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

Size: px
Start display at page:

Download "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

Can Auto Liability Insurance Purchases Signal Risk Attitude?

Can Auto Liability Insurance Purchases Signal Risk Attitude? Internatonal Journal of Busness and Economcs, 2011, Vol. 10, No. 2, 159-164 Can Auto Lablty Insurance Purchases Sgnal Rsk Atttude? Chu-Shu L Department of Internatonal Busness, Asa Unversty, Tawan Sheng-Chang

More information

DO LOSS FIRMS MANAGE EARNINGS AROUND SEASONED EQUITY OFFERINGS?

DO LOSS FIRMS MANAGE EARNINGS AROUND SEASONED EQUITY OFFERINGS? DO LOSS FIRMS MANAGE EARNINGS AROUND SEASONED EQUITY OFFERINGS? Fernando Comran, Unversty of San Francsco, School of Management, 2130 Fulton Street, CA 94117, Unted States, fcomran@usfca.edu Tatana Fedyk,

More information

An Alternative Way to Measure Private Equity Performance

An Alternative Way to Measure Private Equity Performance An Alternatve Way to Measure Prvate Equty Performance Peter Todd Parlux Investment Technology LLC Summary Internal Rate of Return (IRR) s probably the most common way to measure the performance of prvate

More information

The Journal of Applied Business Research January/February 2010 Volume 26, Number 1

The Journal of Applied Business Research January/February 2010 Volume 26, Number 1 Product Dversfcaton In Compettve R&D-Intensve Frms: An Emprcal Study Of The Computer Software Industry C. Catherne Chang, Elon Unversty, USA ABSTRACT Ths paper studes the effect of dversfcaton nto dfferent

More information

Inequality and The Accounting Period. Quentin Wodon and Shlomo Yitzhaki. World Bank and Hebrew University. September 2001.

Inequality and The Accounting Period. Quentin Wodon and Shlomo Yitzhaki. World Bank and Hebrew University. September 2001. Inequalty and The Accountng Perod Quentn Wodon and Shlomo Ytzha World Ban and Hebrew Unversty September Abstract Income nequalty typcally declnes wth the length of tme taen nto account for measurement.

More information

The Investor Recognition Hypothesis:

The Investor Recognition Hypothesis: The Investor Recognton Hypothess: the New Zealand Penny Stocks Danel JP Cha, Department of Accountng and Fnance, onash Unversty, Clayton 3168, elbourne, Australa, and Danel FS Cho, Department of Fnance,

More information

The Probability of Informed Trading and the Performance of Stock in an Order-Driven Market

The Probability of Informed Trading and the Performance of Stock in an Order-Driven Market Asa-Pacfc Journal of Fnancal Studes (2007) v36 n6 pp871-896 The Probablty of Informed Tradng and the Performance of Stock n an Order-Drven Market Ta Ma * Natonal Sun Yat-Sen Unversty, Tawan Mng-hua Hseh

More information

Management Quality, Financial and Investment Policies, and. Asymmetric Information

Management Quality, Financial and Investment Policies, and. Asymmetric Information Management Qualty, Fnancal and Investment Polces, and Asymmetrc Informaton Thomas J. Chemmanur * Imants Paegls ** and Karen Smonyan *** Current verson: December 2007 * Professor of Fnance, Carroll School

More information

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

Research Division Federal Reserve Bank of St. Louis Working Paper Series Research Dvson Federal Reserve Bank of St. Lous Workng Paper Seres Beyond the Numbers: An Analyss of Optmstc and Pessmstc Language n Earnngs Press Releases Angela K. Davs Jeremy M. Pger and Lsa M. Sedor

More information

Answer: A). There is a flatter IS curve in the high MPC economy. Original LM LM after increase in M. IS curve for low MPC economy

Answer: A). There is a flatter IS curve in the high MPC economy. Original LM LM after increase in M. IS curve for low MPC economy 4.02 Quz Solutons Fall 2004 Multple-Choce Questons (30/00 ponts) Please, crcle the correct answer for each of the followng 0 multple-choce questons. For each queston, only one of the answers s correct.

More information

Informational Content of Option Trading on Acquirer Announcement Return * National Chengchi University. The University of Hong Kong.

Informational Content of Option Trading on Acquirer Announcement Return * National Chengchi University. The University of Hong Kong. Informatonal Content of Opton Tradng on Acqurer Announcement Return * Konan Chan a, b,, L Ge b,, and Tse-Chun Ln b, a Natonal Chengch Unversty b The Unversty of Hong Kong Aprl, 2012 Abstract Ths paper

More information

1. Measuring association using correlation and regression

1. Measuring association using correlation and regression How to measure assocaton I: Correlaton. 1. Measurng assocaton usng correlaton and regresson We often would lke to know how one varable, such as a mother's weght, s related to another varable, such as a

More information

HOUSEHOLDS DEBT BURDEN: AN ANALYSIS BASED ON MICROECONOMIC DATA*

HOUSEHOLDS DEBT BURDEN: AN ANALYSIS BASED ON MICROECONOMIC DATA* HOUSEHOLDS DEBT BURDEN: AN ANALYSIS BASED ON MICROECONOMIC DATA* Luísa Farnha** 1. INTRODUCTION The rapd growth n Portuguese households ndebtedness n the past few years ncreased the concerns that debt

More information

Student Performance in Online Quizzes as a Function of Time in Undergraduate Financial Management Courses

Student Performance in Online Quizzes as a Function of Time in Undergraduate Financial Management Courses Student Performance n Onlne Quzzes as a Functon of Tme n Undergraduate Fnancal Management Courses Olver Schnusenberg The Unversty of North Florda ABSTRACT An nterestng research queston n lght of recent

More information

Corporate Real Estate Sales and Agency Costs of Managerial Discretion

Corporate Real Estate Sales and Agency Costs of Managerial Discretion Corporate Real Estate Sales and Agency Costs of Manageral Dscreton Mng-Long Lee * Department of Fnance Natonal Yunln Unversty of Scence & Technology Yunln, Tawan Mng-Te Lee Department of Accountng Tamkang

More information

Do stock prices underreact to SEO announcements? Evidence from SEO Valuation

Do stock prices underreact to SEO announcements? Evidence from SEO Valuation Do stock prces underreact to SEO announcements? Evdence from SEO Valuaton Amyatosh K. Purnanandam Bhaskaran Swamnathan * Frst Draft: December 2005 Comments Welcome * Purnanandam s an Assstant Professor

More information

Staff Paper. Farm Savings Accounts: Examining Income Variability, Eligibility, and Benefits. Brent Gloy, Eddy LaDue, and Charles Cuykendall

Staff Paper. Farm Savings Accounts: Examining Income Variability, Eligibility, and Benefits. Brent Gloy, Eddy LaDue, and Charles Cuykendall SP 2005-02 August 2005 Staff Paper Department of Appled Economcs and Management Cornell Unversty, Ithaca, New York 14853-7801 USA Farm Savngs Accounts: Examnng Income Varablty, Elgblty, and Benefts Brent

More information

Management Quality and Equity Issue Characteristics: A Comparison of SEOs and IPOs

Management Quality and Equity Issue Characteristics: A Comparison of SEOs and IPOs Management Qualty and Equty Issue Characterstcs: A Comparson of SEOs and IPOs Thomas J. Chemmanur * Imants Paegls ** and Karen Smonyan *** Current verson: November 2009 (Accepted, Fnancal Management, February

More information

Understanding the Impact of Marketing Actions in Traditional Channels on the Internet: Evidence from a Large Scale Field Experiment

Understanding the Impact of Marketing Actions in Traditional Channels on the Internet: Evidence from a Large Scale Field Experiment A research and educaton ntatve at the MT Sloan School of Management Understandng the mpact of Marketng Actons n Tradtonal Channels on the nternet: Evdence from a Large Scale Feld Experment Paper 216 Erc

More information

Are stewardship and decision usefulness complementary of conflicting objectives of financial accounting?

Are stewardship and decision usefulness complementary of conflicting objectives of financial accounting? Are stewardshp and decson usefulness complementary of conflctng objectves of fnancal accountng? Tagung des SFB 649 Ökonomsches Rsko - Motzen 5 June 2007 Joachm Gassen Insttute of Accountng and Audtng Center

More information

Gender differences in revealed risk taking: evidence from mutual fund investors

Gender differences in revealed risk taking: evidence from mutual fund investors Economcs Letters 76 (2002) 151 158 www.elsever.com/ locate/ econbase Gender dfferences n revealed rsk takng: evdence from mutual fund nvestors a b c, * Peggy D. Dwyer, James H. Glkeson, John A. Lst a Unversty

More information

Day-of-the-Week Trading Patterns of Individual and Institutional Investors

Day-of-the-Week Trading Patterns of Individual and Institutional Investors Day-of-the-Week Tradng Patterns of Indvdual and Instutonal Investors Joel N. Morse, Hoang Nguyen, and Hao M. Quach Ths study examnes the day-of-the-week tradng patterns of ndvdual and nstutonal nvestors.

More information

Preliminary version The Availability Heuristic and Investors Reaction to Company-Specific Events

Preliminary version The Availability Heuristic and Investors Reaction to Company-Specific Events Prelmnary verson The Avalablty Heurstc and Investors Reacton to Company-Specfc Events Doron Klger and Andrey Kudryavtsev 1 Abstract Contemporary research documents varous psychologcal aspects of economc

More information

STAMP DUTY ON SHARES AND ITS EFFECT ON SHARE PRICES

STAMP DUTY ON SHARES AND ITS EFFECT ON SHARE PRICES STAMP UTY ON SHARES AN ITS EFFECT ON SHARE PRICES Steve Bond Mke Hawkns Alexander Klemm THE INSTITUTE FOR FISCAL STUIES WP04/11 STAMP UTY ON SHARES AN ITS EFFECT ON SHARE PRICES Steve Bond (IFS and Unversty

More information

HARVARD John M. Olin Center for Law, Economics, and Business

HARVARD John M. Olin Center for Law, Economics, and Business HARVARD John M. Oln Center for Law, Economcs, and Busness ISSN 1045-6333 ASYMMETRIC INFORMATION AND LEARNING IN THE AUTOMOBILE INSURANCE MARKET Alma Cohen Dscusson Paper No. 371 6/2002 Harvard Law School

More information

Journal of Empirical Finance

Journal of Empirical Finance Journal of Emprcal Fnance 16 (2009) 126 135 Contents lsts avalable at ScenceDrect Journal of Emprcal Fnance journal homepage: www.elsever.com/locate/jempfn Costly trade, manageral myopa, and long-term

More information

Institute of Informatics, Faculty of Business and Management, Brno University of Technology,Czech Republic

Institute of Informatics, Faculty of Business and Management, Brno University of Technology,Czech Republic Lagrange Multplers as Quanttatve Indcators n Economcs Ivan Mezník Insttute of Informatcs, Faculty of Busness and Management, Brno Unversty of TechnologCzech Republc Abstract The quanttatve role of Lagrange

More information

Macro Factors and Volatility of Treasury Bond Returns

Macro Factors and Volatility of Treasury Bond Returns Macro Factors and Volatlty of Treasury Bond Returns Jngzh Huang Department of Fnance Smeal Colleage of Busness Pennsylvana State Unversty Unversty Park, PA 16802, U.S.A. Le Lu School of Fnance Shangha

More information

The DAX and the Dollar: The Economic Exchange Rate Exposure of German Corporations

The DAX and the Dollar: The Economic Exchange Rate Exposure of German Corporations The DAX and the Dollar: The Economc Exchange Rate Exposure of German Corporatons Martn Glaum *, Marko Brunner **, Holger Hmmel *** Ths paper examnes the economc exposure of German corporatons to changes

More information

SIMPLE LINEAR CORRELATION

SIMPLE LINEAR CORRELATION SIMPLE LINEAR CORRELATION Smple lnear correlaton s a measure of the degree to whch two varables vary together, or a measure of the ntensty of the assocaton between two varables. Correlaton often s abused.

More information

The impact of hard discount control mechanism on the discount volatility of UK closed-end funds

The impact of hard discount control mechanism on the discount volatility of UK closed-end funds Investment Management and Fnancal Innovatons, Volume 10, Issue 3, 2013 Ahmed F. Salhn (Egypt) The mpact of hard dscount control mechansm on the dscount volatlty of UK closed-end funds Abstract The mpact

More information

Causal, Explanatory Forecasting. Analysis. Regression Analysis. Simple Linear Regression. Which is Independent? Forecasting

Causal, Explanatory Forecasting. Analysis. Regression Analysis. Simple Linear Regression. Which is Independent? Forecasting Causal, Explanatory Forecastng Assumes cause-and-effect relatonshp between system nputs and ts output Forecastng wth Regresson Analyss Rchard S. Barr Inputs System Cause + Effect Relatonshp The job of

More information

Sulaiman Mouselli Damascus University, Damascus, Syria. and. Khaled Hussainey* Stirling University, Stirling, UK

Sulaiman Mouselli Damascus University, Damascus, Syria. and. Khaled Hussainey* Stirling University, Stirling, UK CORPORATE GOVERNANCE, ANALYST FOLLOWING AND FIRM VALUE Sulaman Mousell Damascus Unversty, Damascus, Syra and Khaled Hussaney* Strlng Unversty, Strlng, UK Ths paper s accepted for publcaton at: Corporate

More information

To manage leave, meeting institutional requirements and treating individual staff members fairly and consistently.

To manage leave, meeting institutional requirements and treating individual staff members fairly and consistently. Corporate Polces & Procedures Human Resources - Document CPP216 Leave Management Frst Produced: Current Verson: Past Revsons: Revew Cycle: Apples From: 09/09/09 26/10/12 09/09/09 3 years Immedately Authorsaton:

More information

Online Appendix Supplemental Material for Market Microstructure Invariance: Empirical Hypotheses

Online Appendix Supplemental Material for Market Microstructure Invariance: Empirical Hypotheses Onlne Appendx Supplemental Materal for Market Mcrostructure Invarance: Emprcal Hypotheses Albert S. Kyle Unversty of Maryland akyle@rhsmth.umd.edu Anna A. Obzhaeva New Economc School aobzhaeva@nes.ru Table

More information

Forecasting the Direction and Strength of Stock Market Movement

Forecasting the Direction and Strength of Stock Market Movement Forecastng the Drecton and Strength of Stock Market Movement Jngwe Chen Mng Chen Nan Ye cjngwe@stanford.edu mchen5@stanford.edu nanye@stanford.edu Abstract - Stock market s one of the most complcated systems

More information

High Correlation between Net Promoter Score and the Development of Consumers' Willingness to Pay (Empirical Evidence from European Mobile Markets)

High Correlation between Net Promoter Score and the Development of Consumers' Willingness to Pay (Empirical Evidence from European Mobile Markets) Hgh Correlaton between et Promoter Score and the Development of Consumers' Wllngness to Pay (Emprcal Evdence from European Moble Marets Ths paper shows that the correlaton between the et Promoter Score

More information

THE IMPLIED VOLATILITY OF ETF AND INDEX OPTIONS

THE IMPLIED VOLATILITY OF ETF AND INDEX OPTIONS The Internatonal Journal of Busness and Fnance Research Volume 5 Number 4 2011 THE IMPLIED VOLATILITY OF ETF AND INDEX OPTIONS Stoyu I. Ivanov, San Jose State Unversty Jeff Whtworth, Unversty of Houston-Clear

More information

ADVERSE SELECTION IN INSURANCE MARKETS: POLICYHOLDER EVIDENCE FROM THE U.K. ANNUITY MARKET *

ADVERSE SELECTION IN INSURANCE MARKETS: POLICYHOLDER EVIDENCE FROM THE U.K. ANNUITY MARKET * ADVERSE SELECTION IN INSURANCE MARKETS: POLICYHOLDER EVIDENCE FROM THE U.K. ANNUITY MARKET * Amy Fnkelsten Harvard Unversty and NBER James Poterba MIT and NBER * We are grateful to Jeffrey Brown, Perre-Andre

More information

The Analysis of Covariance. ERSH 8310 Keppel and Wickens Chapter 15

The Analysis of Covariance. ERSH 8310 Keppel and Wickens Chapter 15 The Analyss of Covarance ERSH 830 Keppel and Wckens Chapter 5 Today s Class Intal Consderatons Covarance and Lnear Regresson The Lnear Regresson Equaton TheAnalyss of Covarance Assumptons Underlyng the

More information

Whose Private Benefits of Control. Owners or Managers?

Whose Private Benefits of Control. Owners or Managers? Whose Prvate Benefts of Control Owners or Managers? Joon Ho Hwang Fnance Department Kelley School of Busness Indana Unversty 1309 East Tenth Street Bloomngton, IN 47405 johwang@ndana.edu August, 2004 ABSTRACT

More information

Momentum Trading, Mean Reversal and Overreaction in Chinese Stock Market *

Momentum Trading, Mean Reversal and Overreaction in Chinese Stock Market * Momentum Tradng, Mean Reversal and Overreacton n Chnese Stock Market * YANGRU WU Rutgers Unversty and Hong Kong Insttute for Monetary Research December 2003 (Prelmnary, Comments Welcome) ABSTRACT Whle

More information

Financial Instability and Life Insurance Demand + Mahito Okura *

Financial Instability and Life Insurance Demand + Mahito Okura * Fnancal Instablty and Lfe Insurance Demand + Mahto Okura * Norhro Kasuga ** Abstract Ths paper estmates prvate lfe nsurance and Kampo demand functons usng household-level data provded by the Postal Servces

More information

Problem Set 3. a) We are asked how people will react, if the interest rate i on bonds is negative.

Problem Set 3. a) We are asked how people will react, if the interest rate i on bonds is negative. Queston roblem Set 3 a) We are asked how people wll react, f the nterest rate on bonds s negatve. When

More information

Hot and easy in Florida: The case of economics professors

Hot and easy in Florida: The case of economics professors Research n Hgher Educaton Journal Abstract Hot and easy n Florda: The case of economcs professors Olver Schnusenberg The Unversty of North Florda Cheryl Froehlch The Unversty of North Florda We nvestgate

More information

Working Paper The determinants of the flow of funds of managed portfolios: mutual funds versus pension funds

Working Paper The determinants of the flow of funds of managed portfolios: mutual funds versus pension funds econstor www.econstor.eu Der Open-Access-Publkatonsserver der ZBW Lebnz-Informatonszentrum Wrtschaft The Open Access Publcaton Server of the ZBW Lebnz Informaton Centre for Economcs Del Guerco, Dane; Tkac,

More information

Are Women More Likely to Seek Advice than Men? Evidence from the Boardroom

Are Women More Likely to Seek Advice than Men? Evidence from the Boardroom J. Rsk Fnancal Manag. 2015, 8, 127-149; do:10.3390/jrfm8010127 Artcle OPEN ACCESS Journal of Rsk and Fnancal Management ISSN 1911-8074 www.mdp.com/journal/jrfm Are Women More Lkely to Seek Advce than Men?

More information

How Sets of Coherent Probabilities May Serve as Models for Degrees of Incoherence

How Sets of Coherent Probabilities May Serve as Models for Degrees of Incoherence 1 st Internatonal Symposum on Imprecse Probabltes and Ther Applcatons, Ghent, Belgum, 29 June 2 July 1999 How Sets of Coherent Probabltes May Serve as Models for Degrees of Incoherence Mar J. Schervsh

More information

PRIVATE SCHOOL CHOICE: THE EFFECTS OF RELIGIOUS AFFILIATION AND PARTICIPATION

PRIVATE SCHOOL CHOICE: THE EFFECTS OF RELIGIOUS AFFILIATION AND PARTICIPATION PRIVATE SCHOOL CHOICE: THE EFFECTS OF RELIIOUS AFFILIATION AND PARTICIPATION Danny Cohen-Zada Department of Economcs, Ben-uron Unversty, Beer-Sheva 84105, Israel Wllam Sander Department of Economcs, DePaul

More information

Number of Levels Cumulative Annual operating Income per year construction costs costs ($) ($) ($) 1 600,000 35,000 100,000 2 2,200,000 60,000 350,000

Number of Levels Cumulative Annual operating Income per year construction costs costs ($) ($) ($) 1 600,000 35,000 100,000 2 2,200,000 60,000 350,000 Problem Set 5 Solutons 1 MIT s consderng buldng a new car park near Kendall Square. o unversty funds are avalable (overhead rates are under pressure and the new faclty would have to pay for tself from

More information

The timing ability of hybrid funds of funds

The timing ability of hybrid funds of funds The tmng ablty of hybrd funds of funds Javer Rodríguez* Graduate School of Busness Admnstraton Unversty of Puerto Rco PO 23332 San Juan, PR 00931 Abstract Hybrd mutual funds are funds that nvest n a combnaton

More information

Trade Adjustment and Productivity in Large Crises. Online Appendix May 2013. Appendix A: Derivation of Equations for Productivity

Trade Adjustment and Productivity in Large Crises. Online Appendix May 2013. Appendix A: Derivation of Equations for Productivity Trade Adjustment Productvty n Large Crses Gta Gopnath Department of Economcs Harvard Unversty NBER Brent Neman Booth School of Busness Unversty of Chcago NBER Onlne Appendx May 2013 Appendx A: Dervaton

More information

Talking Numbers: Technical versus Fundamental Recommendations

Talking Numbers: Technical versus Fundamental Recommendations Talkng Numbers: Techncal versus Fundamental Recommendatons Doron Avramov *, Guy Kaplansk **, Ham Levy *** Ths verson: August 20, 2015 Abstract: Ths study assesses the economc value of techncal and fundamental

More information

DEFINING %COMPLETE IN MICROSOFT PROJECT

DEFINING %COMPLETE IN MICROSOFT PROJECT CelersSystems DEFINING %COMPLETE IN MICROSOFT PROJECT PREPARED BY James E Aksel, PMP, PMI-SP, MVP For Addtonal Informaton about Earned Value Management Systems and reportng, please contact: CelersSystems,

More information

Scale Dependence of Overconfidence in Stock Market Volatility Forecasts

Scale Dependence of Overconfidence in Stock Market Volatility Forecasts Scale Dependence of Overconfdence n Stoc Maret Volatlty Forecasts Marus Glaser, Thomas Langer, Jens Reynders, Martn Weber* June 7, 007 Abstract In ths study, we analyze whether volatlty forecasts (judgmental

More information

Traffic-light a stress test for life insurance provisions

Traffic-light a stress test for life insurance provisions MEMORANDUM Date 006-09-7 Authors Bengt von Bahr, Göran Ronge Traffc-lght a stress test for lfe nsurance provsons Fnansnspetonen P.O. Box 6750 SE-113 85 Stocholm [Sveavägen 167] Tel +46 8 787 80 00 Fax

More information

WORKING PAPER SERIES TAKING STOCK: MONETARY POLICY TRANSMISSION TO EQUITY MARKETS NO. 354 / MAY 2004. by Michael Ehrmann and Marcel Fratzscher

WORKING PAPER SERIES TAKING STOCK: MONETARY POLICY TRANSMISSION TO EQUITY MARKETS NO. 354 / MAY 2004. by Michael Ehrmann and Marcel Fratzscher WORKING PAPER SERIES NO. 354 / MAY 2004 TAKING STOCK: MONETARY POLICY TRANSMISSION TO EQUITY MARKETS by Mchael Ehrmann and Marcel Fratzscher WORKING PAPER SERIES NO. 354 / MAY 2004 TAKING STOCK: MONETARY

More information

Are Women Better Loan Officers?

Are Women Better Loan Officers? Are Women Better Loan Offcers? Ths verson: February 2009 Thorsten Beck * CentER, Dept. of Economcs, Tlburg Unversty and CEPR Patrck Behr Goethe Unversty Frankfurt André Güttler European Busness School

More information

Is There A Tradeoff between Employer-Provided Health Insurance and Wages?

Is There A Tradeoff between Employer-Provided Health Insurance and Wages? Is There A Tradeoff between Employer-Provded Health Insurance and Wages? Lye Zhu, Southern Methodst Unversty October 2005 Abstract Though most of the lterature n health nsurance and the labor market assumes

More information

Fixed income risk attribution

Fixed income risk attribution 5 Fxed ncome rsk attrbuton Chthra Krshnamurth RskMetrcs Group chthra.krshnamurth@rskmetrcs.com We compare the rsk of the actve portfolo wth that of the benchmark and segment the dfference between the two

More information

THE EFFECT OF PREPAYMENT PENALTIES ON THE PRICING OF SUBPRIME MORTGAGES

THE EFFECT OF PREPAYMENT PENALTIES ON THE PRICING OF SUBPRIME MORTGAGES THE EFFECT OF PREPAYMENT PENALTIES ON THE PRICING OF SUBPRIME MORTGAGES Gregory Ellehausen, Fnancal Servces Research Program George Washngton Unversty Mchael E. Staten, Fnancal Servces Research Program

More information

The Development of Web Log Mining Based on Improve-K-Means Clustering Analysis

The Development of Web Log Mining Based on Improve-K-Means Clustering Analysis The Development of Web Log Mnng Based on Improve-K-Means Clusterng Analyss TngZhong Wang * College of Informaton Technology, Luoyang Normal Unversty, Luoyang, 471022, Chna wangtngzhong2@sna.cn Abstract.

More information

THE DETERMINANTS OF THE TUNISIAN BANKING INDUSTRY PROFITABILITY: PANEL EVIDENCE

THE DETERMINANTS OF THE TUNISIAN BANKING INDUSTRY PROFITABILITY: PANEL EVIDENCE THE DETERMINANTS OF THE TUNISIAN BANKING INDUSTRY PROFITABILITY: PANEL EVIDENCE Samy Ben Naceur ERF Research Fellow Department of Fnance Unversté Lbre de Tuns Avenue Khéreddne Pacha, 002 Tuns Emal : sbennaceur@eudoramal.com

More information

The Analysis of Outliers in Statistical Data

The Analysis of Outliers in Statistical Data THALES Project No. xxxx The Analyss of Outlers n Statstcal Data Research Team Chrysses Caron, Assocate Professor (P.I.) Vaslk Karot, Doctoral canddate Polychrons Economou, Chrstna Perrakou, Postgraduate

More information

Are stewardship and valuation usefulness compatible or alternative objectives of financial accounting?

Are stewardship and valuation usefulness compatible or alternative objectives of financial accounting? SFB 649 Dscusson Paper 2008-028 Are stewardshp and valuaton usefulness compatble or alternatve objectves of fnancal accountng? Joachm Gassen* * Humboldt-Unverstät zu Berln, Germany SFB 6 4 9 E C O N O

More information

Heterogeneous Paths Through College: Detailed Patterns and Relationships with Graduation and Earnings

Heterogeneous Paths Through College: Detailed Patterns and Relationships with Graduation and Earnings Heterogeneous Paths Through College: Detaled Patterns and Relatonshps wth Graduaton and Earnngs Rodney J. Andrews The Unversty of Texas at Dallas and the Texas Schools Project Jng L The Unversty of Tulsa

More information

Module 2 LOSSLESS IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur

Module 2 LOSSLESS IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur Module LOSSLESS IMAGE COMPRESSION SYSTEMS Lesson 3 Lossless Compresson: Huffman Codng Instructonal Objectves At the end of ths lesson, the students should be able to:. Defne and measure source entropy..

More information

The Short-term and Long-term Market

The Short-term and Long-term Market A Presentaton on Market Effcences to Northfeld Informaton Servces Annual Conference he Short-term and Long-term Market Effcences en Post Offce Square Boston, MA 0209 www.acadan-asset.com Charles H. Wang,

More information

Analyzing Search Engine Advertising: Firm Behavior and Cross-Selling in Electronic Markets

Analyzing Search Engine Advertising: Firm Behavior and Cross-Selling in Electronic Markets WWW 008 / Refereed Track: Internet Monetzaton - Sponsored Search Aprl -5, 008 Beng, Chna Analyzng Search Engne Advertsng: Frm Behavor and Cross-Sellng n Electronc Markets Anndya Ghose Stern School of Busness

More information

KEY PERFORMANCE INDICATORS AND ANALYSTS' EARNINGS FORECAST ACCURACY: AN APPLICATION OF CONTENT ANALYSIS

KEY PERFORMANCE INDICATORS AND ANALYSTS' EARNINGS FORECAST ACCURACY: AN APPLICATION OF CONTENT ANALYSIS ASIAN ACADEMY of MANAGEMENT JOURNAL of ACCOUNTING and FINANCE AAMJAF, Vol. 7, No. 2, 79 102, 2011 KEY PERFORMANCE INDICATORS AND ANALYSTS' EARNINGS FORECAST ACCURACY: AN APPLICATION OF CONTENT ANALYSIS

More information

Chapter 15: Debt and Taxes

Chapter 15: Debt and Taxes Chapter 15: Debt and Taxes-1 Chapter 15: Debt and Taxes I. Basc Ideas 1. Corporate Taxes => nterest expense s tax deductble => as debt ncreases, corporate taxes fall => ncentve to fund the frm wth debt

More information

Accounting Discretion of Banks During a Financial Crisis

Accounting Discretion of Banks During a Financial Crisis WP/09/207 Accountng Dscreton of Banks Durng a Fnancal Crss Harry Huznga and Luc Laeven 2009 Internatonal Monetary Fund WP/09/207 IMF Workng Paper Research Department Accountng dscreton of banks durng a

More information

CHAPTER 14 MORE ABOUT REGRESSION

CHAPTER 14 MORE ABOUT REGRESSION CHAPTER 14 MORE ABOUT REGRESSION We learned n Chapter 5 that often a straght lne descrbes the pattern of a relatonshp between two quanttatve varables. For nstance, n Example 5.1 we explored the relatonshp

More information

A Multistage Model of Loans and the Role of Relationships

A Multistage Model of Loans and the Role of Relationships A Multstage Model of Loans and the Role of Relatonshps Sugato Chakravarty, Purdue Unversty, and Tansel Ylmazer, Purdue Unversty Abstract The goal of ths paper s to further our understandng of how relatonshps

More information

ADVERSE SELECTION IN INSURANCE MARKETS: POLICYHOLDER EVIDENCE FROM THE U.K. ANNUITY MARKET

ADVERSE SELECTION IN INSURANCE MARKETS: POLICYHOLDER EVIDENCE FROM THE U.K. ANNUITY MARKET ADVERSE SELECTION IN INSURANCE MARKETS: POLICYHOLDER EVIDENCE FROM THE U.K. ANNUITY MARKET Amy Fnkelsten Harvard Unversty and NBER James Poterba MIT and NBER Revsed May 2003 ABSTRACT In ths paper, we nvestgate

More information

PSYCHOLOGICAL RESEARCH (PYC 304-C) Lecture 12

PSYCHOLOGICAL RESEARCH (PYC 304-C) Lecture 12 14 The Ch-squared dstrbuton PSYCHOLOGICAL RESEARCH (PYC 304-C) Lecture 1 If a normal varable X, havng mean µ and varance σ, s standardsed, the new varable Z has a mean 0 and varance 1. When ths standardsed

More information

This study examines whether the framing mode (narrow versus broad) influences the stock investment decisions

This study examines whether the framing mode (narrow versus broad) influences the stock investment decisions MANAGEMENT SCIENCE Vol. 54, No. 6, June 2008, pp. 1052 1064 ssn 0025-1909 essn 1526-5501 08 5406 1052 nforms do 10.1287/mnsc.1070.0845 2008 INFORMS How Do Decson Frames Influence the Stock Investment Choces

More information

Section 5.3 Annuities, Future Value, and Sinking Funds

Section 5.3 Annuities, Future Value, and Sinking Funds Secton 5.3 Annutes, Future Value, and Snkng Funds Ordnary Annutes A sequence of equal payments made at equal perods of tme s called an annuty. The tme between payments s the payment perod, and the tme

More information

Do Banks Use Private Information from Consumer Accounts? Evidence of Relationship Lending in Credit Card Interest Rate Heterogeneity

Do Banks Use Private Information from Consumer Accounts? Evidence of Relationship Lending in Credit Card Interest Rate Heterogeneity Do Banks Use Prvate Informaton from Consumer Accounts? Evdence of Relatonshp Lendng n Credt Card Interest Rate Heterogenety Sougata Kerr, Stephen Cosslett, Luca Dunn December, 2004 Author nformaton: Kerr,

More information

Section 5.4 Annuities, Present Value, and Amortization

Section 5.4 Annuities, Present Value, and Amortization Secton 5.4 Annutes, Present Value, and Amortzaton Present Value In Secton 5.2, we saw that the present value of A dollars at nterest rate per perod for n perods s the amount that must be deposted today

More information

Statistical Methods to Develop Rating Models

Statistical Methods to Develop Rating Models Statstcal Methods to Develop Ratng Models [Evelyn Hayden and Danel Porath, Österrechsche Natonalbank and Unversty of Appled Scences at Manz] Source: The Basel II Rsk Parameters Estmaton, Valdaton, and

More information

THE DISTRIBUTION OF LOAN PORTFOLIO VALUE * Oldrich Alfons Vasicek

THE DISTRIBUTION OF LOAN PORTFOLIO VALUE * Oldrich Alfons Vasicek HE DISRIBUION OF LOAN PORFOLIO VALUE * Oldrch Alfons Vascek he amount of captal necessary to support a portfolo of debt securtes depends on the probablty dstrbuton of the portfolo loss. Consder a portfolo

More information

benefit is 2, paid if the policyholder dies within the year, and probability of death within the year is ).

benefit is 2, paid if the policyholder dies within the year, and probability of death within the year is ). REVIEW OF RISK MANAGEMENT CONCEPTS LOSS DISTRIBUTIONS AND INSURANCE Loss and nsurance: When someone s subject to the rsk of ncurrng a fnancal loss, the loss s generally modeled usng a random varable or

More information

Hedge Fund Investing in the Aftermath of the Crisis: Where did the Money Go?

Hedge Fund Investing in the Aftermath of the Crisis: Where did the Money Go? Hedge Fund Investng n the Aftermath of the Crss: Where dd the Money Go? Gudo Bollger, Ivan Gudott, Florent Pochon Ths verson: July 2010 Abstract Ths paper nvestgates the determnants of hedge fund flows

More information

Construction Rules for Morningstar Canada Target Dividend Index SM

Construction Rules for Morningstar Canada Target Dividend Index SM Constructon Rules for Mornngstar Canada Target Dvdend Index SM Mornngstar Methodology Paper October 2014 Verson 1.2 2014 Mornngstar, Inc. All rghts reserved. The nformaton n ths document s the property

More information

Survive Then Thrive: Determinants of Success in the Economics Ph.D. Program. Wayne A. Grove Le Moyne College, Economics Department

Survive Then Thrive: Determinants of Success in the Economics Ph.D. Program. Wayne A. Grove Le Moyne College, Economics Department Survve Then Thrve: Determnants of Success n the Economcs Ph.D. Program Wayne A. Grove Le Moyne College, Economcs Department Donald H. Dutkowsky Syracuse Unversty, Economcs Department Andrew Grodner East

More information

Criminal Justice System on Crime *

Criminal Justice System on Crime * On the Impact of the NSW Crmnal Justce System on Crme * Dr Vasls Sarafds, Dscplne of Operatons Management and Econometrcs Unversty of Sydney * Ths presentaton s based on jont work wth Rchard Kelaher 1

More information

1.1 The University may award Higher Doctorate degrees as specified from time-to-time in UPR AS11 1.

1.1 The University may award Higher Doctorate degrees as specified from time-to-time in UPR AS11 1. HIGHER DOCTORATE DEGREES SUMMARY OF PRINCIPAL CHANGES General changes None Secton 3.2 Refer to text (Amendments to verson 03.0, UPR AS02 are shown n talcs.) 1 INTRODUCTION 1.1 The Unversty may award Hgher

More information

Course outline. Financial Time Series Analysis. Overview. Data analysis. Predictive signal. Trading strategy

Course outline. Financial Time Series Analysis. Overview. Data analysis. Predictive signal. Trading strategy Fnancal Tme Seres Analyss Patrck McSharry patrck@mcsharry.net www.mcsharry.net Trnty Term 2014 Mathematcal Insttute Unversty of Oxford Course outlne 1. Data analyss, probablty, correlatons, vsualsaton

More information

The Current Employment Statistics (CES) survey,

The Current Employment Statistics (CES) survey, Busness Brths and Deaths Impact of busness brths and deaths n the payroll survey The CES probablty-based sample redesgn accounts for most busness brth employment through the mputaton of busness deaths,

More information

TESTING FOR EVIDENCE OF ADVERSE SELECTION IN DEVELOPING AUTOMOBILE INSURANCE MARKET. Oksana Lyashuk

TESTING FOR EVIDENCE OF ADVERSE SELECTION IN DEVELOPING AUTOMOBILE INSURANCE MARKET. Oksana Lyashuk TESTING FOR EVIDENCE OF ADVERSE SELECTION IN DEVELOPING AUTOMOBILE INSURANCE MARKET by Oksana Lyashuk A thess submtted n partal fulfllment of the requrements for the degree of Master of Arts n Economcs

More information

An Empirical Study of Search Engine Advertising Effectiveness

An Empirical Study of Search Engine Advertising Effectiveness An Emprcal Study of Search Engne Advertsng Effectveness Sanjog Msra, Smon School of Busness Unversty of Rochester Edeal Pnker, Smon School of Busness Unversty of Rochester Alan Rmm-Kaufman, Rmm-Kaufman

More information

Corporate Governance and Value Creation: Evidence from Private Equity 1

Corporate Governance and Value Creation: Evidence from Private Equity 1 Corporate Governance and Value Creaton: Evdence from Prvate Equty 1 by Vral V. Acharya, Olver Gottschalg, Mortz Hahn and Conor Kehoe Frst draft: 7 Aprl 2008 Ths draft: 2 June 2011 Contact nformaton: Vral

More information

LIFETIME INCOME OPTIONS

LIFETIME INCOME OPTIONS LIFETIME INCOME OPTIONS May 2011 by: Marca S. Wagner, Esq. The Wagner Law Group A Professonal Corporaton 99 Summer Street, 13 th Floor Boston, MA 02110 Tel: (617) 357-5200 Fax: (617) 357-5250 www.ersa-lawyers.com

More information

Factors Affecting Outsourcing for Information Technology Services in Rural Hospitals: Theory and Evidence

Factors Affecting Outsourcing for Information Technology Services in Rural Hospitals: Theory and Evidence Factors Affectng Outsourcng for Informaton Technology Servces n Rural Hosptals: Theory and Evdence Bran E. Whtacre Department of Agrcultural Economcs Oklahoma State Unversty bran.whtacre@okstate.edu J.

More information

The Effects of Tax Rate Changes on Tax Bases and the Marginal Cost of Public Funds for Canadian Provincial Governments

The Effects of Tax Rate Changes on Tax Bases and the Marginal Cost of Public Funds for Canadian Provincial Governments The Effects of Tax Rate Changes on Tax Bases and the Margnal Cost of Publc Funds for Canadan Provncal Governments Bev Dahlby a and Ergete Ferede b a Department of Economcs, Unversty of Alberta, Edmonton,

More information

Beating the Odds: Arbitrage and Wining Strategies in the Football Betting Market

Beating the Odds: Arbitrage and Wining Strategies in the Football Betting Market Beatng the Odds: Arbtrage and Wnng Strateges n the Football Bettng Market NIKOLAOS VLASTAKIS, GEORGE DOTSIS and RAPHAEL N. MARKELLOS* ABSTRACT We examne the potental for generatng postve returns from wagerng

More information