton of the word gudance as an ndcator of the presence of a management forecast on a revew of a random sample of our earnngs press releases across all years. Our revew ndcated that frms dd not use unque and systematc language to descrbe a management earnngs forecasts n the early years of our sample, but began regularly usng the term gudance to descrbe management earnngs forecasts n the latter porton of our sample perod. We thus focus our senstvty analyses on earnngs press releases n To valdate the effcacy of our splt on the word gudance, we read 100 of the earnngs press releases from 2003 to determne whether the presence or absence of the word gudance accurately dentfed whether the press release contaned a management forecast, and found that ths splt accurately classfed the press releases for over 90% of the cases consdered. 17 Ths proporton of the sample that we dentfy as contanng management earnngs forecast (36%) s consstent wth pror research (e.g., Hoskn et al. (1986) fnd 31% of ther sample earnngs releases nclude management forecasts whle Mller (2002) fnds, dependng on frm performance, between 30% and 48% of hs sample ncludes management forecasts). 24

28 non-gudance ( β 9 = and p-value = 0.001) frm quarters. These coeffcents are not sgnfcantly dfferent from one another (p-value = 0.646). Further, results from the market response regressons (equaton 6) ndcate a postve and sgnfcant coeffcent on NETOPT for both gudance ( β 4 = and p-value = 0.027) and non-gudance ( β 4 = and p-value = 0.003) frm quarters. These coeffcents are not sgnfcantly dfferent from one another (p-value = 0.873). Overall, addtonal analyses suggest that management earnngs forecasts ncluded n earnngs press releases do not nfluence our man results and nferences reman unchanged. 5. CONCLUSION Earnngs press releases are the prmary means by whch managers communcate quarterly fnancal results to nvestors and other stakeholders. Although a vast amount of academc research has examned elements of numercal dsclosures n earnngs press releases, very few studes have examned elements of narratve dsclosures contaned n earnngs press releases. To our knowledge, ours s the frst study to examne the role that language usage plays n the credble communcaton of nformaton to nvestors. We argue that elements of narratve dsclosures (e.g., language usage) dffer from elements of numercal dsclosures on several mportant dmensons, ncludng ther nherent subjectvty and the lack of explct regulaton governng ther use n earnng press releases. The purpose of ths paper s to examne whether managers use optmstc and pessmstc language n earnngs press releases to provde nformaton to market partcpants about expected future frm performance, and whether the market responds to optmstc and pessmstc language usage n earnngs press releases. 25