Two Faces of Intra-Industry Information Transfers: Evidence from Management Earnings and Revenue Forecasts

Transcription

1 Two Faces of Intra-Industry Informaton Transfers: Evdence from Management Earnngs and Revenue Forecasts Yongtae Km Leavey School of Busness Santa Clara Unversty Santa Clara, CA TEL: (408) , Emal: Mchael Lacna College of Busness Rochester Insttute of Technology Rochester, NY TEL: (585) , Emal: Myung Seok Park School of Busness Vrgna Commonwealth Unversty Rchmond, VA TEL: (804) , Emal: September 004 The authors thank Akshay Anand, Matthew L, and Erc Wley for excellent research assstance. Km acknowledges fnancal support provded by the Accountng Development Fund and the Leavey Research Grant at Santa Clara Unversty.

2 Two Faces of Intra-Industry Informaton Transfers: Evdence from Management Earnngs and Revenue Forecasts ABSTRACT We examne postve and negatve ntra-ndustry nformaton transfers assocated wth management earnngs and revenue forecasts. Postve nformaton transfers are due to ndustry commonaltes whereas negatve nformaton transfers are caused by compettve shfts wthn ndustres. We hypothesze postve nformaton transfers to non-compettor frms and negatve nformaton transfers to compettors n the same ndustry. As predcted, we fnd negatve (postve) ntra-ndustry nformaton transfer between forecastng frms and non-forecastng frms whch are more (less) lkely to be compettor frms. We also fnd that ndustry growth and concentraton (.e., the degree of mperfect competton) are assocated wth the drecton of ntra-ndustry nformaton transfers. Key Words: Intra-ndustry nformaton transfer, Management earnngs forecast, Management revenue forecast, competton Data Avalablty: Data are publcly avalable from sources dentfed n the paper 1

3 Two Faces of Intra-Industry Informaton Transfers: Evdence from Management Earnngs and Revenue Forecasts 1. Introducton Informaton transfer occurs f an announcement made by one or more frms affects the stock return(s) of one or more non-announcng frms. Ths study examnes two types of ntrandustry nformaton transfers assocated wth management earnngs and revenue forecasts: (1) nformaton transfers from ndustry commonaltes, and () nformaton transfers from compettve shfts. The former are known as postve nformaton transfers and the latter are known as negatve nformaton transfers. Thus far research on ntra-ndustry nformaton transfers focuses on postve nformaton transfers, where good (bad) news from an announcng frm on-average causes a postve (negatve) stock market reacton from non-announcng frms n the same ndustry. Postve ntra-ndustry nformaton transfers occur due to ndustry commonaltes, where the news from the announcng frm s representatve of the current state and/or future prospects of the ndustry. Though pror papers (Foster, 1981; Bagnsk, 1987; Pownall and Waymre, 1989; Detrch, 1989; and Schpper, 1990) recognze negatve nformaton transfers from compettve shfts, lttle research has focused on negatve nformaton transfers or separately nvestgated postve and negatve nformaton transfers. 1 A negatve nformaton transfer occurs when a good (bad) news announcement made by a frm conveys market share taken away from (gven to) the competton, thereby causng a negatve (postve) stock market reacton from competng frms. An nformaton release by a frm could convey postve nformaton transfers for some frms n ts ndustry but negatve nformaton transfers to other frms n ts ndustry that are close compettors. By offsettng the

4 sgned effects of possble nformaton transfers, ths can lead to an overall fndng of no nformaton transfer. Indeed, many researchers fal to fnd meanngful results for nformaton transfers from drectonal tests whle fndng statstcally sgnfcant results from non-drectonal tests (e.g., Han, Wld, and Ramesh; 1989). Our study uses drectonal tests to dstngush between postve (due to the ndustry commonaltes) and negatve (due to the compettve shfts) nformaton transfers assocated wth management earnngs and revenue forecasts. Prevous research dealng wth ntra-ndustry nformaton transfers from management forecasts (e.g., Bagnsk, 1987; Han, Wld, and Ramesh, 1989; Pownall and Waymre, 1989; and Pyo and Lustgarten, 1990) has focused on the ntra-ndustry nformaton transfer from management earnngs forecasts but not from management revenue forecasts. If the negatve nformaton transfers mean compettve shfts n an ndustry, nformaton about revenue as well as earnngs would be valuable to nvestors n compettor frms. Therefore, a secondary motvaton of ths paper s to study the ntra-ndustry nformaton transfer assocated wth management revenue forecasts as well as earnngs forecasts. We hypothesze a negatve nformaton transfer between a forecastng frm and ndustry counterpart frms that are close compettors. On the other hand, we postulate a postve ntrandustry nformaton transfer between a forecastng frm and an ndustry counterpart frm that s not lkely to be a close compettor. Also, we post that postve (negatve) nformaton transfers are predomnant n fast growng ndustres when good (bad) news forecasts are released and negatve nformaton transfers are predomnant n slowly growng ndustres. Furthermore, we 1 Two notable exceptons can be found n the fnance lterature. Lang and Stulz (199) nvestgate contagon and compettve ntra-ndustry effects wth respect to bankruptcy announcements, and Laux, Starks, and Yoon (1998) examne the relatve mportance of these two dfferent ntra-ndustry effects n relaton to large dvdend revsons. Foster (1981) and others use non-drectonal tests to ncrease the power of ther emprcal tests snce drectonal tests may conclude no nformaton transfers even though they exst. 3

5 hypothesze that lower (hgher) ndustry concentraton s assocated wth postve (negatve) ntra-ndustry nformaton transfer. The majorty of our results support our hypotheses. The results show negatve (postve) ntra-ndustry nformaton transfers between forecastng frms and non-forecastng frms of smlar (dssmlar) sze, whch are (are not) lkely to be close compettors of the forecastng frm. Furthermore, when frms forecast good news, the ntra-ndustry nformaton transfers to compettor frms are postve n fast growng ndustres and negatve n ndustres wth low sales growth and hgh concentraton (.e., hgh degree of mperfect competton), where the ndustry concentraton s measured by the Herfndahl ndex. When frms forecast bad news, we fnd that the ntra-ndustry nformaton transfers to compettor frms are negatve n fast growng ndustres wth hgh concentraton. We also present the evdence that negatve nformaton transfers are carred by earnngs forecasts n the case of good news forecasts, and by revenue forecasts n the case of bad news forecasts. Ths paper contrbutes to the lterature n that t addresses and hghlghts the two faces of nformaton transfers assocated wth management forecasts: postve nformaton transfer due to ndustry commonaltes and negatve nformaton transfer due to compettve shfts. By parttonng a non-forecastng sample based on the lkelhood of beng compettor frms and on ndustry characterstcs, we shed lght on the ssue of postve and negatve ntra-ndustry nformaton transfers that would not have been revealed wthout sample parttonng. Furthermore, ths paper provdes evdence of ntra-ndustry nformaton transfer for management revenue forecasts. Despte the fact that a revenue forecast s the most common supplementary dsclosure ncluded wth a management earnngs forecast, no research (to the authors knowledge) has examned the ntra-ndustry effects from management revenue forecasts. 4

6 The next secton formulates the hypotheses. Ths s followed by a dscusson of research desgn. In secton 4, the statstcal tests and emprcal results are dscussed. The fnal secton concludes.. Hypotheses Development We begn wth a dscusson of our secondary predctons. Later, we formulate our hypotheses (H1 through H3). In accordance wth the expectatons adjustment hypothess (Ajnkya and Gft, 1984), management would ssue a revenue forecast when the earnngs forecast conveys an nsuffcent amount of nformaton to ts nvestors (and therefore to nvestors n other frms). Han and Wld (1991) fnd that 1) management revenue forecasts have ncremental nformaton content over management earnngs forecasts, and ) management earnngs forecasts ssued wth management revenue forecasts are less nformatve than management earnngs forecasts ssued alone. Therefore, we expect management earnngs forecasts that are ssued wth management revenue forecasts to provde less nformaton to ndustry counterparts than management earnngs forecasts ssued alone. Hence, we predct: Intra-ndustry nformaton transfer from management earnngs forecasts s stronger when management earnngs forecasts are ssued alone than when management earnngs forecasts are ssued wth revenue forecasts. Although management revenue forecasts have been shown to provde ncremental nformaton over earnngs forecasts, management earnngs forecasts ssued alone have been found to be more nformatve than management earnngs forecasts ssued wth revenue forecasts. Thus, t s an open emprcal queston whether or not the total amount of ntra-ndustry nformaton transfer s greater when management revenue forecasts are ssued wth management 5

7 earnngs forecasts compared to the case n whch management earnngs forecasts are ssued alone. Therefore, no predcton s developed regardng the above dscusson. Determnng whether postve or negatve ntra-ndustry nformaton transfers domnate lkely depends on the compettve relatonshp between the forecastng frm and the ndustry counterpart frm that s the recpent of the forecast nformaton. If a frm forecasts good (bad) news, ths may convey good (bad) prospects for ts ndustry, thereby leadng to a postve nformaton transfer. However, t could also mean market share taken away from (gven to) close compettors n the ndustry, thus leadng to a negatve nformaton transfer. If a frm makes a forecast, a postve nformaton transfer caused by ndustry commonaltes s lkely to preval wth respect to frms n the same ndustry that are not the forecastng frm s close compettors (non-compettor frms). However, a negatve nformaton transfer due to a compettve shft may overtake a postve nformaton transfer for frms that are the forecastng frm s close compettors (compettor frms). Therefore, Hypothess H1 s as follows: H1: The ntra-ndustry nformaton transfer from a frm s management forecasts to noncompettor (compettor) frms n the same ndustry s postve (negatve). When an ndustry s exhbtng strong growth, the pe that represents total ndustry sales s expandng. Therefore, a good news forecast made by an ndustry member lkely conveys good news and thus postve abnormal returns to non-forecasters. The good news from the forecaster carres over to other frms n the same ndustry because there s an ncreasng amount of sales avalable to the ndustry as a whole. On the other hand, a bad news forecast by a frm n a fast growng ndustry can mean that the ncreasng ndustry sales are gong to compettors n the ndustry. Ths would mean a compettve shft n the ndustry and thus postve news to non- 6

8 forecastng compettors. If an ndustry s experencng low growth, then the pe that represents ndustry sales s expandng slowly or even decreasng. Hence, a good (bad) news forecast made by an ndustry member may convey a compettve shft n that market share s taken away from (gven to) non-forecastng frms because addtonal sales are not plentful. As a result, nonforecastng frms may experence negatve (postve) abnormal returns. The above dscusson leads to the followng hypothess: H: Postve (negatve) ntra-ndustry nformaton transfers are predomnant n fast growng ndustres when good (bad) news forecasts are released. In slowly growng ndustres, negatve ntra-ndustry nformaton transfers are predomnant regardless of the forecast news. A hgher ndustry concentraton suggests that the effect of compettve shfts wll domnate ndustry commonalty effects. Lang and Stulz (199) dscuss the compettve and contagon ntra-ndustry effects on other frms n the same ndustry. They argue that the compettve effect should be strongest n an ndustry wth hgh concentraton (hgh degree of mperfect competton, low degree of perfect competton) whereas the contagon effect should be hghest n an ndustry wth low concentraton. By examnng the ndustry effect of bankruptcy announcements, they fnd that the compettve effect s postvely related to ndustry concentraton measured by the Herfndahl-ndex. Therefore, a good (bad) news forecast made by a frm n an ndustry wth hgher market concentraton suggests negatve (postve) abnormal returns for non-forecastng ndustry counterparts. On the other hand, lower ndustry concentraton mples that ndustry commonalty effects are more lkely to domnate. As a result of the prevous dscusson, we post: H3: Postve (negatve) ntra-ndustry nformaton transfers are predomnant n ndustres wth lower (hgher) concentraton. 7

9 3. Research Desgn 3.1 Sample Selecton The sample of management earnngs and revenue forecasts s from Wall Street Journal artcles for the years 1987 to 1993 and s collected from the Dow Jones News Retreval Servce through use of a key word search. 3 Both annual and nterm forecasts are ncluded. A management forecast must be attrbuted to a company offcal. In addton, the forecast must have been made on or before the last day of the fscal perod(s) to whch the forecast apples. Management forecasts made after the end of the fscal perod are often n effect prelmnary announcements of earnngs or revenue. In addton, a management forecast must be for the entre frm. Furthermore, a management earnngs forecast contanng only non-operatng or extraordnary gan or loss components s not ncluded n the sample. Also, the frms to whch the forecasts belong must be on the Compustat database. The aforementoned requrements lead to an ntal sample of 1188 forecasts. Addtonal restrctons are appled. The management forecast must be n a quanttatve (pont, range, mnmum, or maxmum) format 4 to permt comparson wth analyst forecasts and have the necessary daly stock returns avalable on the CRSP daly returns fle. Those two addtonal crtera reduce the sample to 5 forecasts. Fnally, a forecastng frm must have the necessary analyst forecast nformaton avalable from the Value Lne Estmates & Projectons 3 The phrases used nclude two sets of keywords: (1) see(s), expect(s), forecast(s), project(s), estmate(s), hgher, and lower; and () net earnngs, ncome, results, loss, gan, proft(s), mprovement, better, performance, revenue(s), and sales. All keywords, except revenue(s) and sales, were used n Bamber and Cheon (1998). 4 An example of a pont forecast s earnngs are expected to be $.00 per share for ths perod (or an upcomng perod) whereas an example of a range forecast s revenue s expected to be between $500 mllon and $550 mllon.' An example of a maxmum forecast s earnngs are expected to be no more than $3.00 per share and an example of a mnmum forecast s earnngs are expected to be at least $1.50 per share. 8

10 Fle. Ths fnal requrement reduces the sample to 56 management forecasts: 15 forecasts of earnngs alone and 104 forecasts of earnngs and revenue smultaneously. 5 Frms that are n the same ndustry as the forecast frm but do not make a forecast are desgnated as non-forecastng frms. Non-forecastng frms are matched wth a forecastng frm based on four-dgt SIC code and must have the necessary nformaton from the CRSP daly returns fle and the Compustat database. The fnal sample of non-forecasters ncludes 3,309 observatons that are matched wth forecasts of earnngs alone and 1,73 observatons that are matched wth forecasts of earnngs and revenue. 3. Sngle-Index and Two-Index Prcng Models In determnng abnormal returns, followng Han, Wld, and Ramesh (1989), we employ both sngle-ndex and two-ndex prcng models as follows: u e M = R - (α + β R ) (1), t,t M, t M I = R - (α + β R + β R ) (), t,t M,t I, t where R,t s the daly stock return for frm on day t, R M,t s the return on a value-weghted market portfolo for day t, and R I,t s the return on an equally-weghted four-dgt SIC code ndustry portfolo (not ncludng frm ) for day t. Day t = 0 s the day of the management forecast and the parameters n equatons (1) and () are estmated usng ordnary least squares regressons wth stock returns from days -0 to -1 relatve to the date of the management forecast. Equaton (1) s the abnormal return from the standard market model. Equaton () s the abnormal return after controllng for both market and ndustry returns. Han, Wld, and Ramesh 5 The majorty of the tests n our paper use frms that forecast earnngs and revenue smultaneously. 9

11 (1989) show that controllng for ndustry cross-sectonal covaraton n returns s mportant n tests of ntra-ndustry nformaton transfer from management earnngs forecasts. 3.3 Industry Commonaltes, Compettve Shfts, and Forecast News For the purpose of testng ndustry commonaltes versus compettve shfts, we partton our sample based on the followng crtera: (1) forecasters versus sze matched and non- sze matched non-forecasters and () ndustry growth and concentraton. Frst, a forecastng frm s matched based on smlarty n sze wth non-forecastng frms n the same ndustry. Nonforecastng frms that are n the same ndustry as and smlar n sze to the forecastng frm are assumed to more lkely be close compettors wth the forecaster than are non-forecastng frms n the same ndustry as the forecaster but not of smlar sze. Sze s measured as the market value of equty. If the sze of an ndustry-matched non-forecastng frm s between 30% and 300% that of the correspondng forecastng frm, then the non-forecastng frm s consdered to be a szematched frm. For a robustness check, non-forecast frms are also matched wth forecastng frms based on the membershp n the same CRSP sze decle, where decles are measured based on the market value of equty. Second, we determne ntra-ndustry nformaton transfers for ndustry classfcatons parttoned on ndustry growth and the Herfndahl ndex, whch s a proxy for ndustry concentraton. To determne ndustry growth, we measure the growth of the aggregated sales of all sample frms n the forecastng frm s four-dgt SIC code. We take the average of annual ndustry sales growth over the three year perod mmedately precedng the management earnngs/revenue forecast. An ndustry s classfed as a hgh (low) growth ndustry f ts average sales growth s greater than or equal to (less than) the medan sales growth of all ndustres. We 10

12 employ the Herfndahl ndex to measure ndustry concentraton. The Herfndahl ndex s n calculated as s, where s s the market share of frm j for the fscal year n whch the j= 1 j j forecast s made, defned as frm j s sales revenues dvded by total sales revenues of all sample frms n frm j s four-dgt SIC code, and n s the number of frms n the four-dgt SIC code classfcaton. An ndustry s classfed as havng hgh (low) concentraton f ts Herfndahl ndex s greater than or equal to (less than) the medan Herfndahl ndex from all ndustres. In addton, we partton the sample accordng to abnormal stock returns of forecastng frms around the tme of the forecast, wth postve abnormal returns mplyng good news and negatve abnormal returns mplyng bad news. Snce management earnngs and revenue forecast surprses are often of dfferent sgns, the abnormal return s a better surrogate for the news of the forecast n ths study. 3.4 Unexpected Forecasts and Cumulatve Abnormal Returns We measure earnngs and revenue forecast surprses usng the unexpected management forecast for frm : ( MEF - AEF ) AEF UMEF = (3) ( MRF - ARF ) ARF UMRF = (4) where UMEF (UMRF ) s the unexpected management earnngs (revenue) forecast, MEF (MRF ) s the management earnngs (revenue) forecast and AEF (ARF ) s the Value Lne database s most recent analyst earnngs (revenue) forecast before the management earnngs (revenue) forecast. Hereafter, the subscrpts on the varables n equatons (3) and (4) are suppressed except n the upcomng equatons. Analysts forecasts are collected from the Value 11

13 Lne Estmates & Projectons Fle because t s the only database that systematcally ncludes both earnngs and revenue forecasts. Data n ths database correspond to the estmates n the most recently publshed paper copy of the Value Lne Investment Survey. Unlke n the Insttutonal Brokers Estmate System (IBES) database, no subsequent adjustments for stock splts and stock dvdends are made to the data n the Value Lne database. Management forecasts appear n many dfferent forms. In order to fully utlze the sample forecasts, we use all avalable quanttatve management forecasts. In measurng the forecast errors n equatons (3) and (4), the management pont estmate of earnngs (revenue) s used to proxy for MEF (MRF) when management ssues a pont forecast. When management ssues a range forecast, the mdpont of the range s used and when management ssues a mnmum (maxmum) forecast, the lower (upper) bound s used. In addton, to make t comparable to Value Lne forecasts, a management earnngs forecast s converted to a per share amount f t s not n the form of earnngs per share. A management revenue forecast s converted to a total sales amount and compared to the Value Lne forecasts f t s forecasted on a per share bass. Two cumulatve abnormal returns are measured for forecastng and non-forecastng frms. The frst utlzes abnormal returns from the sngle-ndex model n equaton (1). The second s cumulatve abnormal returns based on the two-ndex model n equaton (). The followng s our cumulatve abnormal return measure: t= + 1 t= - CAR = ξ (5), t,t where the event perod s day - to day +1 relatve to the forecast day and ξ s ether u or e. For the remander of ths paper, the tme and frm subscrpts on CAR wll be suppressed except n the upcomng equatons. Also, for the remander of ths paper, CAR based on sngle-ndex model wll be denoted by MCAR and CAR based on two-ndex model wll be denoted by IMCAR. 1

14 4. Emprcal Results 4.1 Descrptve Statstcs Table 1 shows descrptve statstcs. MCAR ( MCAR ) s CAR for forecast (nonforecast) frms, where CAR s computed for days {-, +1} usng the sngle-ndex prcng model. IMCAR ( IMCAR ) s CAR for forecast (non-forecast) frms, where CAR s computed for days {-, +1} usng the two-ndex prcng model. Panel A presents descrptve statstcs for forecastng frms. The forecastng frm full sample medan value of MCAR ( IMCAR ) s -0.3% (-0.9%), ndcatng that the sample forecasts are on-average bad news. Ths s supported by a full sample medan value for UMEF of -.81% and a full sample medan value for UMRF of -0.37%. Another nterestng fndng s that the medan UMEF value s -5.74% when earnngs forecasts are ssued alone but -.50% when earnngs and revenue forecasts are ssued together. Ths mples the possblty that when the management earnngs forecast surprse s better, management s more lkely to nclude supportng nformaton n the form of a revenue forecast to enhance the belevablty of the management earnngs forecast (Dye, 1986; Jennngs, 1987; Hutton, Mller, and Sknner, 003). Also, as expected, average UMEF and UMRF are hgher for frms wth postve CAR (good news) than for frms wth negatve CAR (bad news). Panel B of table 1 shows the summary statstcs for non-forecastng frms. MCAR and IMCAR are slghtly hgher when forecastng frms have postve CAR compared to when forecastng frms have negatve CAR. Overall, the magntudes of non-forecastng frms returns are much smaller than those of forecastng frms. For example, when the forecastng frms release good news (.e., postve MCAR ), the medan value of non-forecastng frms abnormal returns s -0.13% whle the 13

15 correspondng medan value for forecastng frms s.88%. Smlarly, when the forecastng frms ssue bad news (.e., negatve MCAR ), the medan value of non-forecastng frms abnormal returns s -0.3% whle the correspondng medan value for forecastng frms s 5.84%. [Insert Table 1 about here] 4. Correlaton Analyses Table reports Spearman correlatons for frms whch make earnngs and revenue forecasts together and ther non-forecastng ndustry counterparts. Overall, the results under the sngle-ndex prcng model are smlar to those under the two-ndex prcng model. Panel A shows the correlatons for the entre sample. As expected, there are strong postve correlatons between MCAR (IMCAR ) and both UMEF and UMRF. Also, the correlaton between MCAR (IMCAR ) and MCAR (IMCAR ) s postve and sgnfcant at the one percent level. The relatonshp between UMEF and MCAR s also postve and sgnfcant. In sum, the full sample results gve some ndcaton of postve ntra-ndustry nformaton transfer. Panel B shows correlatons between varables for forecastng frms and sze matched non-forecastng frms. The results reveal a postve assocaton between MCAR and MCAR that s sgnfcant at ether the one or fve percent level, dependng on the news of the forecast. Interestngly, the correlatons between UMRF and both MCAR and IMCAR are negatve, especally when management dscloses a bad news forecast. Therefore, the results suggest that negatve ntra-ndustry nformaton transfers to frms that are close compettors may stem from compettve shfts appearng through management revenue forecasts. Panel C gves correlatons when forecastng frms and non-sze matched non-forecastng frms are matched. When management forecasts good news, there s a strong (weak) postve 14

16 assocaton between UMEF (UMRF) and both MCAR and IMCAR. When management forecasts bad news, there s a sgnfcantly postve relatonshp between MCAR (IMCAR ) and MCAR (IMCAR ). However, there s a weakly postve or nsgnfcant assocaton between UMEF (UMRF) and both MCAR and IMCAR. Overall, the results n Panel C convey that there may be some postve ntra-ndustry nformaton transfer (ndustry commonaltes) between management forecasts and non-forecastng ndustry counterpart frms that are not close compettors of the forecastng frm. [Insert Table about here] 4.3 Intra-Industry Informaton Transfers from Management Revenue and Earnngs Forecasts We prevously made no hypothess on whether or not the total amount of ntra-ndustry nformaton transfer s greater when management revenue forecasts are ssued wth management earnngs forecasts compared to when management earnngs forecasts are ssued alone. To test the total nformaton transfer from forecasters to ndustry-matched non-forecasters, we regress nonforecastng frms cumulatve abnormal returns on forecastng frms cumulatve abnormal returns usng ether MCAR or IMCAR. We form the followng regresson models: MCAR ( or IMCAR ) 0 + α1 = α MCAR ( or IMCAR ) + ε (6) MCAR ( or IMCAR ) = α + α MCAR + α MCAR 0 1 ( or IMCAR ( or IMCAR ) * D ER, ) + ε (7) where equals one f the forecastng frm ssues a revenue forecast wth ts earnngs forecast D ER and zero f the forecastng frm ssues only an earnngs forecast. All other varables were defned earler. 15

17 Regresson equaton (6) s run for 1) frms that forecast earnngs alone and ther ndustrymatched non-forecastng counterparts and ) frms that forecast both earnngs and revenue smultaneously and ther ndustry-matched non-forecastng counterparts. If forecasts of earnngs alone transfer more total wthn-ndustry nformaton than do forecasts of earnngs and revenue together, then α1 wll lkely be stronger postve or negatve for forecasts of earnngs alone, dependng on whether ndustry commonaltes or compettve shfts from nformaton transfers preval. If forecasts of earnngs and revenue smultaneously transfer more total ntra-ndustry nformaton, then α 1 wll lkely be stronger postve or negatve for forecasts of earnngs wth revenue. In equaton (7), the coeffcent α 1 represents the ntra-ndustry nformaton transfer from management earnngs forecasts ssued alone and α 1 + α represents the ntra-ndustry nformaton transfer for management earnngs and revenue forecasts ssued smultaneously. [Insert Table 3 about here] Table 3, Panel A presents the results from equaton (6). When earnngs forecasts are made alone, the coeffcent α1 s postve and sgnfcant at the one percent level whether the sngle-ndex or the two-ndex prcng model s used. A 1% ncrease n MCAR (IMCAR ) leads to a % (0.057%) ncrease n MCAR (IMCAR ). On the other hand, when earnngs and revenue forecasts are ssued together, the total ntra-ndustry nformaton transfer sharply declnes. The coeffcent on MCAR s postve and sgnfcant at the 10% level, but a 1% ncrease n MCAR results n only a 0.034% ncrease n MCAR. The coeffcent on IMCAR s statstcally nsgnfcant. Panel B of table 3 reports further evdence based on regresson equaton (7). The ntra-ndustry nformaton transfer from management ssung an earnng forecast alone s measured by α1 and the total ntra-ndustry transfer from management ssung an earnngs forecast wth a revenue forecast s measured by summng α1 and α. As n Panel A, 16

18 the fndngs show a strong postve ntra-ndustry nformaton transfer from management earnngs forecasts ssued alone. However, the coeffcent on MCAR * D ER (IMCAR * D ER ) s sgnfcantly negatve at the one (fve) percent level. As a result, the sum α 1 + α s sgnfcantly less than the coeffcent α 1. For the sngle-ndex prcng model, α 1 = and α + α and for the two-ndex prcng model, α 1 = and α 1 + α = = Overall, the results presented n table 3 show that the postve assocaton between forecastng frms abnormal returns and non-forecastng frms abnormal returns s much lower when management revenue and earnngs forecasts are ssued smultaneously compared to when management earnngs forecasts are ssued alone. Ths could be due to ether 1) weaker ntrandustry nformaton transfer when management earnngs and revenue forecasts are ssued smultaneously or ) ntra-ndustry nformaton transfers due to ndustry commonaltes beng offset by ntra-ndustry nformaton transfers due to compettve shfts when earnngs forecasts are ssued wth revenue forecasts. Usng unexpected earnngs forecasts, we nvestgate whether management earnngs forecasts transfer more ntra-ndustry nformaton when they are ssued alone than when they are ssued wth revenue forecasts. The followng regresson s run for 1) frms that forecast earnngs alone and ther ndustry-matched non-forecastng counterparts and ) frms that forecast both earnngs and revenue and ther ndustry-matched non-forecastng counterparts. MCAR (or IMCAR ) α 0 + α UMEF + ε = 1 (8) All other varables were prevously defned. If unexpected management earnngs forecasts lead to more ntra-ndustry nformaton transfer when earnngs forecasts are ssued alone compared to when earnngs forecasts are ssued wth revenue forecasts, then α 1 should be stronger postve or 17

19 negatve for frms that forecast earnngs alone, dependng on whether ndustry commonaltes or compettve shfts due to ntra-ndustry nformaton transfers preval. We also use a dummy varable approach that s analogous wth equaton (7): MCAR ( or IMCAR ) α + ε (9) = 0 + α1umef + α UMEF * DER, where all varables were defned earler. Table 4 presents the results. In panel A, when a forecastng frm releases ts earnngs forecast alone, the coeffcent on UMEF s sgnfcantly postve under both prcng models (tvalues = 3.88 and 3.11, respectvely). Ths conveys that when earnngs are forecast alone, an unexpected management earnngs forecast leads to a postve ntra-ndustry nformaton transfer. On the other hand, when earnngs and revenue are forecast together, the coeffcent on UMEF s nsgnfcant. Panel B of table 4 provdes evdence from further tests of our predcton. In both prcng models, the coeffcent on UMEF * D ER, α shows the ncremental ntra-ndustry nformaton transfer from unexpected management earnngs forecasts that are ssued wth revenue forecasts. They are negatve and sgnfcant under both prcng models (t-values = -.0 and -.1, respectvely). Agan, the postve ntra-ndustry nformaton transfer from management earnngs forecasts s sgnfcantly reduced when the earnngs forecasts are ssued wth revenue forecasts. Overall, the fndngs are consstent wth those reported n Panel A. That s, the ntra-ndustry nformaton transfers from management earnngs forecasts are stronger when management earnngs forecasts are ssued alone than when management earnngs and revenue forecasts are ssued smultaneously. [Insert Table 4 about here] 4.4 Intra-Industry Informaton Transfers Based on Extent of Competton 18

20 From Tables 3 and 4, we show that ntra-ndustry nformaton transfer s not a smlar phenomenon for two dfferent types of management forecasts: earnngs forecasts ssued alone, and the smultaneous ssuance of earnngs and revenue forecasts. Further, we observe less sgnfcant ntra-ndustry nformaton transfers when earnngs and revenue forecasts are ssued together. One possble reason for such an nsgnfcant result s that postve and negatve nformaton transfers offset each other n the sample. Therefore n the followng analyses, we focus on the sample of management forecasts wth both earnngs and revenue and attempt to separate out postve and negatve nformaton transfers. Our hypothess one posts that ntra-ndustry nformaton transfers from forecasters to non-compettor (compettor) frms n the same ndustry s postve (negatve). We assume the extent of competton s closely related to whether a non-forecaster s n the smlar sze (market value of equty) category as the forecaster. We run the followng regresson usng full sample and sub-samples, one contanng sze matched non-forecasters and the other ncludng non-sze matched non-forecasters. MCAR or IMCAR ) = α + α UMEF + α UMRF + ε. ( 0 1 (10) We further refne the sub-samples based on the types of forecast news. Thus, we test how both sze matched and non-sze matched non-forecasters react to good and bad news forecasts. Table 5 dsplays the results. From Panel A, when the full sample s used, we fnd no evdence of ntra-ndustry nformaton transfer from UMEF or UMRF. Ths result s consstent wth results reported n Tables 3 and 4. Ths could be due to the offsettng effect of postve nformaton transfers to one group of non-forecastng frms and negatve nformaton transfers to another group of non-forecastng frms. 19