Expecaion Heerogeneiy in Japanese Sock Index

Transcription

1 JCER DISCUSSION PAPER No.136 Belief changes and expecaion heerogeneiy in buy- and sell-side professionals in he Japanese sock marke Ryuichi Yamamoo and Hideaki Hiraa February 2012 公 益 社 団 法 人 日 本 経 済 研 究 センター Japan Cener for Economic Research

2 Belief changes and expecaion heerogeneiy in buy- and sell-side professionals in he Japanese sock marke Ryuichi Yamamoo a and Hideaki Hiraa b a Deparmen of Inernaional Business, Naional Chengchi Universiy, Taipei 116, Taiwan b Japan Cener for Economic Research, Tokyo, Japan and Deparmen of Business Adminisraion, Hosei Universiy, Tokyo, Japan February, 2012 Absrac We documen he deerminans of he expecaion heerogeneiy of sock price forecasers on he TOPIX. Monhly panel daa surveyed by QUICK Corporaion in he Nikkei Group is uilized in he process. We examine he deerminans of expecaion heerogeneiy by caegorizing our sample ino buy-side and sell-side professionals, and demonsrae ha expecaion heerogeneiy arises as a resul of he co-exisence of differen ypes of professionals wihin he same marke. We show ha he buy-side and he sell-side professionals, who have differen business goals, differeniae he informaion conens as well as heir inerpreaions of he same informaion in heir forecass, conribuing o he expecaion heerogeneiy. In addiion, we invesigae he ineracive expecaions formulaion of buy-side and sell-side professionals. We find ha buy-side professionals incorporae he sell side s ideas regarding he fuure sock prices ino heir own forecass, alhough hey exclusively refer o heir own ideas when relaing foreign exchange raes o he fuure sock prices. Meanwhile, sell-side professionals end o uilize buy-side professionals ideas abou fuure prices in order o improve heir research and ingraiae hemselves o heir cliens, i.e., he buy-side professionals. We demonsrae ha his ineracive expecaions formulaion also conribues o he generaion of he expecaion heerogeneiy. JEL classificaion: G14, D84 Keywords: Expecaion heerogeneiy, dispersion, survey daa address: ryuichi@nccu.edu.w (Yamamoo) and h-hiraa@hosei.ac.jp (Hiraa). 1

3 1.Inroducion In conras wih he common assumpion abou he radiional raional represenaive agen, several papers invesigae survey daa on professional forecass of such macroeconomic series as inflaion and GDP, as well as such financial series as sock prices and foreign exchange raes and find expecaions o be heerogeneous. 1 While Mankiw, Reis, and Wolfers (2003) sugges ha disagreemen may be a key o macroeconomic dynamics (p.242), several recen agen-based models demonsrae ha heerogeneiy drives observed feaures in real sock markes ha have no ye been sufficienly explained by radiional asse-pricing models under efficien marke and raional expecaion hypoheses, such as clusered volailiy and fa ails of he reurn disribuion. 2 Thus, providing beer explanaions of he facors deermining he differences in expecaions can faciliae a beer undersanding of risk managemen and opion pricing in financial markes. While several sudies have examined he deerminans of expecaion heerogeneiy in inflaion, GDP or foreign exchange raes, recen empirical research has faced he challenge of explaining expecaion heerogeneiy among sock marke professionals. This paper empirically examines he deerminans of expecaion heerogeneiy or dispersion in he Japanese sock marke uilizing a panel daase of monhly surveys of marke professionals on he TOPIX forecass, conduced by QUICK Corporaion, a Japanese financial informaion vendor in he Nikkei Group. The academic lieraure offers hree explanaions of he sources of expecaion 1 For example, Allen and Taylor (1990), Io (1990), and Frankel and Froo (1990) idenify expecaion heerogeneiy in foreign exchange markes, while Mankiw, Reis, and Wolfers (2003) and Capisran and Timmermann (2009) find heerogeneiy in inflaion expecaions. Paon and Timmermann (2010) demonsrae expecaion heerogeneiy for GDP growh and inflaion. 2 For example, Hommes (2006) and LeBaron (2006) survey he lieraure on agen-based compuaional finance and explain he imporance of heerogeneiy in generaing financial marke phenomena. 2

4 heerogeneiy. 3 One explanaion is based on he idea ha forecasers share he same informaion-processing echnology, bu have access o differen ses of informaion abou he curren sae of he economy (see, for example, Carroll, 2003; Kyle, 1985; Lucas, 1973; Mankiw and Reis, 2002). The second explanaion in he lieraure indicaes ha agens who share he same informaion abou he curren sae of he economy inerpre i differenly (see, for example, Laser, Benne, and Geoum, 1999; Paon and Timmermann, 2010). A hird possibiliy offered is ha he forecas dispersion arises due o he exisence of fundamenally differen ypes of agens in he marke (for example, in he noise-raders and raional-arbirageurs model presened by De Long, Shleifer, Summers, and Waldmann (1990) and a series of fundamenaliss and chariss models). 4 Due o he difference in ypes, agens in he hird srand of lieraure no only observe differen informaion, bu also have differen ways o inerpre he same informaion. Thus, an implicaion in he hird srand of lieraure overlaps he explanaions in he firs and second srands of lieraure. We invesigae wheher or no his hird asserion in lieraure can be empirically validaed in he Japanese sock marke. In paricular, we explore why professionals expecaions are heerogeneous by disaggregaing he forecass in our sample offered by professionals ino hose of fundamenally differen ypes, namely, ino buy- and sell-side professionals. Buy-side professionals are hose who work for invesing insiuions, such as muual funds, pension funds, and insurance firms, which purchase securiies on heir own accoun. Buy-side analyss research and make recommendaions o heir own insiuions invesors regarding purchasing securiies. Buy-side recommendaions are usually no available o he 3 We refer o Frijns, Lehner, and Zwinkels (2010) for caegorizing he lieraure ino hree srands. 4 See, for example, Hommes (2006) and LeBaron (2006), who survey papers on agen-based compuaional finance. Boswijk, Hommes, and Manzan (2007), Branch (2004), Frankel and Froo (1990), Menkhoff, Rebizky, and Schröder (2009), and Reiz, Sadmann, and Taylor (2009) empirically demonsrae ha he exisence of fundamenaliss and chariss in he same marke generaes he forecas dispersion. 3

5 public. Sell-side professionals work for companies which sell invesmen services o invesors, ha is o say, he buy-side professionals, and provide recommendaions o he public. Sell-side analyss work for brokerage firms; heir research is used o promoe securiies o buy-side invesors. 5 We demonsrae ha our resuls are consisen wih he explanaions offered by he hird srand of he lieraure in ways oulined below. We firs demonsrae ha buy-side and sell-side professionals uilize differen informaion o make heir forecass. Even if hey observe he same informaion, hey ofen inerpre he informaion differenly, resuling in varied expecaions. Secondly, we demonsrae ha cerain forms of informaion exchanges ake place beween he buy-side and he sell-side professionals ha generaes heerogeneiy in expecaions. More precisely, we demonsrae ha he buy-side professionals refer o he way in which sell-side professionals evaluae he marke, paricularly when he sell-side professionals share opinions ha resemble o hose of he buy-side professionals. Meanwhile, he buy-side professionals do no ake his acion when aemping o relae foreign exchange raes o fuure sock prices. On he oher hand, sell-side professionals seek o share marke views similar o hose of heir cusomers, ha is o say, o buy-side professionals. Our resuls imply ha expecaion heerogeneiy arises because buy-side and sell-side professionals having differen business goals inerac wih one anoher and hey differeniae he conens of he informaion as well as heir inerpreaions of he same informaion in heir forecass. Thus, we conclude ha he exisence of fundamenally differen ypes of professionals wihin he same marke is a key o generaing he dispersion. In addiion, we demonsrae he robusness of our resuls afer conrolling for imporan evens in he Japanese economy during our sample periods, such as he Lehman 5 For more informaion on he differen aciviies in which buy-side and sell-side professionals engage, see Groysberg, Healy, and Chapman (2008) and Busse, Green, and Jegadeesh (forhcoming). 4

6 shock, he Bear Searns shock, he Resona shock, he merger of he Misubishi Tokyo Financial Group and UFJ Holdings, he quaniaive easing moneary policy, he selemen of he accoun in each fiscal year, and he January effec. This paper makes he following six conribuions. Firs, we empirically explain he deerminans of he expecaion dispersion among he Japanese sock marke professionals. Several papers invesigae he sources of he dispersion in expecaions of exchange raes, inflaion, GDP, and unemploymen, bu no specifically of he expecaions of Japanese sock marke professionals. 6 Second, we demonsrae he causes of he forecas dispersion relaed o he sock index using professionals opinions abou he various macroeconomic, poliical, and psychological facors ha influence fuure sock prices. The QUICK corporaion asks respondens o selec he facors ha influence fuure sock prices from among he following facors: Business condiions, Ineres raes, Foreign exchange raes, Poliics and diplomacy, Inernal facors and marke psychology in sock markes, and Sock and bond markes abroad. These macroeconomic, poliical, and psychological facors are among he mos likely candidaes o explain he sock index price forecass. Our panel daase enables us o direcly relae professionals ideas on hese facors o he expecaion dispersion. This approach is differen from ha in previous papers, such as Lamon (2002), which explains he expecaion dispersion by using he forecasers age and repuaion. Third, we empirically analyze boh buy-side and sell-side professionals dispersions of he sock index forecass. Several papers invesigae he behavior of sell-side invesors from a cross-secional viewpoin, bu heir effors focus exclusively on he sell-side 6 See, for example, Menkhoff, Rebizky, and Schröder (2009) and Reiz, Sadmann, and Taylor (2009) for heerogeneiy in exchange rae expecaions, Mankiw, Reis, and Wolfers (2003) and Capisran and Timmermann (2009), for heerogeneiy in inflaion, Paon and Timmermann (2008) and Döpke and Frische (2006) for heerogeneiy in boh GDP and inflaion, and Lamon (2002) for he heerogeneiy in GDP, inflaion, and unemploymen. 5

7 professionals. 7 Accordingly o Groysberg, Healy, and Chapman (2008), his acion is due o a lack of daa on buy-side professionals. Among he relaively limied amoun of research conduced on buy-side professionals, Cowen, Groysberg, and Healy (2006) and Groysberg, Healy, and Chapman (2008) examine he forecass made by boh buy-side and sell-side professionals, bu focus on individual socks and do no characerize he forecas dispersion of buy-side and sell-side professionals. Fourh, we empirically idenify he ypes of professionals who acually drive he forecas dispersion. We demonsrae ha he buy-side and sell-side professionals significanly impac he dispersion. The hird srand of lieraure menioned above poses he idea ha he exisence of differen ypes of professionals wihin he same marke generaes he forecas dispersion, such as noise raders and raional arbirageurs in he noise-rader model and fundamenaliss and chariss in agen-based models. Noneheless, hose papers idenify neiher he ype of financial insiuions o which noise raders, raional arbirageurs, fundamenaliss, and chariss specifically belong nor heir respecive business caegories. Fifh, we demonsrae ha a form of informaion exchange beween buy-side and sell-side professionals exiss, which deermines he forecas dispersion. The research of sell-side professionals is usually available o he public in realiy, whereas ha of buy-side professionals is conduced exclusively for buy-side firms porfolio managers (Cheng, Liu, and Qian, 2006). However, i is no empirically validaed as o wheher or no hey uilize each oher s analyses in making heir forecass. Even if hey do, he informaion from he sell-side professionals ha he buy-side professionals use and he informaion from he buy-side professionals ha he sell-side professionals uilize in making heir forecass remain unknown. 8 7 See, for example, Clemen (1999) and Hong and Kubik (2003). 8 Busse, Green, and Jegadeesh (forhcoming) find sell-side analyss recommendaions o be informaive o he buy-side professionals bu do no find he reverse o be rue. 6

8 Sixh, in addiion o analyzing he relaionship beween professionals behavior and he expecaion dispersion, we examine he impacs of imporan economic and financial evens on he dispersion. Those evens include he global financial crises, he naionalizaion of Resona Bank, and he merger of he Misubishi Tokyo Financial Group and UFJ Holdings, ha have caused imporan srucural changes in he Japanese financial markes. Such an approach can be aken wih our daase, as our sample covers he pas 10 years in which hese evens have occurred. The paper is organized as follows. Secion 2 presens lieraure review of he sources of dispersion. Secion 3 inroduces deails regarding our daase and derives various empirical hypoheses regarding he cause of dispersion. Secion 4 ess hese hypoheses, and Secion 5 checks he robusness of our resuls. The las secion conains concluding remarks. 2. Lieraure One explanaion for he expecaion dispersion is ha he dispersion arises when agens have access o heerogeneous informaion abou he curren sae of he economy. For example, in he islands model presened by Lucas (1973), expecaion heerogeneiy wih respec o prices and inflaion arises when producers on separae islands have access o differen informaion. The sicky-informaion models of Mankiw and Reis (2002) and Carroll (2003) sugges ha agens asynchronous updaing of informaion ses generaes he disagreemen in expeced inflaion rae. In he field of marke microsrucure, for example, Kyle (1985) demonsraes ha he asymmery of he informaion available o informed and uninformed raders drives forecas dispersions. The second srand of he lieraure mainains ha he dispersion arises when agens inerpre he same informaion abou he curren sae of he economy differenly. For example, Laser, Benne, and Geoum (1999) and Paon and Timmermann (2008) demonsrae by using heir macroeconomic survey daases ha expecaion dispersions arise in siuaions in which no difference exiss in access o 7

9 informaion used o make forecass. The hird srand of he lieraure ceners on he idea ha he forecass are heerogeneous because agens are fundamenally differen due o he diversiy in heir informaion ses and rading sraegies. For example, according o he noise-rader model of DeLong, Schleifer, Summers, and Waldmann (1990), noise raders respond o noise, raher han o informaion on he curren sae of he economy, because hey inerpre he noise as useful informaion ha will generae profis if hey base rade on he noise. Meanwhile, raional arbirageurs form fully raional expecaions of he sock prices. Thus, expecaions become heerogeneous in he noise rader model, due o he fac ha noise raders and raional arbirageurs uilize differen informaion ses and differen informaion-processing echnologies in heir rading sraegies. Several agen-based models, popularly exemplified by a model creaed by Brock and Hommes (1998), assume wo differen ypes of agens in he marke, ermed fundamenaliss and echnical raders. Fundamenaliss expec fuure prices o revolve around he fundamenal price of he asse, whereas echnical raders expecaions are posiively relaed o recen price movemens if agens are momenum raders and negaively relaed o recen price movemens if hey are conrarians. Expecaions in he agen-based models are heerogeneous as a resul of he differences in heir informaion and informaion-processing echnologies ha fundamenaliss and echnical raders use in formulaing heir rading sraegies. In his sense, he explanaions by he hird srand of lieraure cover implicaions from boh of he firs and second srands of lieraure. Our empirical research is conduced in line wih his hird srand of he lieraure. We disaggregae our sample ino buy-side and sell-side professionals, using he responden informaion in our daase as explained in Secion 3.1., and deermine he dispersions of he wo ypes. Esablishing he deerminans of he buy-side and sell-side dispersions will conribue o explaining he forecas heerogeneiy among all professionals. The nex secion will define he forecas dispersion as he sandard deviaion of he forecass. Bu he 8

10 following decomposiion of forecass variance shed furher insighs on undersanding on he saemen. The variance of he forecass by all professionals a is given by: V all n m 1 buy side, i 2 sell side, j 2 = ( F ) + ( ) F F F n + m i= 1 j= 1 (1) where buy side, i F is he forecas by a buy-side professional i, sell side, j F is ha by a sell-side professional j, n is he number of buy-side professionals, and m is he number of sell-side professionals. F is he average forecas over all professionals, i.e., buy- and sell-side professionals, and is given by: F n m 1 buy side, i sell side, j = ( F ) + ( ) F n + m i= 1 j= 1 where he sum of n and m is he number of all professionals. Defining i V as he variance of forecass by i where i = all, buy-side or sell-side professionals, equaion (1) is re-expressed as: V all n m ( ) ( buy side sell side buy side sell side = V V F 2 F n )2 m + n m + (2) + + n + m n m where buy side F and sell side F are he mean forecass of buy-side and sell-side professionals, respecively. The firs and second erms in equaion (2) are he weighed average of forecass variances of buy-side and sell-side professionals and he hird erm is he adjusmen erm of he level-difference of he mean forecass of buy-side and sell-side professionals. Plugging he acual values ino equaion (2), he effecs of forecass variances of buy-side and sell-side professionals on V all look much larger han hose of he fracions of he buy-side and sell-side professionals ( ( n n + m ) and ( n m ) m +, respecively) and he hird erm. 9

11 Compuing he correlaion coefficiens beween all V and he variance of he forecass by each professional, we find ha all V is highly and posiively correlaed wih sell side V (0.80) and buy side V (0.96), respecively. 9 Thus, we focus on he deerminans of he dispersions in he buy-side and sell-side professionals, in order o pin down he dispersion in all professionals. While validaing he asserion presened in he hird srand of lieraure, we also characerize he forecasing behavior of buy-side and sell-side professionals. In paricular, we invesigae wheher or no buy-side (sell-side) forecasers refer o and uilize informaion abou he ways in which sell-side (buy-side) professionals evaluae he marke in order o forecas fuure prices. This issue has no been saisfacorily clarified in previous empirical papers. 10 In order o achieve hese goals, we presen hree hypoheses o es empirically afer deailing our daase in he nex secion. 3. Daa and Hypoheses 3.1. Daa Our analyses rely on a monhly panel daase gahered in surveys conduced by 9 The correlaion coefficiens beween all V and he fracion of he buy-side professionals, he fracion of he sell-side professionals, and he las erm in equaion (2) are 32%, -32%, and 36%, respecively. 10 Previous papers, such as Clemen (1999) and Hong and Kubik (2003), invesigae sell-side behaviors, while recen lieraure examines boh buy-side and sell-side behaviors, (see, for example, Busse, Green, and Jegadeesh, forhcoming; Cowen, Groysberg, and Healy, 2006; Groysberg, Healy, and Chapman, 2008). Cowen, Groysberg, and Healy (2006) and Groysberg, Healy, and Chapman (2008) mainly focus on he difference in he degree of opimism beween buy-side professionals and sell-side professionals. They find ha sell-side professionals make more opimisic forecass and recommendaions han do buy-side professionals. On he oher hand, he findings of Busse, Green, and Jegadeesh (forhcoming) indicae ha buy-side rades follow sell-side analyss recommendaions, whereas sell-side rades do no follow he recommendaions of buy-side analyss. 10

12 QUICK Corporaion. The daase we use covers a period of 117 monhs, from June 2000 hrough February 2010 and includes he one-monh ahead expecaions for he TOPIX provided by a oal of 1,132 professionals. The average number of respondens each monh is 182.0, and each forecaser replied an average of 20.5 imes. The survey is usually conduced a he beginning of each monh over he course of hree consecuive days, wih he las of hese days aking place on he firs Thursday of he monh, and he survey repor released on he following Monday. The published repor solely includes summarized survey resuls, such as he mean, sandard deviaion, median, minimum, and maximum of he forecass, and so forh. However, our daase conains he survey resuls of each responden and also includes such informaion as he individual code and company code of each responden, enabling us o rack he forecas record of a paricular individual and firm over ime, alhough no all of he professionals replied o he survey for he full ime period of he sudy Buy-side and sell-side professionals We caegorize he respondens ino buy- and sell-side professionals, using he informaion for each responden presened in wo columns of he daase, which are labeled assigned work and business caegory. 11 Wih respec o he assigned work column, a responden is caegorized as he buy-side professional if he/she is in charge of managing: 1) his/her company s own funds, 2) pension funds, 3) funds placed in rus (excluding pension purposes), 4) funds placed in rus (including pension purposes), 5) invesmen rus, or 6) proprieary rading. (These subcaegories are denoed B1, B2, B3, B4, B5, and B6, respecively). A responden is defined as he sell-side professional if he/she is involved 11 This caegorizaion is primarily based upon he papers reviewed in Secion 2, such as ha of Groysberg, Healy, and Chapman (2008). In addiion, we asked various Japanese marke professionals abou our caegorizaions ino buy-side and sell-side professionals. In paricular, we hank Hideoshi Ohashi (Morgan Sanley) for his helpful suggesions. 11

13 in: 7) brokerage of agency rades or 8) brokerage of principal rading and agency rades (denoed as S7 and S8, respecively). If a forecaser works for 9) research and informaion, 10) planning for invesmen managemen, or 11) else, we look a a column labeled business caegory. If he/she works a a domesic or foreign securiy company, hen he/she is caegorized as a sell-side professional (denoed as S1 and S2, respecively). Oherwise, for example, if he/she works a an invesmen rus, commercial bank, rus bank, in life insurance, posal life insurance, pension fund, or else, or he/she is an invesmen advisor, hen he/she is caegorized as a buy-side forecaser (B9, B10, and B11, respecively). Our daase includes 826 buy-side and 306 sell-side professionals. The average number of respondens each monh is buy-side and 52.0 sell-side professionals. The number of respondens does no change much over ime. The sandard deviaion of he number of forecass by buy-side professionals is 7.81, and ha by sell-side professionals is Figure 1 also confirms ha he fracions of buy-side and sell-side professionals over all respondens do no vary over ime. Each buy-side professional replied an average of 19.8 imes, and each sell-side professional replied an average of 21.5 imes hroughou he sampling period. There are 9 ypes of buy-side professionals (B1 B6 and B9 B11) and 4 ypes of sell-side professionals (S1 2 and S7 8). Throughou our sample periods, he average fracions of hese ypes in percenage are as follows: 18.6 percen for B1, 6.9 percen for B2, 4.6 percen for B3, 9.3 percen for B4, 10.0 percen for B5, 9.2 percen for B6, 7.3 percen for B9, 3.2 percen for B10, 2.4 percen for B11, 19.2 percen for S1, 2.4 percen for S2, 3.3 percen for S7, and 3.7 percen for S8. 12

14 Figure 1: Fracions of buy-side and sell-side professionals over all respondens 1 fracion of buy-side professionals fracion of sell-side professionals June 00 June 02 July 04 Aug 06 Sep 08 Feb 10 periods 3.2. Dispersion The expecaion heerogeneiy or dispersion among all professionals is measured in erms of he sandard deviaion of forecass across all individuals a each poin in ime, referred o all dispersion. The expecaion dispersion among buy-side (sell-side) professionals is represened by he sandard deviaion of forecass across all buy-side (sell-side) individuals a each poin in ime, denoed as buy side dispersion sell side ( dispersion ). Figure 2 presens he hisograms of he dispersion for he 1 monh-ahead forecass of all professionals. The mean is 51.3 (yen), and he sandard deviaion is 11.0, indicaing ha he professionals expecaions are, in fac, heerogeneous. 13

15 Figure 2: Hisogram of dispersion for 1 monh-ahead expecaions by all professionals Mean = 51.3 Sandard deviaion = Densiy Dispersion Figure 3: Hisogram of dispersion for 1 monh-ahead expecaions by buy-side (bar) and sell-side (line) forecasers Densiy Dispersion Buy-side: Mean=49.6, sd.= 11.3 Sell-side: Mean=54.3, sd.=

16 Figure 4: Dispersion of All (op), Buy-side (boom), and Sell-side (following page) professionals and TOPIX TOPIX Dispersion x Feb 02 June 05 Oc 08 Periods: from June 00 o Feb TOPIX Dispersion x Feb 02 June 05 Oc 08 Periods: from June 00 o Feb 10 15

17 TOPIX Dispersion x Feb 02 June 05 Oc 08 Periods: from June 00 o Feb 10 Figure 3 displays he hisograms of he dispersion for he 1 monh-ahead forecass of buy-side and sell-side forecasers. The hisogram for he buy-side professionals is marked wih bars, while ha for he sell-side professionals is represened wih a line. They indicae ha expecaions among buy-side and sell-side professionals are heerogeneous as well and ha he differences beween buy-side and sell-side professionals are significan. The mean of he dispersion is larger (54.3) for sell-side professionals han for buy-side professionals (49.6). The sandard deviaions of he dispersion amoun o 11.3 (12.7) for buy- (sell-) side professionals. These resuls indicae ha disagreemens among sell-side professionals end o be more diverse han among he buy-side professionals and ha he level of disagreemen flucuaes more among sell-side professionals han among buy-side professionals. This endency seems o be consisen wih he fac ha sell-side analyss have an incenive o differeniae heir research from oher sell-side analyss for heir business (Hong and Kubik, 2003 and Michaely and Womack, 2005). 16

18 Figure 4 presens he ime series for TOPIX and he dispersion for all, buy-side, and sell-side professionals. As in he survey daa of several papers, such as hose of Döpke and Frische (2006), Mankiw, Reis, and Wolfers (2003), Menkhoff, Rebizky, and Schröder (2009), and Reiz, Sadmann, and Taylor (2009), dispersions appear o be quie persisen in our daase as well, suggesing ha he auoregressive componens should be included in he esimaion model Facors The QUICK survey asks professionals o rae he exen o which hey believe he following facors having influences on sock prices over he nex 6 monhs: Facor 1: Business condiions; Facor 2: Ineres raes; Facor 3: Foreign exchange raes; Facor 4: Poliics and diplomacy; Facor 5: Inernal facors and marke psychology in sock markes; and Facor 6: Sock and bond markes abroad. Forecasers answer his quesion by numbering from 1 (srongly posiive) o 5 (srongly negaive). Thus, hese facors give us six variables, since he respondens provide an answer for all 6 facors each period. 13 In esablishing he facor variables used in our empirical analysis, we also uilize individuals idenificaion of he facor ha hey deem mos imporan each monh. In our 12 In addiion, recall ha equaion (2) indicaes ha buy-side and sell-side dispersions as well as he fracions of each professional over all respondens deermine he dispersion of all respondens. Figure 1 shows ha fracions of buy-side and sell-side professionals over all respondens are sable over ime, and Figure 4 suggess more volaile dispersion of buy-side and sell-side professionals han he flucuaions of he fracions. And hen, raher han he fracions of buy-side and sell-side professionals over all respondens, dispersions for buy- and sell-side professionals essenially explain he dispersion for all professionals. 13 Over our sample periods from June 2000 hrough February 2010, Facor 1 (Business condiions), Facor 2 (Ineres raes), Facor 3 (Foreign exchange raes), Facor 5 (Inernal facors and marke psychology), and Facor 6 (Sock and bond markes abroad) end o have posiive influences on fuure price expecaions presened by all of he professionals, i.e., he averages are less han 3 (2.61, 2.91, 2.97, 2.87, and 2.88, respecively). Poliics and diplomacy (Facor 4) appear o cause all of he professionals o expec a downward rend in fuure prices, as he average is greaer han 3 (3.23). 17

19 daase, he respondens change over ime, and he 5-poin scaling used may involve subjecive personal opinion. We ackle his problem by consrucing a new variable as follows. Firs, we assign he variables of he various facors 1, -1, or 0, if respondens answer 1 or 2, 4 or 5, or 3, respecively. Second, hese modified facors are muliplied by he binary dummy variable for he responses relaed o he mos imporan facor, which is aken as one if he facor is deemed he mos imporan and as zero oherwise. Thus, he new variable akes 0 if i is no deemed o be he mos imporan variable and i akes 1 or -1 if i is considered o be he mos imporan facor. We ake averages of he new variable for buy-side and sell-side professionals each monh, and consruc ime series of he averages of he new variables. Then, we have Facor( j) and buy side Facor( j) a each monh, where j represens sell side he facor from 1 o 6. For example, Facor( 1) is he average of Facor 1, Business buy side condiions, among buy-side professionals a ime. We furher decompose Facor( 1) buy side ino buy side, Facor (1) and Facor (1). buy side,+ Facor( j ) i is assigned o Facor( j) i, + if Facor( j ) i >0 and 0 is assigned o Facor( j) i, + oherwise. Similarly, Facor( j ) i is assigned o Facor( j) i, if Facor( j ) i <0 and 0 is assigned o Facor( j) i, oherwise. Thus, we consruc wo ime series Facor ( j) and i,+ Facor ( j) for facor j s variable ha will be used in our i, empirical analyses, where i denoes buy-side and sell-side professionals. Facor (1) buy side,+ means ha buy-side professionals believe on average a ha business condiions conribue o increasing he fuure sock prices, and Facor (1) refers ha hey believe on buy side, average a business condiions o conribue o decreasing he fuure sock prices. 18

20 Table 1: Facors influencing he fuure sock price Facor ( j) Facors: j i = All i = Buy-side i = Sell-side (0.2864) [53.7%] (0.2969) [55.0%] (0.2705) [50.5%] (0.0235) [3.3%] (0.0231) [2.9%] (0.0366) [4.3%] (0.0294) [4.1%] (0.0318) [3.6%] (0.0375) [5.3%] (0.0575) [9.9%] (0.0565) [9.7%] (0.0745) [10.4%] (0.041) [8.7%] (0.0454) [8.6%] (0.046) [9.1%] (0.0534) [14.5%] (0.0555) [14.5%] (0.0631) [14.4%] Noes: The numbers in he firs row are he uncondiional means, and he numbers in parenheses are he sandard deviaions. The percenages in he hird row refer o he percenages of professionals who indicaed ha his paricular facor is he mos imporan among all six facors. Noe ha some professionals did no give an answer o he quesions; hus, he sum of he percenages may no amoun o 100%. The facors are caegorized as follows: Facor 1: Business condiions; Facor 2: Ineres raes; Facor 3: Foreign exchange raes; Facor 4: Poliics and diplomacy; Facor 5: Inernal facors and marke psychology; and Facor 6: Sock and bond markes abroad. i Table 1 describes he summary saisics of facor variables Facor ( j) where j = 1, i, 6, and i = buy-side and sell-side professionals. The numbers in he firs row are he uncondiional means, and he numbers in parenheses are he sandard deviaions. The numbers in brackes refer o he percenages of professionals indicaing his paricular facor o be he mos imporan among all six facors. Over our sample period from June 2000 hrough February 2010, more han 50% of respondens placed greaer imporance on Business condiions (Facor 1) increasing fuure prices (he average values are abou

21 for all, buy-side, and sell-side professionals), whereas approximaely 14.5% of hem seleced he Sock and bond markes abroad (Facor 6) as he mos imporan facor o posiively influence he fuure sock prices (he average values range from o 0.006). Ineres raes (Facor 2) and Foreign exchange raes (Facor 3) are seleced as he mos imporan facor by approximaely 3-5% of respondens; however, his influence on fuure prices is regarded as negaive (usually abou o ). Poliics and diplomacy (Facor 4) and Inernal facors and marke psychology in sock markes (Facor 5) are seleced by 8-10% of respondens as having he mos imporan influence on he fuure prices (wih a posiive influence ranging from o 0.027) Hypohesis We explore he deerminans of he forecas dispersion by characerizing he behavior of buy-side and sell-side professionals, and hus, uncover an explanaion for he forecas heerogeneiy among all professionals Hypohesis 1 This hypohesis is used o es he asserion made by he hird srand of lieraure. Hypohesis 1: Expecaion heerogeneiy arises because differen ypes of professionals exis in he same marke; ha is o say, professionals uilize differen informaion in heir forecass and inerpre he same informaion on he curren sae of he economy differenly. If his asserion is correc, buy-side and sell-side professionals acquire differen informaion on macroeconomic and poliical facors so ha differen facors are used o deermine expecaions and dispersions of buy-side and sell-side professionals. Or, on he oher hand, a cerain macroeconomic or poliical facor may influence he dispersion of he buy-side professionals wihou affecing he sell-side dispersion, or vice versa, because hey inerpre he informaion in differen ways. We es his hypohesis using he following equaion: 20

22 ln i i, + i, ( dispersion ) = α + β ( Facor( j) ) + β ( Facor( j) + + ) ε (3) Recall ha Facor( j ) i represens he facors ha influence fuure sock price forecass, where i is buy-side or sell-side, and j =1,., 6 and is he index of he facors. We have Facor( j) i, + if Facor( j ) i >0, oherwise ha akes 0. Similarly, we have Facor( j) i, if Facor( j ) i <0, oherwise ha akes 0. This seup enables us o check he asymmery of influences of each facor on forecass. If he hypohesis is correc, we should obain he following wo resuls. Firs, we should observe ha he buy-side and sell-side professionals have differen facors wih significance ha deermine heir dispersion. Each professional has access o differen informaion, resuling in cerain informaion on hese facors influencing significanly he dispersion of one ype, bu no he oher. Alernaively, buy-side and sell-side professionals gaher he same informaion bu employ i in differen ways. As a resul, we should observe ha a leas one facor will be saisically significan for one ype of professional and no he oher Hypoheses 2 and 3 In he ess of he following Hypoheses 2 and 3, we firs examine he ineracive effec beween buy-side and sell-side professionals. We hen examine wheher he deerminans of 14 We validae Hypohesis 1, for example, in a following case: The idea of buy-side professionals on "Foreign buy side exchange raes," i.e., Facor() 3, significanly influences heir own dispersion, while ha of he sell side sell-side professionals, i.e., Facor() 3, does no for he sell-side dispersion. A he same ime, sell-side own idea on he oher facors deermines he sell-side dispersion. 21

23 our professionals forecas dispersion are consisen wih he asserion of he hird srand of lieraure as being esed in Hypohesis 1. Hypohesis 2: Buy-side professionals refer o he way in which sell-side professionals evaluae he marke. Buy-side professionals examine sell-side research and disill he informaion in order o uilize i in making heir invesmen decisions (e.g., Busse, Green, and Jegadeesh, forhcoming). This fac indicaes ha he forecass of buy-side professionals are, a leas in par, affeced by sell-side professionals views. If such is he case, he ideas of sell-side professionals are indirecly refleced in he forecass of buy-side professionals. To es his hypohesis, he following model is esimaed: ln buy side sell side, + sell side, ( dispersion ) = α + β ( Facor( j) ) + β ( Facor( j) ) + + ε (4) Hypohesis 3: Sell-side professionals can be rewarded for having marke views similar o hose of buy-side professionals, who are he sell-side professionals cusomers. According o Groysberg, Healy, and Chapman (2008) and many ohers, opporuniies o inerac wih heir cliens, i.e., wih buy-side professionals, enable sell-side professionals no only o improve heir research bu also o ingraiae hemselves wih heir cliens. Sell-side professionals hus aemp o undersand how buy-side professionals evaluae he marke, hereby mos likely influencing sell-side professionals forecass. In order o es his hypohesis, we esimae he following model: ln sell side buy side, + buy side, ( dispersion ) = α + β ( Facor( j) ) + β ( Facor( j) ) + + ε (5) In addiion o finding he ineracive effec, in he ess of Hypoheses 2 and 3, we expec o observe he following resuls ha are consisen wih he asserion of he hird srand of he lieraure. In conras o he ineracive feaure where one ype refers o he ideas 22

24 of he oher, buy-side or sell-side professionals should have a leas one facor where each ype does no refer o he idea of he oher bu heir own idea deermines heir own dispersion. The resul will be inerpreed as follows. On he one hand, professionals would share he same informaion abou a facor bu inerpre i in a differen way so ha one ype uilizes heir own informaion in heir forecass, while he oher does no. On he oher hand, as a resul of he ineracion, hey obain informaion from he oher ype abou a facor bu uilize heir own informaion, which hey each primarily had, in heir forecass. The laer implies ha professionals uilize differen informaion ha deermines or backs heir own forecass. 4. Tesing Hypoheses In his secion, he hree hypoheses discussed above are esed. In he process, wo economeric issues are addressed. Firs, based on he Akaike Informaion Crierion (AIC) and he Schwarz-Bayesian Informaion Crierion (SBIC), auocorrelaions of he dispersion are indicaed a a lag of wo in mos buy-side regression cases. Meanwhile, auocorrelaions are presen a a lag of one or wo in sell-side regression cases. As a consequence, we specify an AR(2) specificaion, esimae he regression models using OLS, and compue he -saisics uilizing he Newey-Wes sandard errors, which have been shown o be robus in he face of heeroskedasiciy and auocorrelaion (Newey and Wes 1987, 1994). Second, we focus on he one-monh ahead forecas in order o avoid he overlapping forecas problem, in spie of he fac ha he QUICK daase conains one-monh, hree-monh, and six-monh ahead forecass. Our facor variables sem from he respondens responses regarding he facors influencing he sock price over he nex 6 monhs, indicaing ha he facor variables may influence he dispersion wih cerain lags. Thus, we compued he AIC and SBIC and deermined he appropriae lengh of he lags o be 0 or 1 monh. Therefore, facor variables a a lag of 0 or 1 are considered in our esimaions. 23

25 We firs conduc a univariae regression by including each facor variable independenly in each regression, because our facor variables may possibly be correlaed. Were hey correlaed, he mulicollineariy problem should arise. The professionals' opinions abou macroeconomic, poliical, and psychological facors ha influence fuure sock prices may commove when cerain sysemaic shocks hi he economy. However, in Secion 5, we will demonsrae ha our resuls do no change significanly even when we add all six facor variables a once in each regression Tesing Hypohesis 1 Table 2 summarizes he resuls of esing Hypohesis 1. We es wheher buy-side dispersion can be explained by buy-side professionals own ideas (heir own facor variables) and we do he analogue for sell-side professionals. The posiive sign of he coefficien esimaes of Facor( j) i, + means an increase in he dispersion. Bu since Facor ( j) akes i, negaive values, dispersion will increase if he sign of he coefficien is negaive. For example, he coefficien esimae on Facor (3) is -1.93, meaning he dispersion among buy side, + 1 buy-side professionals decreases when he buy-side professionals consider foreign exchange raes as an imporan facor involved in increasing he fuure sock price. The coefficien esimae on Facor (6) is -0.93, meaning ha he buy-side professionals dispersion buy side, ends o widen when he buy-side professionals consider sock and bond markes abroad o be he mos imporan facor decreasing he fuure sock price When he explanaory variable is negaive and he parameer esimae has a negaive sign, muliplicaion of he wo erms resuls in a posiive number, and hus, he dispersion will increase. 24

26 Table 2: Tesing hypohesis 1 Facor 1 Facor 2 Facor 3 Facor 4 Facor Buy-side Sell-side ** (0.38) (-1.99) (-0.84) (-1.11) * (1.04) (-1.75) (-0.08) (0.03) (-0.53) (0.72) (0.90) (-0.77) (0.99) (-0.46) (0.03) (-0.64) (-0.84) (0.64) (0.60) (0.22) *** (-3.41) (0.25) (-1.42) (0.18) *** 0.24 (1.15) (0.85) (3.37) (0.39) *** (1.14) (-0.28) (3.09) (-0.43) ** (0.21) (-2.49) (-1.17) (0.53) (-0.65) (-0.78) (-0.49) (0.15) *** ** ** (-2.99) (-2.54) (-0.96) (-2.05) Facor *** (0.38) (-3.04) (-0.34) (-0.94) Noes: We es wheher buy-side dispersion can be explained by heir own ideas (heir own facor variables) and do he analogue for sell-side dispersion. The numbers wihou parenheses are he esimaes of he parameers on facor variables, and hose wih parenheses are -saisics compued by uilizing Newey-Wes correced sandard errors. *, **, and *** denoe significance a he 10%, 5% and 1% levels, respecively. The facors are caegorized as follows: Facor 1: Business condiions; Facor 2: Ineres raes; Facor 3: Foreign exchange raes; Facor 4: Poliics and diplomacy; Facor 5: Inernal facors and marke psychology; and Facor 6: Sock and bond markes abroad. 25

27 In esing Hypohesis 1, we find ha buy-side and sell-side professionals uilize differen informaion abou he various facors involved in order o make heir predicions, since he various facors are significan in generaing he dispersions among buy-side and sell-side professionals. This resul also implies ha even if he same facor s informaion is aken ino consideraion in he forecasing process, cerain facors, which may no significanly conribue o he sell-side dispersion, deermine he buy-side dispersion, and vice versa. In oher words, he wo groups inerpre he same informaion in differen ways. Thus, we conclude ha he dispersion arises because he wo differen ypes of professionals co-exis wihin he same marke. Table 2 reveals ha Business condiions, Foreign exchange raes, Inernal facors and marke psychology in sock markes, and Sock and bond markes abroad o be significan facors in deermining he buy-side dispersion. We confirm ha he influence of he facors on he buy-side dispersion is asymmeric, based on he following hree observaions. Firs, when he buy-side professionals consider a ha Business condiions and Inernal facors and marke psychology in sock markes are facors ha lower he fuure sock prices, he dispersion increases, meaning ha he disagreemen arises when he buy-side professionals expec a recession. The resul is asymmeric because he buy-side dispersion is no influenced when he buy-side professionals consider hese facors o resul in higher fuure prices. Second, when he buy-side professionals regard Sock and bond markes abroad as a conribuing facor o fuure price increases, he dispersion ends o decrease. However, expecaions end o diverge when he buy-side professionals ake his facor o have a negaive impac on he price. Third, when he buy-side professionals believe foreign exchange raes o be a key facor in increasing he fuure sock prices, ha is o say, 26

28 hey expec a depreciaion of he yen, he expecaions end o converge. 16 Bu he expecaion dispersion is no influenced when a yen appreciaion is expeced. The las wo columns in Table 2 demonsrae ha he facors ha explain he sell-side dispersion differ significanly from hose on he buy-side professionals in he following wo ways. Firs, in Table 2, we observe a significan impac from he buy-side idea on he fundamenal facors, i.e., Business condiions and Foreign exchange raes, influencing he buy-side dispersion. Noneheless, he sell side s ideas on Business condiions and Foreign exchange raes do no influence heir dispersion. This finding does no imply ha hese fundamenal facors are irrelevan o heir expecaions; raher, i indicaes ha he sell-side professionals may change heir expecaions in a similar manner, leaving he difference in heir expecaions nearly unchanged. Second, he sell side s ideas abou Poliics and diplomacy and Sock and bond markes abroad are significanly and posiively relaed o he sell-side dispersion, whereas he buy-side dispersion is no relaed o heir ideas regarding Poliics and diplomacy. These resuls imply ha buy-side and sell-side professionals may have differen informaion or differen informaion processing echnology for making heir forecass ha are consisen wih he resuls of he hird srand of lieraure on he deerminans of he expecaion heerogeneiy. Opinions of sell-side professionals on Poliics and diplomacy and Sock and bond markes abroad diverge he sell-side forecass. Thus, differen forecass are made among he sell-side professionals, when he informaion on Poliics and diplomacy or Sock and bond markes abroad is used in forecasing. As described by Cheng, Liu, and Qian (2006), his finding implies ha hose on he sell-side professionals aemp o differeniae hemselves from oher sell-side professionals and hopefully esablish a repuaion on he marke by 16 As Japan has regisered rade surplus for he las consecuive 30 years, depreciaing yen is hough as a posiive facor for sock prices and vice versa (Japan Cener for Economic Research, 2011). 27

29 making forecass ha are disinc from hose of ohers. Table 3: Tesing hypoheses 2 and 3 Buy-side Sell-side (-0.13) (-0.78) (-0.73) (-0.99) Facor (0.55) (-1.20) (-0.07) (-0.89) (0.61) (-1.33) (-0.06) (-1.23) Facor ** (0.07) (0.77) (2.15) (-1.21) (-1.12) (1.28) (-1.45) (-0.14) Facor (-1.30) (0.39) (-1.62) (0.66) 0.68 ** * 0.68 (1.98) (-0.03) (1.94) (0.67) Facor * (1.93) (0.27) (0.60) (0.05) *** (-0.51) (-0.98) (0.18) (-2.60) Facor * (-0.97) (1.25) (-1.68) (1.51) ** ** ** (-1.17) (-2.43) (-2.38) (-2.13) Facor ** -1 (-0.52) (-0.90) (0.19) (-2.54) Noes: We es wheher buy-side dispersion can be explained by he facor variables of sell-side professionals. The numbers wihou parenheses are he esimaes of he parameers on facor variables, and hose wih parenheses are -saisics compued by uilizing Newey-Wes correced sandard errors. *, **, and *** denoe significance a he 10%, 5% and 1% levels, respecively. The facors are caegorized as follows: Facor 1: Business condiions; Facor 2: Ineres raes; Facor 3: Foreign exchange raes; Facor 4: Poliics and diplomacy; Facor 5: Inernal facors and marke psychology; and Facor 6: Sock and bond markes abroad. 28

30 4.2. Tesing Hypoheses 2 and 3 The resuls in he columns of Table 3, denoed as Buy-side dispersion, describe he es resuls for Hypohesis 2. Firs, we reveal ha he buy-side professionals incorporae he ideas of he sell-side professionals abou cerain facors, such as Poliics and diplomacy and Sock and bond markes abroad, when he buy-side professionals have an opinion similar o is own. In such cases, he buy-side dispersion ends o increase. Second, we demonsrae ha for cerain facors, such as he Foreign exchange raes, he buy-side professionals only ake is own ideas ino accoun when forecasing, while for he Poliics and diplomacy facor, he buy-side professionals refer o sell side s ideas regardless of heir own ideas. The deails on he wo resuls are explained in he following by comparing wih he resuls in Table 2 and 3. Firs, he sell side s ideas regarding he facors of Poliics and diplomacy and Sock and bond markes abroad widen he buy-side dispersion, while hose facors also deermine he sell-side dispersion presened in Table 3. When he sell-side professionals consider Sock and bond markes abroad (and Poliics and diplomacy ) o be an imporan facor pushing in increasing or decreasing he fuure price, he sell-side dispersion increases, as shown in Table 2, and he buy-side dispersion also increases, as presened in Table 3. The resuls imply ha he buy-side professionals refer o and uilize he sell side s ideas in making heir predicions, hereby influencing heir dispersion, when he buy-side professionals have ideas similar o hose of he sell-side professionals. Second, Table 2 indicaes ha buy-side inerpreaions of Foreign exchange raes affec is own dispersion, while he resuls given in Table 3 indicae ha he sell side s ideas regarding foreign exchange raes do no influence on he buy-side dispersion. Thus, he buy side s inerpreaions of his fundamenal facor are exclusively relaed o is own dispersion. The las wo columns in Table 3, denoed as Sell-side dispersion, summarize he resuls for esing Hypohesis 3. We find ha buy-side professionals have a significan influence on he forecas decisions of sell-side professionals. This implies ha he sell-side 29