Are there private information benefits to participating in a public earnings conference call?

Transcription

1 Are here privae informaion benefis o paricipaing in a public earnings conference call? William J. Mayew Fuqua School of Business Duke Universiy 1 Towerview Drive Durham, NC [email protected] Web: hp:// Nahan Y. Sharp Mays Business School Texas A&M Universiy 460 Wehner, Mailsop 4353 College Saion, TX [email protected] Mohan Venkaachalam Fuqua School of Business Duke Universiy 1 Towerview Drive Durham, NC [email protected] Web: hp:// Augus 2009 We appreciae helpful commens and suggesions from Larry Brown, Michael Clemen, Yonca Erimur, Jennifer Francis, Richard Willis, he managing direcor and direcor of research a a prominen sell-side research firm, and seminar paricipans a he Duke Accouning mini-brown bag, Fuqua summer brown bag, Texas A&M summer brown bag, and Souheas Summer Accouning Research Conference.

2 Are here privae informaion benefis o paricipaing in a public earnings conference call? Absrac We examine wheher analyss who paricipae in earnings conference calls by asking quesions receive privae informaion benefis relaive o analyss who do no ask quesions. Privae informaion benefis accrue o a paricipaing analys when a manager s response o he analys s quesion uniquely complemens ha analys s privae informaion se. Our evidence is consisen wih paricipaing analyss receiving beneficial privae informaion. Specifically, we find ha he iniial annual earnings forecass subsequen o a conference call are more accurae and more imely for paricipaing analyss. We also find ha paricipaing analyss are more likely o reciprocae by upgrading (no downgrading) a firm s sock recommendaion upon receiving good (bad) earnings news. Our resuls sugges ha managers and analyss coninue o exchange privae benefis in he Pos Regulaion FD era. Key words: Conference calls, privae informaion benefis, financial analyss, Regulaion FD, forecas accuracy, forecas imeliness JEL classificaions: M41, G24, G29, G38, K22

3 Are here privae informaion benefis o paricipaing in a public earnings conference call? 1.0 Inroducion Prior o Regulaion Fair Disclosure (Reg FD), earnings conference calls, where managers discuss he firms resuls and prospecs, were accessible only o he privileged few. Such preferenial access offered significan privae informaion and rading advanages o he invesors and analyss who were allowed access by managemen. To level he informaion playing field across marke paricipans and o forbid preferenial access o managemen, he Securiies and Exchange Commission implemened Reg FD in Ocober Wih a prohibiion on he privae communicaion of maerial informaion, conference calls became open by being accessible o anyone wih eiher a phone line or an inerne connecion (Bushee e al. 2004). However, while all invesors and analyss are allowed o lisen in on he call, only a subse of analyss is allowed by managemen o ask quesions (Mayew 2008). The purpose of his sudy is o invesigae wheher analyss who paricipae in earnings conference calls by asking quesions receive privae informaion benefis relaive o nonparicipaing analyss who do no ask quesions. Our sudy is moivaed by he coninuing debae over wheher Reg FD has achieved is inended objecive o reduce selecive disclosure. Some researchers (e.g., Bushee e al. 2004; Ginschel and Markov 2004; Mohanram and Sunder 2006) have provided evidence consisen wih Reg FD leveling he playing field by curailing selecive disclosure. Despie his evidence, praciioners (e.g., Mayo 2002; Lowengard 2006; Mayo 2006; Erdos and Morgan 2008) and he Securiies and Exchange Commission (Cox 2005) coninue o voice concerns ha unequal conference call access pus nonparicipaing analyss a an informaional disadvanage. Analyss conend ha because heir quesions are being ignored during he quesion and answer porion of he conference call, hey canno generae he insighs necessary o successfully compee in he 1

4 marke for informaion. 1 These concerns are grounded in he heoreical noion ha public informaion can play a complemenary role o an individual s informaion se (Barron e al. 2002; Kim and Verrecchia 1997). In conras, Libby e al. (2008) quesion he exac naure of he benefis o analyss from conference call paricipaion given ha he answers o conference call quesions are immediaely made public. Tha public answers would eliminae any privae informaion benefi o an individual analys is based on he noion ha public informaion subsiues for privae informaion (Verrecchia 1982; Diamond 1985). Our sudy seeks o provide empirical evidence on he exisence and exen of privae informaion benefis ha may accrue o analyss who receive preferenial reamen from managemen by being allowed o ask quesions in public earnings conference calls. Using pos-reg FD earnings conference call ranscrips, represening 1,919 firms and 3,246 analyss beween 2002 and 2005, we firs idenify which analyss from he I/B/E/S populaion paricipaed in he firms quarerly earnings conference calls. We classify analyss as paricipaing (nonparicipaing) if hey ask (do no ask) a quesion on he conference call and invesigae wheher paricipaing analyss accrue more privae informaion benefis han nonparicipaing analyss. Since privae informaion is no observable, direcly measuring he naure of privae informaion a he individual analys level is no possible. 2 We herefore seek evidence 1 One presigious analys, in esimony o he U.S. Senae Commiee on Banking, Housing and Urban Affairs, saed ha even subsequen o Reg FD, managers sill have ools o informaionally handicap cerain analyss using he analogy ha some analyss are sill doing he equivalen of playing baskeball wih one hand behind our back (Mayo 2002). In he appendix we provide an analyic example based on Barron e al. (2002) and Kim and Verrecchia (1997) ha illusraes he poenial advanages of being able o ask a public conference call quesion. 2 Barron e al. (1998) have developed he BKLS measure which proxies for he exen of privae informaion collecively held among all analyss following a firm bu does no accommodae (1) privae informaion measuremen a he individual analys level or (2) wheher such informaion is beneficial. As such, we are unable o use his measure in our empirical ess. 2

5 consisen wih analys possession of beneficial privae informaion. Privae informaion is beneficial o an analys if i is rewarded by he analys s cliens. Analys invesor cliens, who seek o uncover profiable rading opporuniies, no surprisingly value analyss who provide useful and imely informaion (Johnson 2005). Since analyss conend ha conference call access helps hem beer informaionally serve heir cliens, we operaionalize useful and imely informaion via he accuracy and imeliness of he iniial annual earnings forecas issued subsequen o he conference call. Levels, changes, and propensiy score mached sample empirical specificaions provide corroboraing evidence ha he relaive accuracy of paricipaing analyss iniial annual earnings forecass is greaer han ha of nonparicipaing analyss. Paricipaing analyss also offer he marke more imely iniial forecass of upcoming annual earnings following he conference call compared o nonparicipaing analyss. These resuls sugges ha access o managemen hrough quesioning during earnings conference calls helps analyss provide superior informaion o heir invesing cliens. We acknowledge ha, despie using a number of empirical research designs, we canno fully rule ou analys effor as a correlaed omied variable. In oher words, unobservable analys effor may sill explain boh conference call paricipaion and he accuracy and imeliness of analys forecass. To miigae his concern and provide addiional insighs ino he effecs of conference call paricipaion, we consider he economic exchange purpored o occur when managers allow conference call paricipaion. If managers allow analys paricipaion in exchange for he analys mainaining a favorable sock recommendaion on he firm, we should observe recommendaion aciviy in response o analys specific news ha reflecs incenives o mainain rappor wih firm managemen. This predicion is consisen wih he discriminaion 3

6 hypohesis (Francis e al. 2004) ha posis managers exchange privae informaion for favorable sock recommendaions from analyss (Cox 2005; Chen and Masumoo 2006; Lowengard 2006; Mayew 2008). This would imply ha upon receip of bad (good) news abou he firm, paricipaing analyss should be less (more) likely o downgrade (upgrade) han nonparicipaing analyss. Differenial effor is unlikely o resul in his asymmeric predicion because i is unclear why simply puing forh more effor would resul in differenial responses o receiving good and bad news. Consisen wih privae informaion benefis raher han differenial effor, we find ha when updaing recommendaions for firms ha have fallen below (exceeded) each analys s individual quarerly earnings forecas, paricipaing analyss on average are 11% (10%) less (more) likely o downgrade (upgrade) he sock han nonparicipaing analyss over he subsequen 90 days. While our evidence collecively is consisen wih informaion benefis accruing from conference call paricipaion, superior informaion may no be he only benefi for he analys. As Libby e al. (2008) suggess, analyss may enhance heir repuaions wih insiuional invesors by being publicly visible. Empirically i is difficul o direcly measure repuaion enhancemen or oher benefis ouside of he informaion advanage. However, o provide preliminary insighs on his issue, we examine wheher conference call paricipaion is associaed wih analys career oucomes. We find ha paricipaing analyss have lower urnover in he subsequen year relaive o nonparicipaing analyss, consisen wih analyss reaping relaive rewards by obaining conference call access. We view he idenificaion and quanificaion of benefis oher han informaion benefi as an imporan area for fuure research. 4

7 This paper makes several conribuions. Firs, we offer evidence ha conference call access, despie being public, does seem o provide privae informaion benefis o paricipaing analyss. By documening he exisence and exen of hese benefis, we provide relevan insighs o regulaors as hey coninue o evaluae he success of Reg FD in leveling he informaion playing field. Relaedly, our resuls provide evidence for regulaors and invesors alike regarding one poenial driver for he opimism observed in analys sock recommendaions. Since privae informaion benefis are no provided for free by economically raional managers, conference call paricipaion, like invesmen banking ies in he pre-reg FD period (O Brien e al. 2005), becomes an observable proxy for analyss who have incenives o please managemen. Second, we add o he lieraure ha suggess public informaion can have privae informaion benefis. Barron e al. (2002) show ha as a group, analyss possess more privae informaion afer he public release of earnings. Our research idenifies a paricular se of hese analyss, namely paricipaing analyss, who poenially drive he generaion of imporan privae informaion afer he public earnings even. Furher, our resuls corroborae he pre-reg FD findings of Chen and Masumoo (2006) ha analyss wih access o managemen deliver more accurae earnings forecass in he pos-reg FD period. Third, we offer new insighs on he accuracy and imeliness radeoff analyss face when forecasing earnings (Schipper 1991; Guman 2009). While informaion is more useful when i is boh imely and accurae, he lieraure on earnings forecasing has repeaedly documened an inverse relaionship beween accuracy and imeliness, based on he noion ha more accurae forecass are issued as he earnings repor dae approaches. Brown and Mohd (2003) highligh imeliness as one of he single mos imporan deerminans of earnings forecas accuracy. Our finding ha paricipaing analyss are boh more imely and more accurae in heir forecass 5

8 provides suppor for he informaion benefis o paricipaion and is consisen wih he heoreical predicion in Guman (2009) ha analyss wih more precise (i.e. accurae) privae informaion will forecas earlier. Fourh, we begin o answer he quesion posed in Libby e al. (2008) regarding he specificaion of he exac naure of he benefis of conference call access. Our invesigaion should be viewed as a preliminary sep in his direcion. Our resuls poin oward privae informaion benefis as one idenifiable benefi. However, here may be oher benefis as we find ha, incremenal o informaion benefis, paricipaing analyss have more favorable career oucomes in he subsequen year. Finally, his paper complemens recen evidence suggesing managers and analyss coninue o reciprocally exchange privae benefis in he Pos Regulaion FD era. The exan lieraure suggess ha analyss provide favorable sock recommendaions o firms whose managers offer personal favors o analyss (Wesphal and Clemen 2008) and o managers who gran he analys wih lucraive board of direcor posiions (Cohen e al. 2008). We build on Mayew s (2008) conenion ha analyss issue favorable recommendaions o acquire conference call access by documening ha managers reciprocae via privae informaion benefis from call paricipaion. The paper proceeds as follows. Secion 2 reviews he lieraure and develops he hypoheses. Secion 3 oulines he sample selecion, variable measuremen and research design. Secion 4 provides empirical resuls, Secion 5 assesses he robusness of he findings, and Secion 6 concludes. 6

9 2.0 Relaed Lieraure and Hypohesis Developmen The SEC issued Reg FD in Ocober 2000 o level he informaion playing field among analyss and invesors by prohibiing managemen from selecively disclosing maerial informaion o some analyss and no ohers. Under Reg FD, managers mus publicly disseminae maerial informaion, hereby eliminaing any informaion advanage ha an individual analys may have oherwise obained via privae communicaions wih managemen. The noion ha public informaion would subsiue for privae informaion is consisen wih heoreical models of disclosure (Verrecchia 1982; Diamond 1985). Empirically, Bowen e al. (2002) show ha firms choosing o hos conference calls prior o he passage of Reg FD helped level he playing field among analyss, consisen wih public informaion eliminaing he privae informaion advanage of some analyss. Ginschel and Markov (2004) documen ha he informaiveness of analys oupus, in paricular, he price impac of analys forecass, declined subsequen o Reg FD consisen wih effeciveness of Reg FD in reducing selecive disclosure. Mohanram and Sunder (2006) provide addiional evidence ha public conference call access leveled he playing field o some degree in he one year window surrounding he passage of Reg FD, paricularly for non-all-sar analyss. However, a growing number of analyss have suggesed ha a level playing field sill does no exis, ciing differenial conference call access as he culpri (Mayo 2002; Kelly 2003; Davis 2004; Morgenson 2005; SIA 2005; Lowengard 2006; Mayo 2006; Erdos and Morgan, 2008). 3 The SEC has aken noice and has idenified differenial conference call access as a paricular concern (Cox 2005). The economic underpinning of hese concerns is he noion ha 3 Janakiraman e al. (2006) examine wheher he imeliness of analyss firs earnings forecass differs across favored and nonfavored analyss. Their proxy for favored analyss are leaders in providing forecass, i.e., quickes o provide forecass. They repor mixed evidence on wheher Reg FD eliminaed he iming advanage ha favored analyss enjoyed pre-reg FD. 7

10 being able o paricipae in a conference call uniquely serves he paricipaing analys by complemening her exising privae informaion. Assuming analyss ask quesions condiional on heir exising privae informaion, he public answer o such quesions will uniquely complemen he informaion se of he asking analys relaive o analyss who do no ask quesions (see Appendix 1 for a formal derivaion). Boh heoreical and empirical research suggess ha public informaion disclosures, such as earnings announcemens, can indeed faciliae he generaion of uniquely privae informaion (Barron e al. 2002; Kim and Verrecchia 1997). Wheher, and o wha exen, analyss reap benefis from any poenial privae informaion derived from public answers o heir quesions in he pos-fd conference call seing remains an unresolved issue in he lieraure. Consisen wih privae informaion benefis accruing o paricipaing analyss, analys subjecs in Libby e al. (2008) claim ha conference call access, even hough public, is imporan o analyss in analyzing he firm. Mayew (2008) provides evidence consisen wih manages graning more conference call access o more favorable analyss, poenially consisen wih analys paymen for privae informaion benefis. Boh of hese sudies, however, offer caveas regarding wheher one can conclude ha analyss acually receive privae informaion benefis. Libby e al. (2008) noe ha he exac naure of he benefis arising from conference call paricipaion is unclear given ha he answers o conference call quesions are immediaely made public. Mayew (2008) concedes ha managers may gran conference call access o favorable analyss because managers prefer o discuss he firm s prospecs wih analyss who share heir same opimisic view of he firm s fuure. Such aciviy would no necessarily pu nonparicipaing analyss a an informaional disadvanage. 8

11 In his paper we aemp o fill he void in he lieraure on he exisence and exen of privae informaion benefis from paricipaing in he conference call. Privae informaion is beneficial o an analys if i is rewarded by he analys s invesor cliens. Analys invesor cliens, seeking o uncover profiable rading opporuniies, sae hey value analyss who provide hem boh useful and imely insighs abou he fuure prospecs of he companies hey cover (Johnson 2005, Bagnoli e al. 2008). 4 To invesigae wheher conference call paricipaion helps faciliae he generaion of privae informaion of his sor, we operaionalize hese informaion aribues of usefulness and imeliness via he accuracy and imeliness, respecively, of he iniial annual earnings forecas issued subsequen o he conference call. We acknowledge ha insiuional invesors do no value aggregae analys oupus like sock recommendaions and earnings forecass per se as highly as individual insighs abou a firm s value drivers (Johnson 2005; Bagnoli e al. 2008). 5 As such, our proxies for beneficial privae informaion based on properies of he iniial annual earnings forecass provided immediaely subsequen o he conference call are consruced under he assumpion ha hese earnings forecass properies are correlaed wih he informaional insighs ha insiuional invesors find valuable. If asking a conference call quesion yields privae informaion benefis, he iniial forecass of fuure earnings issued by paricipaing analyss afer he conference call should be boh more accurae and more imely relaive o nonparicipaing analyss. Saed formally: 4 Guman (2009) uses his assumpion o model he imeliness of analys forecass, noing ha invesors benefi from analys insighs by receiving early access o informaion ha can be used o make rading decisions. 5 Bagnoli e al. (2008) provide a lis of analys aribues insiuional invesors value. During he pos-fd period analyzed, providing useful and imely calls ranked 4 h while earnings esimaes and sock selecion ranked beween 9 h and 12 h. Consisen wih his noion, Groysberg e al. (2008) noe ha he properies of earnings forecass such as accuracy and imeliness are no explicily par of analyss compensaion conracs. In privae discussion, he managing direcor and direcor of research a a prominen sell-side research firm also noed ha insiuional invesor cliens have heir own personnel o map informaion ino projecions of earnings and sock recommendaions and herefore valued individual insighs from sell-side analyss more han heir aggregaed overall opinions abou fuure earnings and firm value. 9

12 H1a: Cerius paribus, paricipaing analyss iniial annual earnings forecass following he conference call are more accurae han nonparicipaing analyss forecass. H1b: Cerius paribus, paricipaing analyss iniial annual earnings forecass following he conference call are more imely han nonparicipaing analyss forecass. 3.0 Sample Selecion, Research Design and Variable Measuremen 3.1 Sample Our empirical analysis uses earnings conference call ranscrips from he Thomson SreeEvens daabase. We firs exrac he 27,497 quarerly earnings conference call ranscrips available on SreeEvens beween July 2001 and March 2005 for which we could obain a firm idenifier in I/B/E/S. Our sampling period begins in July 2001 because his is he incepion of he SreeEvens daabase coverage of earnings conference calls. The sample ends in March 2005 because his was he las dae we were able o obain he I/B/E/S analys and broker ranslaion files. I/B/E/S ranslaion files faciliae he mapping of analys names and brokerages from he ranscrips o he numeric codes for he respecive analys and brokerage in I/B/E/S. Since 2005, I/B/E/S sopped providing hese ranslaion files o researchers. From our iniial exracion of ranscrips, we remove 835 firm quarer observaions where I/B/E/S did no lis a leas one idenifiable analys wih boh an ousanding quarerly earnings forecas for he quarer in quesion and an ousanding recommendaion as of he conference call dae. 6 The analys following associaed wih his resuling se of 26,662 firm quarer observaions represen our proxy for he poenial populaion of analyss who could paricipae in he firm s quarerly earnings conference call. 7 From each conference call ranscrip, we proceed 6 We do no impose any resricions regarding saleness of eiher he quarerly earnings forecas or he sock recommendaion a his poin in he sample selecion process. 7 The heoreical poenial se of paricipans include I/B/E/S analyss who cover he firm, I/B/E/S analyss who do no cover he firm, analyss no on I/B/E/S, bankers, insiuional invesors and individual invesors. Idenifying his heoreical populaion and measuring he naure of he informaion ses conained by paricipans oher han I/B/E/S analyss following he firm is cos prohibiive. Mayew (2008) documens ha a he median, managers ake 10

13 o exrac he name and broker affiliaion of each analys who asked a quesion during he conference call. Using he I/B/E/S ranslaion files, we hen code a paricipaing analys as one who asked a quesion on he firm s quarerly earnings conference call. Separaely, we obain individual analys annual earnings forecas daa from he I/B/E/S deail file. We focus on annual forecass because analyss commonly issue hem muliple imes during a fiscal year, which allow us o empirically measure revision aciviy around conference call evens and conduc a changes analysis. We were able o obain annual earnings forecass for 149,210 analys firm quarers where he firm announces earnings wihin 45 (90) days of he quarer (year) end, and he analys issues a one-year-ahead annual earnings forecas wihin 90 days boh before and afer each quarerly earnings announcemen beween July 1, 2001 and March 30, We resric our forecas sample o firms ha have a leas hree analyss following he firm in order o calculae meaningful relaive measures in our empirical ess. This reduces he forecas sample o 138,216 analys firm quarers. Combining he earnings forecas sample wih he conference call sample yields 71,542 analys firm quarer observaions. Eliminaing all firmquarers during our period where he conference calls have no variaion in he paricipaing saus of analyss (i.e., eiher all analyss asked quesions or no analyss asked quesions) reduces he sample o 57,449 analys firm quarers. Finally, requiring a measure of analys forecas frequency over he preceding calendar year reduces our final sample o 57,443 analys firm quarers observaions, which represen coverage of 8,516 firm quarer conference calls of 1,919 unique firms by 3,246 unique analyss from 265 unique brokerages. Alhough he conference call ranscrip daa is available from July 2001, our final sample begins in he firs quarer of quesions from 9 non-corporae paricipans, firms are covered by 6 I/B/E/S analyss, and 3 I/B/E/S analyss ask quesions. 11

14 2002 because (i) in he early periods conference call ranscrips were sparsely populaed and (ii) sample resricions due o I/B/E/S daa availabiliy resul in no observaions for Our sample selecion process yields a sample of analyss who are likely o have ineres in covering he firm and mainain such ineres afer he conference call even, since we require recen analys forecasing aciviy boh before and afer he call Research Design Accuracy of annual earnings forecass We invesigae wheher he paricipaing analyss issue more accurae iniial annual earnings forecass han nonparicipaing analyss as prediced in H1a using boh a levels and changes analysis. Our levels analysis follows he exan lieraure (Clemen and Tse 2003; Clemen and Tse 2005; Ke and Yu 2006) and esimaes he deerminans of relaive accuracy using a pooled cross-secional OLS model wih sandard errors clusered by analys: pos ACC _ R i = β, j, 0 + β 1 Paricipae i,j, + β 2 F_Exper_R i,j, + β 3 T_Exper_R i,j, + β 4 LnFollow i,j, + β 5 Firms_ R i,j, + β 6 Broker_R i,j, pos pre + β 7 Horizon _ R i + β, j, 8 ForFreq_R i,j, + β 9 ACC _ R i, j, + ε i,j, (1a) The dependen variable, pos ACC _ R i, j,, is he forecas accuracy of analys i s firs forecas revision of firm j s annual earnings issued afer quarer s earnings announcemen relaive o oher analyss forecasing for firm j. Formally, pos absferank pos i, j, 1 ACC _ R i, j, = 100 x100, Follow 1 i, j, where absferank pos is he rank of he absolue forecas error of an analys s iniial forecas of upcoming annual earnings afer he conference call among he forecasing analyss and Follow is he number of analyss issuing such a forecas. absferank akes on low values for small forecas errors and high values for large forecas errors, yielding a range for ACC _, beween 0 and pos R i, j 8 This works agains finding resuls if lack of access is so derimenal ha analyss drop coverage and hence are excluded from our sample. 12

15 100, where higher scores indicae more accurae forecass. The use of relaive forecas accuracy insead of raw forecas accuracy helps alleviae differences in forecasing environmens ha can exis across firms and accommodaes differen levels of analys following across firms (Ke and Yu 2006; Hong and Kubik 2003). Paricipae i,j, is an indicaor variable ha equals 1 if analys i asked a quesion on firm j s quarer conference call, and zero oherwise. H1a predics β 1 > 0. Drawing appropriae inferences on β 1 requires conrols for analys effor, since effor could explain boh conference call paricipaion and beer forecas accuracy. Unforunaely, effor is unobservable and so we proxy for effor in wo ways. 9 Firs, we include primiive facors ha influence an analys s cos of effor, and expec accuracy o be increasing in each of he following aribues: overall and firm-specific experience (T_Exper_R and F_Exper_R ), and relaive resources as proxied by relaive broker size (Broker_R). We also expec relaive accuracy o be decreasing in he analys s relaive porfolio size (Firms_R), because as porfolio size increases analys effor mus be disribued across a larger number of firms. All of he above analys characerisic variables are compued in relaive form as follows: Characerisic_R = Characerisic _ revrank 100 Follow 1 i, j, i, j, 1 x100, where Characerisic = T_Exper, F_Exper, Broker, and Firms. Characerisic_revrank is he reverse ranking of each characerisic yielding higher ranks for higher values on each characerisic. Characerisic_R, like relaive accuracy, ranges from 0 o 100 and capures he exen o which an analys differs on hese characerisics relaive o oher analyss following he same firm. 9 While we can observe which analyss acually asked a quesion, we canno observe which analyss exered effor o ener he quesion queue. Condiioning he sample on only hose analyss who enered he quesion queue would be he ideal research design, because being in he queue would sugges ha he analys was likely aenive o he conference call discussion and ineresed in obaining informaion from he manager. In our sample, we rea analyss who poenially enered he quesion queue bu never go o ask quesions as nonparicipaing. This misclassificaion, if anyhing, would bias agains finding evidence in suppor of our hypoheses. 13

16 Second, we include wo oupu measures ha capure effor, boh of which are expeced o be posiively relaed o relaive accuracy. In paricular, we include he relaive forecasing frequency of he analys during he year prior o he quarer earnings announcemen pre (ForFreq_R) and he relaive accuracy ( ACC _ R i, j, ) of he analys s las forecas of annual earnings issued immediaely before he quarer earnings announcemen. Boh measures proxy for how relaively acive and accurae he analys has been in covering he sock prior o he earnings announcemen. We also include a relaive measure of he disance from he earnings forecas issued immediaely afer he conference call ( Horizon_ pos R ). Forecass wih shorer horizons have he advanage of incorporaing more informaion abou upcoming earnings (including he informaion conained in he forecass of oher analyss) han forecass issued a longer horizons. We predic a negaive associaion beween relaive horizon and relaive accuracy. Finally, we include a proxy for analys compeiion (LnFollow) ha serves o conrol for measuremen error in our relaive analys measures for firms wih lower levels of analys following (Hong e al. 2000; Ke and Yu 2006). Noe ha we do no include measures of bias in earnings forecass issued prior o he conference call. The lieraure commonly measures bias in preceding forecass as a predicor of fuure forecas accuracy under he noion ha bias proxies for access o managemen (Ke and Yu 2006) and faciliaes conference call access (Libby e al. 2008). We measure he inended oucome of such bias managemen access via conference call paricipaion direcly via he variable Paricipae, which would make he inclusion of prior forecas bias redundan Chen and Masumoo (2006) repor ha relaive forecas accuracy of analyss issuing more favorable recommendaions is greaer han ha for analys issuing less favorable recommendaions. In heir paper, he favorableness of analys recommendaions is a proxy for managemen access. We do no include he favorableness 14

17 We also refrain from including oher characerisics of he earnings forecas issued immediaely afer he conference call, such as boldness (Gleason and Lee 2003; Chen and Jiang 2005; Clemen and Tse 2006). Boldness is commonly aribued o he possession of superior privae informaion. If conference call paricipaion helps generae his superior privae informaion, including boldness as an explanaory facor would have he effec of conrolling away he effec we are aemping o examine. 11 Our changes analysis esimaes he associaion beween changes in relaive forecas accuracy and changes in conference call paricipaion using he following pooled cross-secional OLS model wih sandard errors clusered by analys: Δ ACC _ R i = α, j, 0 + α 1 Paricipa e i, j, Δ + α 2 Δ Horizon _ R i + μ, j, i,j, (1b) where pos pre Δ ACC _ Ri, j, = ACC _ Ri, j, ACC _ R, i, j, ΔParicipa ei, j, = Paricipaei, j, Paricipaei, j, 1, and Δ pos pre Horizon _ Ri, j, = Horizon_ Ri, j, Horizon_ Ri, j,. The dependen variable ΔACC_R reflecs he change in he relaive forecas accuracy for forecass of annual earnings ha sraddle he conference call. ΔParicipae reflecs he change in access o managemen, which is only defined for analyss wih available daa for consecuive conference calls. H1a predics ha more (less) access will yield increases (decreases) o relaive accuracy, implying α 1 > 0. ΔHorizon reflecs he change in he relaive ime span in beween he annual forecass ha sraddle he conference call. Less negaive values of ΔHorizon imply shorer ime span in beween forecass, which in urn implies smaller increases in accuracy due o he arrival of informaion (i.e. α 2 < 0). The remaining analys specific facors are expeced o be consan during he period beween he of analys recommendaions in our model because we measure access o managemen direcly, which renders he analys recommendaion redundan. Neverheless, as a sensiiviy check, we include he ousanding sock recommendaion of each analys prior o he earnings announcemen in our specificaions and our inferences remain unchanged. 11 Including he boldness of analys forecass as defined in Ke and Yu (2006) in our empirical models does no impac our inferences. 15

18 las forecas prior o he conference call and he firs forecas afer he conference all. As such, hose facors are no included in equaion (1b). The changes specificaion helps miigae he poenial for consan, unmeasurable effecs from confounding he associaion beween relaive forecas accuracy and conference call paricipaion Timeliness of annual earnings forecass We now urn o a levels analysis of H1b o invesigae wheher paricipaing analyss issue more imely forecass. Empirically, we model he duraion o forecas as he number of calendar days beween quarer s earnings announcemen dae and he dae he analys provides an earnings forecas o I/B/E/S. We employ a Cox proporional hazard esimaion wih he hazard rae a ime is defined as: We define he hazard rae as a funcion of he baseline hazard (h 0 ) a ime and he effecs of he following explanaory variables hazard rae a ime as follows, wih sandard errors clusered by analys: probabiliy of failing beween imes and + δ h( ) = ( δ ) ( probabiliy of failing afer ime) h() = h 0 () exp(δ 0 ) + δ 1 Paricipae i,j, + δ 2 F_Exper_R i,j, + δ 3 T_Exper_R i,j, + δ 4 LnFollow i,j, + δ 5 Firms_ R i,j, + δ 6 Broker_R i,j, pre δ 7 ForFreq_R i,j, + δ 8 ACC _ R i, j, + μ i,j, (2a) If conference call paricipaion faciliaes faser issuance of informaion o cliens, we expec δ 1 > 0. A posiive coefficien esimae corresponds wih a higher (i.e., more rapid) hazard rae, which would indicae quicker revision of he forecas for paricipaing analyss relaive o nonparicipaing analyss. We include he same effor proxies from he relaive accuracy model because analyss who pu forh more effor are boh more likely o paricipae in a conference call and revise heir forecass earlier, wih one excepion. We no longer include he relaive forecas 16

19 horizon of he firs forecas issued afer he conference call as an explanaory variable. Horizon is a funcion of he ime of he firs earnings forecas issued afer he conference call, which is he duraion we are modeling in equaion (2a). Our changes specificaion models he change in he ime i akes an analys o issue an annual earnings forecas afer consecuive conference calls as follows: Δ Delay i, j, = γ 0 + γ 1 Paricipa e i, j, Δ + η i,j, (2b) whereδdelay i,j, =Delay i,j, Delay i,j,-1, where Delay i,j, equals he number of days elapsed beween firm j s quarer earnings announcemen and analys i s firs subsequen forecas of annual earnings, and Delay i,j,-1 is he number of days ha elapsed beween analys i s firs forecas of annual earnings afer firm j s quarer -1 earnings announcemen. ΔParicipae is as defined previously. As wih he changes specificaion for forecas accuracy (equaion 1b) we do no include oher analys or firm specific variables. If increased (decreased) access o managemen during he conference call decreases (increases) he ime-o-marke of an annual earnings forecas as H1b predics, we expec γ 1 o be negaive. 4.0 Resuls 4.1 Descripive Saisics and Univariae Resuls Panel A of Table 1 provides descripive saisics for our sample. To faciliae inerpreaion we provide raw values in addiion o he relaive measures used in our empirical models. The median analys in our sample has five years of overall experience, covers en firms, has wo years experience on firms covered, and issues five annual forecass over he one calendar year leading up o he conference call. The median brokerage size is 53 analyss, which is much larger han he average brokerage size repored in he exan lieraure (Clemen and Tse, 2003; Clemen and Tse 2005). This reflecs our condiioning he sample on very acive analyss, which 17

20 end o be hired by larger brokerages. Conference call paricipaion occurs for 47% of our sample analys firm quarer observaions. Panel B of Table 1 reveals ha our 1,919 sample firms followed closely mimic he populaion of Compusa in erms of indusry concenraion. Panel C of Table 1 reveals he disribuion of our 8,516 earnings conference calls by calendar quarer. The number of conference calls examined in our sample grows from Q1 of 2002 hrough Q3 of 2003, leveling off a roughly 850 conference calls each calendar quarer hereafer. The number of calls grows iniially as he SreeEvens daabase grew in populariy, reaching a seady sae in he middle of 2003 (Mayew 2008). Panel D of Table 1 presens he correlaion saisics and iniial evidence on our hypoheses. Spearman correlaions reveal ha analyss who paricipae deliver annual earnings forecass ha are boh more imely and more accurae (ρ(acc_r pos, Paricipae) = 0.01, p<0.01); ρ(delay, Paricipae) = -0.06, p<0.01). While suggesive, using hese correlaions o draw conclusions would be premaure because a number of oher analys aribues are associaed wih conference call paricipaion ha have been shown o also influence he accuracy and imeliness of earnings forecass. In paricular, Spearman correlaions also reveal ha paricipaing analyss have relaively more overall and firm experience, cover relaively more firms, work for relaively larger brokerages, and issue relaively more forecass on he firms hey cover. In our mulivariae analysis ha follows, we explicily incorporae conrols for hese facors in our empirical design. 4.2 Mulivariae Resuls Our mulivariae analysis of forecas accuracy is presened in Table 2. Consisen wih H1a, Panel A of Table 2 reveals ha analyss who paricipae in he conference call provide 18

21 significanly more accurae forecass relaive o oher analyss following he firm (Paricipae = 0.524, p<0.01). This resul obains afer conrolling for oher known deerminans of relaive forecas accuracy. The conrol variables, when significan, behave as prediced. As expeced and shown in prior lieraure (Clemen 1999; Brown 2001; Brown and Mohd 2003), he relaive accuracy of he analys immediaely prior o he conference call and he relaive horizon of he earnings forecas are he mos poen predicors of relaive forecas accuracy (Acc_R pre = 0.373, p<0.01; Horizon_R pos = 0.347, p<0.01). Analyss who follow relaively more firms (work for larger brokerages) are less (more) accurae (Clemen and Tse 2003). 12 The changes analysis repored in Panel B of Table 2 buresses our findings in Panel A. The number of observaions used for esimaion in Panel B is smaller han hose used in Panel A because we require he same analys coverage on consecuive conference calls in order o consruc our change variables. 13 As prediced, we find ha changes in conference call paricipaion are posiively and significanly associaed wih changes in relaive forecas accuracy (ΔParicipae = 0.775, p = 0.019). The coefficien on he change in relaive horizon is significanly negaive (ΔHorizon_R = , p<0.001), as expeced. Assuming we have conrolled sufficienly for analys effor, he resuls hus far sugges analyss who are allowed o paricipae in conference calls provide more accurae earnings forecass. These resuls are consisen wih Chen and Masumoo s (2006) evidence from he pre Regulaion FD era ha access o managemen improves he forecas accuracy of financial 12 An alernaive explanaion for hese resuls is ha Paricipae simply proxies for analyss who issue walkdown forecass of annual earnings (Richardson e al. 2004; Ke and Yu 2006). If analyss walkdown heir annual forecass, consecuive annual forecass made early in he year will become consisenly more accurae, and a some poin become consisenly less accurae as he forecass move from opimisic o pessimisic. To rule ou his explanaion, we measure wheher each analys during a fiscal year walks down annual earnings forecass in perfec foresigh. Tha is, we observe he firs forecas and las forecas of annual earnings for a given fiscal year for each firm and creae an indicaor variable ha equals one if he firs (las) forecas is opimisic (pessimisic) relaive o repored earnings. Including his indicaor variable does no qualiaively change he inferences. 13 Esimaing our levels analysis wih he smaller number of observaions used in he changes analysis yields qualiaively similar resuls o hose presened in Panel A of Table 2. 19

22 analyss. However, evidence from our pos FD sample suggess he economic significance is no very large. Recall ha relaive accuracy is bounded beween 1 and 100. Evidence from he levels (changes) specificaion sugges ha paricipaion improves relaive accuracy by only (0.775) unis, which is quie small. Despie he small magniude i would be premaure o conclude ha he accuracy difference associaed wih conference call paricipaion is no economically meaningful, for a leas wo reasons. Firs, his esimae is likely a lower bound. We can only measure differenial accuracy effecs for analyss who coninue o follow he firm. Analyss who know hey are likely o be denied paricipaion (such as hose analyss wih unfavorable views of he firm) may drop coverage and herefore no show up in our sample (McNichols and O Brien 1997, Mayew 2008). 14 Second, he economic rewards o an analys from heir cliens for even a small difference in accuracy may be large when coupled wih how quickly an analys can provide he forecas o he markeplace. Tha is, we canno draw conclusions on he economic impac of accuracy wihou also considering he imeliness wih which such an incremenally accurae forecas is delivered. We now urn o our assessmen of forecas imeliness. Panel A of Table 3 presens resuls from he hazard model specified in equaion (2a). The coefficien on Paricipae is significanly greaer han zero (δ 1 = 0.052, p<0.001), implying ha paricipaing analyss provide heir forecass o he marke more quickly han nonparicipaing analyss. Using hazard raios o describe he economic inuiion, our resuls indicae ha, condiional on no having provided an earnings forecas a ime, paricipaing analyss are 5.4 percen more likely o provide a forecas a ime +δ han nonparicipaing analyss (hazard raio is 1.054). 14 If analyss had unlimied flexibiliy in choosing heir porfolio, hey could poenially only follow firms where hey would be allowed o paricipae in he conference call. However, as a pracical maer, analyss generally are indusry expers who canno avoid covering cerain bellweher firms in he indusry (Graham e al. 2005). 20

23 In erms of conrol variables, no surprisingly, relaively more accurae analyss immediaely preceding he call, analyss working for relaively larger brokerages, and analyss who forecas more frequenly all deliver forecass significanly more quickly o he marke. The posiive and significan coefficien on LnFollow is consisen wih analyss providing more imely forecass when he firm has a rich informaion environmen as well as siuaions where analyss face siffer compeiion from oher analyss. Surprisingly, analyss wih relaively more firm experience and who follow relaively fewer firms deliver forecass o he marke more slowly, afer conrolling for oher facors. However, if heighened focus on an individual firm proxies for analyss wih higher abiliy o learn abou he firm, hese resuls are in fac consisen wih Guman (2009), who shows analyically ha analyss wih higher learning abiliy will forecas earlier. Alhough an advanage of he Cox proporional hazard mehod of duraion analysis is ha i is insensiive o he specificaion of a funcional form for he baseline hazard funcion, assumpions underpinning hazard models esimaion may be violaed. Furher, like he levels model esimaed for relaive forecas accuracy, correlaed omied variables coninue o be concern. As such, we urn o Panel B of Table 3 where we esimae he change, across consecuive conference calls, in he delay beween forecas issuance and he earnings announcemen. 15 We find ha changes in paricipaion reduces he delay in forecas issuance (γ 1 = , p=0.035). This implies ha analyss who gained (los) paricipaion in he conference call deliver heir forecass o he marke 0.82 days faser (slower). Given he changes in Delay 15 While hazard models are mos appropriae for modeling he amoun of ime ha passes beween evens, in unrepored analysis we also use OLS o esimae he relaive delay in he iniial forecas of analyss afer he conference call as a funcion of he same deerminans in he hazard model. We find negaive and significan coefficien on Paricipae (coefficien = , p<0.001), consisen wih paricipaing analyss providing a forecas o he marke wih relaively less delay han nonparicipaing analyss. We also model changes in relaive delay as a funcion of changes in conference call paricipaion and observe a coefficien on ΔParicipae (coefficien = , p<0.001), consisen wih increased (decreased) access o managemen shorening (lenghening) he relaive ime i akes o issue an earnings forecas. 21

24 are measured over consecuive quarerly conference calls, he exclusion of oher analys, broker and firm specific characerisics is appropriae since i is unlikely such characerisics vary in during he 90 days beween consecuive earnings conference calls. In economic erms, 0.82 days equaes o roughly hours, which anecdoally represens a significan advanage. When discussing he ime delay managers can impose on analyss by no answering heir quesions, Lowengard (2006) saes: for an analys looking o pu ou a fas noe, four hours may as well be 400. Addiionally, our confidenial review of a policies and procedures manual from one large global invesmen banking firm noed he imporance of preparing an analysis of a firm s earnings as quickly as possible afer he earnings conference call wih he objecive of being he firs o noify cliens, relaive o oher analyss. We also reierae ha his esimae of ime delay likely represens a lower bound on he effecs of conference call paricipaion. To summarize, he resuls hus far provide evidence consisen wih analyss deriving privae benefis from conference call paricipaion. In paricular, boh levels and changes models provide saisical evidence ha paricipaing analyss deliver more useful and imely informaion producs o heir cliens, as evidenced by he accuracy and imeliness of heir earnings forecass. This finding is in conras wih he evidence in prior lieraure ha analyss, on average, rade off accuracy and imeliness when making a forecas (Schipper 1991; Brown and Mohd, 2003). Our resuls imply ha paricipaing analyss face less of a rade off when compared o he nonparicipaing analys since paricipaing analys forecass are boh more imely and more accurae. This resul is also consisen wih he heoreical predicion in Guman (2009) ha analyss wih higher precision of privae informaion will forecas earlier. 22

25 5.0 Robusness and Addiional Analyses 5.1 Self Selecion and Propensiy Score Maching A limiaion of he regression approach o draw inferences abou he benefis o conference call paricipaion is ha i assumes a linear funcion form. In addiion, paricipaion in a conference call is no random (Mayew 2008). Alhough we conrol for oher facors ha deermine conference call paricipaion in he regression specificaion, i may no compleely eliminae poenial sample selecion bias. 16 The changes specificaion parially addresses his issue bu imposes an assumpion ha he facors ha deermine paricipaion are consan across ime. To miigae selecion bias and examine wheher he paricipaion (reamen) effec is robus, we use a propensiy score maching procedure (Rosenbaum and Rubin 1983) where we idenify a mached se of analyss who did no paricipae in a conference call bu who would have been oherwise allowed o paricipae in a conference call (i.e., would have fallen under he reamen group) given observable characerisics. Alhough here are several versions of he maching algorihm in he lieraure, we use a simple neares-mach mehod. This maching procedure involves wo seps. Firs, we deermine a propensiy score for each analys which is he condiional probabiliy of receiving he reamen effec (i.e., he probabiliy ha an analys ges o paricipae) given a se of observable characerisics ha deermine paricipaion. Tha is, we obain he propensiy score by esimaing a logisic regression using he enire sample of paricipaing (reamen) and nonparicipaing (conrol) analyss. Second, for each of he paricipaing analyss we idenify a nonparicipaing analys wih he closes mach, wihou 16 Mayew (2008) models he probabiliy ha an analys asks a quesion on he conference call. The ideal selecion model would model he choice by analyss o ener he quesion queue of he conference call. Afer conrolling for analys self-selecion ino he quesion queue, analyss allowed o ask quesions would clearly be solely a funcion of managerial choice, and we would proceed o examine differences in he characerisics of analys oupus. Unforunaely, he quesion queue is unobservable making such a specificaion impossible. 23

26 replacemen, in erms of he propensiy score. To ensure comparabiliy beween he reamen and he conrol groups, we drop observaions where we are unable o find a reasonable nonparicipaing analys mach for a paricipaing analys, i.e., where he difference in propensiy score is more han Following Mayew (2008) we esimae he following pooled logisic regression o deermine he condiional probabiliy of paricipaion by an analys a a firm s quarerly conference call: Paricipae i,j, = β 0 + β 1 SBuy i,j, + β 2 Buy i,j, + β 3 Sell i,j, + β 4 SSell i,j, +β 5 QAmin i,j, + β 6 LnFollow i,j, + β 7 AllSar i,j, + β 8 ACC_R pre i,j, + β 9 F_Exper_R i,j, + β 10 T_Exper_R i,j, +β 11 Inds_R i,j, + β 12 ForFreq_R i,j, + β 13 Broker_R i,j, +β 14 Firms_R i,j, + β 15 CCuser i,j, +β 16 PriorParicipae i,j, + β 17 RecHorizon i,j, + υ i,j,. (3) The dependen variable, Paricipaei,j,,, as defined earlier, is an indicaor variable ha represens wheher he analys asked a quesion during he conference call. SBuy, Buy, Sell, and SSell are indicaor variables ha capure he analys s mos recen ousanding sock recommendaion prior o he conference call. QAmin is he lengh of he quesion and answer porion of he call in minues, where minues are derived from oal word coun of he conference-call ranscrip a 150 words per minue. LnFollow, ACC_R pre, F_Exper_R, T_Exper_R, ForFreq_R, Broker_R, and Firms_R are defined as in equaion (1a). AllSar indicaes wheher he analys was included on any of he Insiuional Invesor Research All- American eams in he mos recen prior year, Inds_R is he analys s relaive indusry coverage, and CCuser is he oal number of conference calls (excluding firm j) in which analys i paricipaed during he calendar quarer conaining fiscal quarer for firm j. Finally, PriorParicipae indicaes wheher he analys asked a quesion on any of firm j s prior 17 Relaxing he resricion of he difference in propensiy score from.01 o.05 does no impac our inferences. 24

27 conference calls in he sample, and RecHorizon measures he number of days beween he conference call dae and he dae of he analys s mos recen sock recommendaion. Panel A of Table 4 presens he resuls of esimaing equaion (3). All independen variables are saisically significan in he same direcion as documened in Mayew (2008), wih he excepion ha prior accuracy is posiive bu no saisically significan, while relaive broker size is posiive and saisically significan. The overall pseudo R 2 is 14.2%, which is of comparable magniude o he 20.0% documened in Mayew (2008). Collecively, he behavior of he independen variables and model fi sugges we are able o successfully replicae he selecion model of Mayew (2008) for our sample. Using he coefficiens from equaion (3), we compue he propensiy score for each observaion as he prediced probabiliy ha an analys paricipaes in he conference call. This forms he basis for consrucing mached pairs using he neares-mach mehod described earlier. Because we resric our sample o pairs wih a near perfec mach on propensiy scores (where he difference in propensiy scores is less han 0.01), we increase he likelihood of covariae balance, i.e., he similariy in he disribuion of he reamen and he propensiy score mached conrol samples. I is no surprising ha he mean (median) of he propensiy scores for he reamen and conrol samples is no saisically differen (see Panel B of Table 4). To furher assess covariae balance we es wheher he means (median) of he independen variables in equaion (3) are differen beween he reamen and conrol samples. Wih he excepion of LnFollow and Firms_R, we observe no saisical differences across he samples (resuls no repored) suggesing ha our maching scheme was reasonably successful in ensuring covariae balance. 25

28 Comparing he main variables of ineres, forecas accuracy (ACC_R pos ) and imeliness (DELAY) across he wo samples, Panel B of Table 4 reveals ha paricipaing analyss are boh more accurae and imely han nonparicipaing analyss. The mean differences are also consisen wih resuls documened in Tables 2 and 3. Wih respec o forecas accuracy, paricipaing analyss are saisically beer han nonparicipaing analyss by unis (p = 0.05), which is comparable o he unis repored in Panel B of Table 2. Wih respec o imeliness, paricipaing analyss deliver forecass saisically faser by days (p<0.01) which is comparable o days repored in Panel B of Table 3. Collecively, he resuls in Table 4 indicae ha our findings are quie robus. 5.2 Analys effor as a compeing explanaion In our research design, we have aemped o rule ou he lack of effor as a compeing explanaion in hree ways. Firs, our sample selecion requires ha all analyss issue a forecas wihin a 90 day period boh before and afer he conference call. This ensures ha we have isolaed analyss wih significan vesed ineres in following he firm and provide earnings forecass. Second, we include several proxy variables o capure analys effor in our levels analysis. Third, we perform corroboraing propensiy score mached pair analysis and changes analysis which effecively mach on effor characerisics or difference hem away. Despie hese aemps, analys effor is no observable and as such, he possibiliy remains ha some analyss simply may no have pu forh he effor o ask a quesion on he conference call. Since we canno compleely rule ou effor, we conduc a differen es. Specifically, we inroduce an analysis of subsequen recommendaion revision aciviy ha can boh rule ou effor as a compeing explanaion and provide addiional insighs on he implicaions of conference call paricipaion. If analyss receive privae informaion benefis from paricipaing 26

29 on conference calls, managers will no raionally provide such benefis for free. Chen and Masumoo (2006) and Mayew (2008) provide evidence consisen wih favorable sock recommendaions being one currency o obain such benefis. 18 Under his economic seing, analys paricipaion becomes an observable proxy for analyss who have received a favor from managemen and from whom managemen may expec coninued reciprociy in he form of mainaining favorable recommendaions. O Brien e al. (2005) documens ha analyss wih incenives o mainain relaionships ha hinge on he favorabiliy of sock recommendaions have incenives o accelerae (delay) he reflecion of good (bad) news ino heir sock recommendaions. If paricipaing analyss in fac are receiving privae informaion benefis from managemen access during conference calls, we should also observe recommendaion aciviy ha responds o earnings news in a way ha is pleasing o managemen. Saed formally: H2a: Paricipaing analyss are less likely o downgrade sock recommendaions in he presence of bad news han nonparicipaing analyss. H2b: Paricipaing analyss are more likely o upgrade sock recommendaions in he presence of good news han nonparicipaing analyss. Noe ha if paricipaion on a conference call capured only effor and is no an indicaion of some privae benefi received by he analys, we would expec paricipaing analyss o updae heir recommendaions wih a higher likelihood regardless of he news. Tha is, paricipaing analyss would be more likely o upgrade in he presence of good news and more likely o downgrade in he presence of bad news. 18 An alernaive currency purpored o be used by analyss o curry favor wih managemen is he walkdown forecas (Libby e al. 2008; Ke and Yu 2006). However, o idenify wheher an annual forecas issued afer a conference call is being walked down appropriaely, he researcher needs o make assumpions abou when during he fiscal year a manager would prefer a forecas o urn from opimisic o pessimisic. Given he ambiguiy regarding he evoluion of a walkdown forecas, we insead examine sock recommendaions. 27

30 To es wheher analyss differenially incorporae bad (good) news ino heir sock recommendaions, we model he probabiliy ha an analys downgrades (upgrades) he firm s shares over he 90 days subsequen o he earnings announcemen as a funcion of wheher he analys paricipaed on he conference call using he following logisic regression specificaion: Pr(Downgrade i,j,+1 FE i,j, <0) = β 0 + β 1 Paricipae i,j, + β 2 SBuy i,j, + β 3 Buy i,j, + β 4 Hold i,j, + β 5 Sell i,j, + ε i,j, (3a) Pr(Upgrade i,j,+1 FE i,j, >0) = α 0 + α 1 Paricipae i,j, + α 2 Buy i,j, + α 3 Hold i,j, + α 4 Sell i,j, + α 5 SSell i,j, + υ i,j, (3b) The dependen variable in model 3a (3b) akes on a value of 1 if he analys downgraded (upgraded) a firm s sock in he presence of a bad (good) news during he 90 days subsequen o he earnings announcemen a quarer, and zero oherwise. We choose a 90 day period o be consisen wih our earnings forecas ess. Because we can measure each individual analys s expecaion of quarer s earnings we are able o measure wheher he quarerly earnings realizaion was good or bad news from he perspecive of each individual analys. We herefore operaionalize analys specific news via each individual analys s forecas error, FE i,j,., defined as he price scaled spli-unadjused I/B/E/S acual earnings per share for quarer minus he las spli-unadjused forecas for quarer s earnings issued by analys i covering firm j. H2a and H2b predic β 1 o be negaive and α 1 o be posiive. Each model conrols for he exising sock recommendaion, as he probabiliy of a downgrade increases as he exising sock recommendaion becomes more favorable (O Brien e al. 2005). Naurally, model 3a (3b) excludes observaions where he analys recommendaion 28

31 was a srong sell (srong buy) since upgrades (downgrades) are no possible from such recommendaion levels. 19 Panel A of Table 5 provides descripive evidence for boh he good news and bad news analys forecas error samples. We find ha he disribuion of price scaled analys specific earnings surprises o be virually idenical in boh subsamples, wih a mean of (-0.013) and sandard deviaion of (0.494) for he good (bad) news subsamples. This suggess he news received by he analyss in each subsample is no likely o differ on dimensions such as he ransiory naure of he surprise. Furher, we find ha conference call paricipaion is also virually idenical, wih 46.6% (46.5%) paricipaion percenages in he good (bad) news subsamples, implying ha paricipaion is no sysemaically relaed wih wheher he analys is posiively or negaively surprised by repored earnings. Finally, we observe ha analyss in he good news sample have slighly more unfavorable sock recommendaions prior o observing he good news, wih an average of 2.82, compared wih an average of 2.38 for he bad news sample. 20 Turning o our recommendaion revision ess, in Panel B of Table 5 we find he coefficien on Paricipae o be negaive and significan (β 1 = , p<0.01). This implies ha, condiional on receiving bad news, and conrolling for he exising recommendaion level, analyss who paricipae are significanly less likely o downgrade he firm s sock over he subsequen 90 days. Panel C of Table 5 shows he opposie, as prediced. Condiional on receiving good news, analyss who paricipae are more likely o upgrade he firm s shares han 19 To ensure our ess compare analyss who coninue o follow he firm afer he earnings news is received, we include only analyss who revise heir sock recommendaion on he firm a leas once in he year following he earnings announcemen. 20 We reain he I/B/E/S coding mehodology such ha more favorable recommendaions receive smaller values (srong sell as 5, sell as 4, hold as 3, buy as 2 and srong buy as 1). 29

32 non paricipaing analyss (α 1 =0.127, p<0.01). 21 An analysis of prediced probabiliies (no abulaed) reveals ha paricipaing analyss are, on average, 11 percen less likely o downgrade firms ha miss heir earnings forecass and 10 percen more likely o upgrade firms ha bea heir earnings forecass. Togeher, hese recommendaion revision resuls are more consisen wih paricipaion yielding some privae benefi for he analys raher han paricipaion simply proxying for differenial analys effor. 22 Aside from ruling ou effor as an alernaive explanaion, hese resuls exend he exan lieraure in wo imporan ways. Firs, O Brien e al. (2005) show analyss wih invesmen banking ies asymmerically impound good and bad news. The change in regulaion hrough he Global Selemen in April of 2003 was mean o remove his invesmen banking conflic. Our sudy, conduced in periods primarily subsequen o he Global Selemen, suggess an addiional conflic ha may require regulaory aenion. Second, his resul builds on Mayew (2008) by showing ha analys recommendaion aciviy afer he conference call is also associaed wih wheher he analys is allowed o paricipae. Mayew (2008) finds ha analyss who issue more favorable sock recommendaions prior o he conference call are more likely o paricipae. However, his resul canno discriminae beween informaion benefis and managemen preferring o discuss he firm s prospecs wih analyss who share a similar favorable view of he firm. Our resuls sugges he associaion in Mayew (2008) is more consisen wih he privae informaion benefis explanaion. 21 Noe ha if analyss sysemaically bias heir earnings forecass downward (Ke and Yu 2006; Libby e al. 2008), posiive analys forecas errors may no represen favorable news abou he firm. This would bias agains finding resuls for H2(b). 22 An alernaive way o invesigae wheher he analys receives an informaion benefi is o observe wheher he sock marke reacs differenially o iniial earnings forecass issued by paricipaing analyss relaive o analyss who do no paricipae. This es is difficul o execue because he average difference in imeliness is less han one day. Thus, he use of daily firm level sock reurns would be confounded wih he marke reacion o boh he paricipaing and nonparicipaing analyss. 30

33 5.3 Oher Privae Benefis o Paricipaion Is privae informaion he only benefi? The preceding analysis provides evidence consisen wih analyss receiving privae informaion benefis. Bu i does no preclude he possibiliy ha paricipaion provides oher benefis as discussed in Libby e al. (2008). For example, analys cliens may simply believe he informaion from analyss who hey can see working by asking quesions on he conference call may heighen heir view of he analys, and in urn rae he analys more favorably. If conference call paricipaion provides labor marke benefis more generally han informaion benefis, he paricipaing analys should in urn enjoy more favorable career oucomes, all else equal. This leads o he following hypohesis: H3: Analys urnover is likely o be higher for nonparicipaing analyss han paricipaing analyss, afer conrolling for informaion benefis. To invesigae he exen o which conference call paricipaion plays a role in he career oucomes of analyss, we esimae he following logisic regression model of he probabiliy of analys urnover in he subsequen year, wih sandard errors clusered by analys: Pr(A_Turnover i,+1 ) = δ 0 + δ 1 A_Paricipae i, + δ 2 A_ACC_R i, + δ 3 ln(a_delay i, )+ δ 4 ln(a_toal_exper i, ) + δ 5 ln(a_forfreq i,) + ω i, (4) Following prior lieraure (Hong e al. 2000; Ke and Yu 2006), A_Turnover is an indicaor variable ha equals 1 if he analys moves o a broker employing less han 25 analyss in he year following he annual earnings announcemen or is no lised in I/B/E/S a he end of he subsequen year, and zero oherwise. A_Paricipae is he raio of he number of firms in he porfolio where he analys paricipaed in a leas one conference call during he year. We expec δ 1 < 0 if paricipaion lowers he probabiliy of analys urnover. A_ACC_R is he average (across firms in he analys s porfolio) relaive accuracy of an analys s firs forecas of annual earnings afer he conference call from he hird fiscal quarer. 31

34 A_Delay is he average (across firms in he analys s porfolio) number of days elapsed since he hird fiscal quarer earnings conference call and he firs annual earnings esimae provided afer he hird fiscal quarer conference call. A_ACC_R (A_Delay) proxy for informaion benefis, and we expec ha he more accurae (more delay) in forecasing he forecas is, he less (more) likely is he urnover. A_Toal_Exper is he number of years of experience he analys has. We expec longer serving analyss, who presumably have survived due o heir superior abiliy, o have lower likelihood of urnover. A_ForFreq is he average number of forecass issued over he analys s porfolio during he year. We expec analyss who exer more effor o deliver more forecass o be less likely o experience an employmen separaion. The coun variables A_Delay, A_Toal_Exper, and A_ForFreq are righ skewed, so we use he naural logarihm of hese variables in our empirical specificaion. Panel B of Table 6 presens he empirical resuls of he relaion beween analys urnover and conference call paricipaion. We observe a negaive and significan coefficien on A_Paricipae (δ 1 = , p = wo ailed), suggesing paricipaing analyss are less likely o experience a downward employmen separaion. To assess economic significance, we noe ha he uncondiional urnover rae depiced in Table 6 Panel A is 1.8 percen. Holding all oher variables a heir means, he prediced urnover probabiliy (unabulaed) for analyss paricipaing in a conference call for all firms (A_Paricipae = 1) in heir porfolio is 1.5 percen, compared wih 2.2 percen for analyss who do no paricipae in any conference calls during he year for heir porfolio (A_Paricipae = 0). 32

35 Turning o conrol variables, analyss wih more overall experience are less likely o urnover. However, he remaining conrol variables do no behave as prediced. 23 The coefficiens on analys forecas accuracy and forecas frequency are posiive and no saisically significan. Conrary o expecaions, we also observe a saisically negaive associaion beween urnover and he delay of he forecas, suggesing ha analyss who wihhold heir forecas longer are less likely o urnover. If paricipaion is in fac capuring informaion benefis, he coefficiens on accuracy and delay represen he impac of accuracy and delay afer conrolling for he informaion benefis. The mixed resuls on he conrol variables make i difficul o discern wheher paricipaion provides benefis over and above informaion benefis, bu i is clear ha paricipaion is in fac associaed wih lower likelihood of employmen separaion. Taken ogeher, we find some evidence of labor marke benefis, bu his exploraion is admiedly preliminary. A comprehensive examinaion of all he benefis associaed wih conference call paricipaion is beyond he scope of his sudy. Therefore we leave an invesigaion of oher angible and inangible benefis o conference call paricipaion o fuure research. 6.0 Conclusion This paper invesigaes wheher, pos-reg FD, analyss who ask quesions in public earnings conference calls receive privae informaion benefis relaive o analyss who do no ask quesions. Our evidence suggess ha conference call access provides informaional benefis o 23 I is difficul o ascerain why he conrol variables do no behave as prediced. Two main differences relaive o he exan lieraure examining analys urnover (Hong and Kubik 2003; Clemen and Tse 2005; Ke and Yu 2006) appear o be sample selecion and he paricular forecas we use o measure accuracy and delay a he analys level. Wih respec o he former, our analys level specificaion is derived using he observaions in our deerminan models of accuracy and imeliness, which is condiioned on very acive analyss working for large brokerage firms. Second, we measure accuracy and delay a he analys level using he firs annual forecas afer 3 rd quarer earnings. Commonly, he lieraure uses he las annual earnings forecas of he year for each firm in an analys s porfolio. 33

36 analyss who ask quesions during he calls. Afer conrolling for analys effor in a levels, changes, and a propensiy score mached sample specificaion, we find ha analyss who paricipae in conference calls issue annual earnings forecass ha are boh more accurae and imely han analyss who do no paricipae in he calls. These resuls add o he lieraure documening ha public informaion can have privae informaion benefis and shed new ligh on he naure of he benefis of conference call access. We also invesigae wheher analyss who paricipae in conference calls are more (less) likely o incroporae good (bad) news informaion ino heir sock recommendaions. Consisen wih reciprociy for receiving conference call access, we find ha analyss who paricipae in conference calls are more (less) likely o upgrade (downgrade) a firm s sock recommendaion afer receiving good (bad) news abou he firm over he 90 days subsequen o he earnings announcemen. Our sudy adds o recen evidence ha suggess analyss and managers coninue o exchange privae benefis, even in he pos-reg FD period (Cohen e al. 2008; Wesphal and Clemen 2008). We believe his informaion is imporan o regulaors as hey evaluae he success of Reg FD in curailing selecive disclosures and consider he coss and benefis of aemping o mainain a level informaion playing field hrough furher regulaion of manager and analys ineracions. Finally, we documen ha analyss who paricipae in conference calls are less likely o experience negaive career oucomes (i.e., urnover) in he year afer he earnings announcemen, incremenal o oher poenial explanaory facors for urnover. This preliminary finding hins a oher privae benefis o conference call access beyond informaion benefis, as suggesed by Libby e al. (2008). We leave he exploraion of such non-informaion benefis o fuure research. 34

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41 Appendix 1 To illusrae he informaional benefis of being able o ask a quesion on a conference call, we apply he Kim and Verrecchia (1997) framework o he conference call seing. Suppose firm value V is comprised of hree componens V 1, V2 and V 3, such ha V = V1 + V2 + V3. Consider a manager wih hree privae, noisy and uncorrelaed signals of firm value, S 1 = V1 + ε1, 2 = V2 + ε 2 S and S 3 = V3 + ε 3, ha will be supplied o he marke when asked for by analyss during he conference call. Nex, inroduce hree financial analyss, A, B and C, who each uncover noisy privae informaion abou he error erm in one of he manager s privae signals. We assume ha in a compeiive environmen, analyss collec differen privae informaion based on heir differenial skill and experise (see Barron e al for an example). This assumpion of analyss seeking ou differenial privae informaion is also consisen wih sraegies ha brokerage house employers sugges o prospecive sell side analyss. 24 Analys A (B) [C] s noisy privae informaion is characerized as I A = ε 1 + η A ( I B ε + η B = 2 ) [ I C = ε 3 + ηc ]. Each analys s noisy privae informaion canno be used in isolaion o learn abou firm value since hey do no conain componens of V. Suppose only analys A and analys B are allowed o ask quesions during he conference call. They will ask quesions condiional on heir privae informaion, and managemen will provide public signals S1and S 2. Wih public signal S 1, analys A can combine he public signal wih her own privae signal o generae a new privae signal abou firm value, Z 1, where Z 1 A = S1 I A = V1 + η A. Analys B canno generae a new privae signal from 1 A S abou firm value because S 1 and I B do no carry a common elemen o allow combinaion. Similarly, wih S2 analys B can generae a new privae signal bu analys A canno. Analys C, who asks no quesion, generaes no informaion abou firm value from eiher public signal. 24 We aended an informaion session hosed by he managing direcor and direcor of research a a prominen sellside research firm. The purpose of his informaion session was o provide an audience of MBA sudens, many of whom become sell-side analyss, wih insighs abou a career as a sell-side analys, keys o success, and sources of failure in he profession. The overarching objecive of all analyss is clien service, wih primary ineres on insiuional invesors. Regarding impressing insiuional invesor cliens, he direcor of research said all analyss had o firs find a niche. Broadly, finding a niche means specializing in economic value-driving aspecs of covered firms ha would uniquely idenify he analys o insiuional cliens relaive o oher analyss. Examples include being a foreign growh specialis or a supply chain specialis. 39

42 Thus, we argue ha alhough quesions and answers are boh publicly disclosed during conference calls, new unique privae signals of value can be generaed for he analys who asks a quesion. In his way, conference call paricipaion is a means by which analyss can generae an informaional advanage over oher analyss. Nohing prevens he analys who does no ask a quesion from gahering addiional privae informaion abou he public signal s error erm afer he public signal is released. However, privae informaion gahering ex pos is paricularly cosly because i akes ime and effor. For cliens looking o exploi marke opporuniies, ime is of he essence and he value of informaion provided by analyss is decreasing in ime (Lowengard 2006). 40

43 Appendix 2 Variable Definiions Analys-Firm-Quarer Level Variables ACC_R pos is he relaive accuracy of analys i s firs forecas of firm j s annual earnings issued afer quarer s earnings announcemen. Following Ke and Yu (2006), esimaes range beween zero and 100: ACC_R pos = ((absferank pos - 1)/(Follow - 1))*100. absferank pos is he rank of he absolue forecas error, FE, of an analys i s iniial forecas of upcoming annual earnings afer he conference call among forecasing analyss. Follow is he number of analyss providing a forecas. ACC_R pre is he relaive accuracy of analys i s las forecas of firm j s annual earnings issued before quarer s earnings announcemen. Following Ke and Yu (2006), esimaes range beween zero and 100: ACC_R pos = ((absferank pre - 1)/(Follow - 1))*100. absferank pre is he rank of he absolue forecas error, FE, of an analys i s las forecas of upcoming annual earnings before he conference call among forecasing analyss. Follow is he number of analyss providing a forecas. Broker_R is he relaive size of he brokerage house employing analys i a he ime of he quarer Bro ker_ revranki, j, 1 earnings announcemen, defined as100 x100, where Followi, j, 1 Broker_revrank is he reverse ranking of Broker. Broker is he number of employees working a he brokerage house as of he mos recenly compleed calendar quarer prior o he conference call dae. Buy is an indicaor variable ha equals 1 if analys i s recommendaion for firm j s sock prior o he quarer earnings announcemen is buy, and zero oherwise. CCuser is he oal number of conference calls (excluding firm j) in which analys i paricipaed during he calendar quarer conaining fiscal quarer for firm j. Delay is he number of days ha elapse beween firm j s quarerly earnings announcemen and he issuance of analys i's firs one-year-ahead annual earnings forecas subsequen o he conference call. Downgrade is an indicaor variable ha equals 1 if analys i downgrades he sock recommendaion for firm j in he 90 days subsequen o he quarer earnings announcemen and zero oherwise. F_Exper is he number of full years of experience analys i has covering firm j, as of quarer. F_Exper_R is he relaive firm specific experience defined as F _ Exper _ revranki, j, x100, where F_Exper_revrank is he reverse ranking Followi, j, 1 of F_Exper. FE is he difference beween he firm s acual repored earnings and analys i s annual earnings forecas and as obained from he I/B/E/S unspli-adjused deail file. FE_p FE scaled by sock price wo days before he quarer earnings announcemen. Firms is he number of firms followed by analys i a quarer, as measured by he number of firms for which he analys forecass provides forecass for wihin he sample. Firms _ revranki, j, 1 Firms_R is he relaive number of firms covered defined as100 x100, Followi, j, 1 where Firms_revrank is he reverse ranking of Firms. ForFreq is he number of annual earnings forecass for firm j issued by analys i in he 12 monhs prior o he quarer earnings announcemen. 41

44 ForFreq_R Follow LnFollow Hold Horizon pos Horizon_R pos Inds Inds_R is he relaive number of annual earnings forecass issued defined as ForFreq _ revranki, j, x100, where ForFreq_revrank is he reverse ranking Followi, j, 1 of ForFreq. is he number of I/B/E/S sell-side analyss in our sample forecasing annual fuure earnings for firm j a quarer. is he naural logarihm of Follow. is an indicaor variable ha equals 1 if analys i s recommendaion for firm j s sock prior o he quarer earnings announcemen is hold, and zero oherwise. is he number of days beween he analys i's firs forecas of firm j s annual earnings issued afer quarer s earnings announcemen and he repor dae of annual earnings. is he relaive forecas horizon of analys i's firs forecas of firm j s annual earnings issued afer quarer s earnings announcemen defined as pos Horizon _ revranki, j, x100, where Horizon pos _revrank is he reverse Followi, j, 1 ranking of Horizon pos. is he number of indusries covered by he analys over he mos recenly compleed calendar year prior o he conference call dae. is he relaive number of indusries covered by he analys over he mos recenly compleed calendar year prior o he conference call dae defined as Inds _ revranki, j, x100, where Inds_revrank is he reverse ranking of Inds. Follow 1 i, j, Paricipae is an indicaor variable ha equals 1 if he analys i asked a quesion on firm j s quarer conference call, and zero oherwise. Prior_Paricipae is an indicaor variable ha equals 1 if he analys was idenified as asking a quesion on any of he firm s prior conference calls in he sample, and 0 oherwise. QAmin he lengh of he quesion and answer porion of he call in minues, where minues are derived by convering he oal word coun of he quesion and answer session o minues using a rae of 150 words per minue. Rec is he recommendaion level of analys i s recommendaion for firm j s sock prior o he quarer earnings announcemen, where srong buy = 1, buy = 2, hold = 3, sell = 4, and srong sell = 5. RecHorizon is he recommendaion horizon measured as he number of days beween he conference call dae and he dae of he analys s mos recen sock recommendaion. SBuy is an indicaor variable ha equals 1 if analys i s recommendaion for firm j s sock prior o he quarer earnings announcemen is srong buy, and zero oherwise. Sell is an indicaor variable ha equals 1 if analys i s recommendaion for firm j s sock prior o he quarer earnings announcemen is sell, and zero oherwise. SSell is an indicaor variable ha equals 1 if analys i s recommendaion for firm j s sock prior o he quarer earnings announcemen is srong sell, and zero oherwise. T_Exper is he number of full years of experience analys i has covering any firm on I/B/E/S as of quarer. T _ Exper _ revranki, j, 1 T_Exper_R is he relaive oal experience defined as100 x100, where Followi, j, 1 T_Exper_revrank is he reverse ranking of T_Exper. 42

45 Upgrade is an indicaor variable ha equals 1 if analys i upgrades he sock recommendaion for firm j in he 90 days subsequen o he quarer earnings announcemen and zero oherwise. Analys-Year Level Variables A_ACC_R A_Delay A_ForFreq A_Paricipae A_Toal_Exper_R A_Turnover AllSar is he average ACC_R pos for analys i s iniial annual earnings forecas issued afer he Q3 earnings conference calls in year y, across all firms in her porfolio. is he average Delay for analys i s iniial annual earnings forecas issued afer he Q3 earnings conference calls in year y, across all firms in her porfolio. is he average number of forecass issued over he analys s porfolio during year y. is he proporion of firms in analys i's porfolio in which he analys asked a quesion on a leas one quarerly earnings conference call during year y. is he number of full years of experience analys i has covering any firm in I/B/E/S as of he beginning of year y. is an indicaor variable ha equals 1 if he analys moves o a broker employing less han 25 analyss in he year following he annual earnings announcemen or is no lised in I/B/E/S a he end of he subsequen year, and zero oherwise. is an indicaor variable ha equals 1 if he analys made any of he Insiuional Invesor Research All-American eams as of he mos recen prior year, and 0 oherwise. 43

46 Table 1 Descripive Saisics Panel A: Univariae saisics for 57,443 analys firm quarers Variable a Mean Median Sd Dev Min Max Delay Horizon pos Horizon_R pos F_Exper_R Firms_R LnFollow Broker_R T_Exper_R ForFreq_R Acc_R pos Acc_R pre F_Exper Firms Broker T_Exper ForFreq Paricipae

47 Table 1 (coninued) Panel B: Comparison of indusry composiion of 1,919 sample firms and Compusa universe b Firms % of Sample % of Compusa populaion firm-year observaions in each indusry Food 36 2% 2% Mining and Minerals 24 1% 4% Oil and Pero Producs 92 5% 5% Texiles, Apparel & Fooware 26 1% 1% Consumer Durables 32 2% 2% Chemicals 33 2% 1% Drugs, Soap, Perfumes, Tobacco 72 4% 4% Consrucion 40 2% 2% Seel 24 1% 1% Fabricaed Producs 10 1% 1% Machinery and Business Equipmen % 9% Auomobiles 24 1% 1% Transporaion 67 4% 3% Uiliies 46 2% 3% Reail Sores 136 7% 4% Financial Insiuions % 25% Oher % 32% Toal 1, % 100% 45

48 Table 1 (coninued) Panel C: Frequencies Unique Firms 1,919 Unique Analyss 3,246 Unique Brokerages 265 Quarer End Dae Number of Conference Calls % 3/31/ % 6/30/ % 9/30/ % 12/31/ % 3/31/ % 6/30/ % 9/30/ % 12/31/ % 3/31/ % 6/30/ % 9/30/ % 12/31/ % 3/31/ % Toal 8, % 46

49 Table 1 (coninued) Panel D: Correlaions c (Pearson above he diagonal / Spearman below he diagonal) Variable Paricipae Delay Horizon pos Horizon_R pos F_Exper_R Firms_R Ln_Follow Broker_R T_Exper_R ForFreq_R Acc_R pos Acc_R pre F_Exper Firms Broker T_Exper ForFreq a See appendix 2 for variable definiions. b Indusry definiions correspond o he 17 groups of SIC codes on Ken French s websie hp://mba.uck.darmouh.edu/pages/faculy/ken.french/daa_library.hml). c Bolded correlaions are significanly differen from zero a p < 0.01 wo-ailed level. 47

50 Table 2 OLS regression invesigaing he associaion beween conference call paricipaion and relaive forecas accuracy Panel A: OLS pooled cross secion of 57,443 analys-firm-quarers pos ACC _ R i = β, j, 0 + β 1 Paricipae i,j, + β 2 F_Exper_R i,j, + β 3 T_Exper_R i,j, + β 4 LnFollow i,j, + β 5 Firms_ R i,j, + β 6 Broker_R i,j, pos pre + β 7 Horizon _ R i + β, j, 8 ForFreq_R i,j, + β 9 ACC _ R i, j, + ε i,j, (1a) Variable a Prediced Sign Coefficien b Sandard Error c Paricipae *** F_Exper_R T_Exper_R Lnfollow? Firms_R * Broker_R *** Horizon_R pos *** ForFreq_R Acc_R pre *** Inercep? *** Sample Size 57,443 Adjused R Panel B: OLS ime-series changes model wih 25,010 adjacen analys-firm-quarers Δ ACC _ R i = α, j, 0 + α 1 Δ Paricipa e i + α, j, 2 Δ Horizon _ R i + μ, j, i,j, (1b) Variable a Prediced Sign Coefficien b Sandard Error c ΔParicipae ** ΔHorizon_R *** Inercep? *** Sample Size 25,010 Adjused R a See appendix 2 for variable definiions. ΔParicipae is defined as Paricipae in he curren quarer minus Paricipae in he prior quarer for a given analys on adjacen quarers for a given firm. ΔHorizon_R is he relaive Horizon of he firs annual earnings forecas revision afer he quarer earnings announcemen minus he relaive Horizon of he las annual earnings forecas before he quarer earnings announcemen. b ***, **, * Saisical significance a he 0.01, 0.05, 0.10 level, respecively, in wo-ailed ess. c Robus sandard errors are esimaed using he Huber (1967)-Whie(1980) procedure, wih analys-level clusering for lack of independence of analys observaions over ime. 48

51 Table 3 Duraion from Conference Call o Forecas Revision Panel A: Cox regressions of he imeliness of forecas revision in he pooled cross secion of observaions h() = h 0 () exp(δ 0 ) + δ 1 Paricipae i,j, + δ 2 F_Exper_R i,j, + δ 3 T_Exper_R i,j, + δ 4 LnFollow i,j, + δ 5 Firms_ R i,j, + δ 6 Broker_R i,j, pre δ 7 ForFreq_R i,j, + δ 8 ACC _ R i, j, + μ i,j, (2a) Variable a Prediced Sign Coefficien b Hazard Raio Sandard Error c Paricipae *** F_Exper_R ** T_Exper_R LnFollow? *** Firms_R *** Broker_R *** ForFreq_R *** Acc_R pre *** Sample size 57,443 Panel B: OLS regression of he change in imeliness of forecas revisions on changes in conference call paricipaion in adjacen analys-firm-quarers Δ Delay i, j, = γ 0 + γ 1 Paricipa e i, j, Δ + η i,j, (2b) Variable a Prediced Sign Coefficien b Sandard Error c ΔParicipae ** Inercep? *** Sample Size 25,010 Adjused R a See appendix 2 for variable definiions. ΔParicipae is defined as Paricipae in he curren quarer minus Paricipae in he prior quarer for a given analys on adjacen quarers for a given firm. b ***, **, * Saisical significance a he 0.01, 0.05, 0.10 level, respecively, in wo-ailed ess. c Robus sandard errors are esimaed using he Huber (1967)-Whie(1980) procedure, wih analys-level clusering for lack of independence of analys observaions over ime. 49

52 Table 4 Propensiy Score Analysis Panel A: Logisic regression of he likelihood of conference call paricipaion Paricipae i,j, = β 0 + β 1 SBuy i,j, + β 2 Buy i,j, + β 3 Sell i,j, + β 4 SSell i,j, +β 5 QAmin i,j, + β 6 LnFollow i,j, + β 7 AllSar i,j, + β 8 ACC_R pre i,j, + β 9 F_Exper_R i,j, + β 10 T_Exper_R i,j, +β 11 Inds_R i,j, + β 12 ForFreq_R i,j, + β 13 Broker_R i,j, +β 14 Firms_R i,j, + β 15 CCuser i,j, +β 16 PriorParicipae i,j, + β 17 RecHorizon i,j, + υ i,j,. Variable a Prediced Sign e Coefficien b Sandard Error c SBuy *** Buy *** Sell *** SSell *** QAmin *** LnFollow *** AllSar * ACC_R pre F_Exper_R *** T_Exper_R *** Inds_R *** ForFreq_R * Broker_R *** Firms_R *** CCuser *** PriorParicipae *** RecHorizon *** Inercep? *** Sample size d 56,907 Psuedo R % Log Likelhood -33, Wald χ

53 Panel B: Differences in Mean, Median and disribuion of relaive forecas accuracy and imeliness beween paricipaing and nonparicipaing firms for he Propensiy Score Mached Sample Variable a Treamen (Paricipaing Analyss) Conrol (Nonparicipaing analyss) Mean Median Mean Median -es p-value Wilcoxon p-value Propensiy Score ACC_R pos Delay Sample size d 13,255 13,255 a See appendix 2 for variable definiions. Propensiy Score is he prediced probabiliy derived from he logisic regression in Panel A. b ***, **, * Saisical significance a he 0.01, 0.05, 0.10 level, respecively, in wo-ailed ess. c Robus sandard errors are esimaed using he Huber (1967)-Whie(1980) procedure, wih analys-level clusering for lack of independence of analys observaions over ime. d The sample size of 56,907 in Panel A equals he overall pooled sample of 57,433 in Table 1 less 536 analys firm quarer observaions where no ousanding recommendaion was available on I/B/E/S. Of he 56,907 observaions used for esimaion in Panel A, 26,906 observaions had he reamen effec Paricipae = 1. Of hese 26,906 paricipaing analyss in Panel A, mached pairs for 13,255 were idenified, where maches were drawn from nonparicipaing analyss wihou replacemens and required a propensiy score wihin 0.01 of he paricipaing analys. e Prediced signs are aken from Mayew (2008) 51

54 Table 5 Changes in Sock Recommendaions afer Good or Bad News Panel A: Descripive saisics for analys recommendaion samples Bad News / Downgrade Sample N=13,097 Variable a Mean Median Sd Dev Minimum Maximum FE_p Rec Paricipae Good News / Upgrade Sample N = 26,915 Variable a Mean Median Sd Dev Minimum Maximum FE_p Rec Paricipae

55 Table 5 (coninued) Panel B: Logisic regression invesigaing he probabiliy of downgrading afer observing bad news Pr(Downgrade i,j,+1 FE i,j, <0) = β 0 + β 1 Paricipae i,j, + β 2 SBuy i,j, + β 3 Buy i,j, + β 4 Hold i,j, + β 5 Sell i,j, + ε i,j, (3a) Variable a Coefficien b Odds Raio Sandard Error c SBuy *** Buy *** Hold *** Paricipae *** Sample size 13,097 Pseudo R Correcly classified 77.81% Panel C: Logisic regression invesigaing he probabiliy of upgrading afer observing good news Pr(Upgrade i,j,+1 FE i,j, >0) = α 0 + α 1 Paricipae i,j, + α 2 Buy i,j, + α 3 Hold i,j, + α 4 Sell i,j, + α 5 SSell i,j, + υ i,j, (3b) Variable a Coefficien b Odds Raio Sandard Error c SSell *** Sell *** Hold *** Paricipae *** Sample Size 26,915 Pseudo R Correcly Classified 79.12% a See appendix 2 for definiions of variables. b ***, **, * Saisical significance a he 0.01, 0.05, 0.10 level, respecively, in wo-ailed ess. c Robus sandard errors are esimaed using he Huber (1967)-Whie(1980) procedure, wih analys-level clusering for lack of independence of analys observaions over ime. 53

56 Table 6 Logisic Regression Invesigaing he Associaion beween Conference Call Paricipaion and Analys Turnover Panel A: Descripive Saisics on Analys Level Variables Variable a Mean Median Sd Dev Min Max A_Paricipae A_Turnover A_ACC_R A_Delay A_Toal_Exper A_ForFreq Panel B: Logisic Regression (dependen variable is analys urnover) Variable a Predicion Coefficien b Odds Raio Sandard Error c A_Paricipae * A_ACC_R ln(a_delay) ** ln(a_toal_exper) * ln(a_forfreq) Sample size 7,189 Pseudo R Correcly classified 98.16% a See appendix 2 for variable definiions. Naural logarihm ransformaions of variables wih values equal o zero are se o zero. b ***, **, * Saisical significance a he 0.01, 0.05, 0.10 level, respecively, in wo-ailed ess. c Robus sandard errors are esimaed using he Huber (1967)-Whie(1980) procedure, wih analys-level clusering for lack of independence of analys observaions over ime. 54