JCER DISCUSSION PAPER

Transcription

1 JCER DISCUSSION PAPER No.135 Sraegy swchng n he Japanese sock marke Ryuch Yamamoo and Hdeak Hraa February 2012 公 益 社 団 法 人 日 本 経 済 研 究 センター Japan Cener for Economc Research

2 Sraegy swchng n he Japanese sock marke Ryuch Yamamoo a and Hdeak Hraa b a Deparmen of Inernaonal Busness, Naonal Chengch Unversy, Tape 116, Tawan b Japan Cener for Economc Research, Tokyo, Japan and Deparmen of Busness Admnsraon, Hose Unversy, Tokyo, Japan February, 2012 Absrac Ths paper dscusses he expecaon formaon process of Japanese sock marke professonals and how her expecaons are relaed o larger flucuaons of he TOPIX prce han hose of economc fundamenals. By ulzng a monhly forecas survey daase on he TOPIX dsrbued by QUICK Corporaon, we sor forecasers no buy-sde and sell-sde professonals. We frs demonsrae ha he buy-sde and sell-sde professonals use boh fundamenal and echncal radng sraeges hroughou her expecaon formaon processes and ha hey swch beween fundamenal and echncal radng sraeges over me. We hen emprcally show ha sraegy swchng s key n undersandng he perssen devaon of he TOPIX from he fundamenals. JEL Classfcaon: G17, G12 Keywords: Sraegy swchng, agen-based modelng, survey daa, expecaons, Japanese sock marke E-mal address: [email protected] (Yamamoo) and [email protected] (Hraa). 1

3 1. Inroducon Snce he fnancal marke lberalzaon of he 1990s, we have observed remarkable ncrease n radng volume by nsuonal nvesors n he Japanese sock marke, who have been seekng shor-erm profs. Ceran prevous emprcal sudes show ha he shor-erm radng, smulaneously conduced by nsuonal nvesors, s prmarly responsble for desablzng he sock markes ha ofen nvolves large devaons of he sock prce from he fundamenal value. 1 Praconers ry o deermne he sources of he unsable sock prce movemens for beer rsk managemen n fnancal markes. The lberalzaon of global fnancal markes, whch ncreases he number of marke parcpans, ndcaes ha nvesors expecaons are more lkely o be ncorporaed no he asse prces han n he pre-lberalzaon perods. Therefore, beer explanaons of he expecaon formaon process of nvesors and how nvesors expecaons are relaed o asse prce movemens can faclae beer undersandng of he sources of rsk n fnancal markes. Ths paper provdes emprcal evdence for undersandng boh he deermnans of expecaons and he causes of sock prce movemens by usng a monhly forecas survey daase on he TOPIX dsrbued by QUICK Corporaon, a Japanese fnancal nformaon vendor n he Nkke Group. We frs demonsrae ha he professonals nvolved n he Japanese sock marke ulze boh fundamenal and echncal radng sraeges n her expecaon formaon processes and ha hey swch beween fundamenal and echncal radng sraeges over me. We hen emprcally show ha he sraegy swchng s key n undersandng he perssen devaons of he TOPIX prce from he fundamenal value. Our conclusons are conssen wh wha several agen-based models predc and are presened as follows. Recen agen-based heorecal models successfully explan he causes of sock marke nsably, such as larger prce flucuaons han hose of he fundamenal prce, ha are sll no suffcenly explaned wh radonal asseprcng models usng effcen marke and raonal expecaon hypoheses. 2 Many agen-based heorecal models assume ha agens form her expecaons by combnng several nvesmen 1 Several recen sudes, such as Chen, Jegadeesh, and Wermers (2000), Nofsnger and Sas (1999), Sas (2004), and Wermers (1999) show a srong posve correlaon beween nsuonal ownershp and sock reurns. Shller (1981) measures he fundamenal prce and demonsraes ha he sock prce ofen devaes from he fundamenal prce and ha s varaons are much greaer han hose of he fundamenal prce. 2 Agen-based models also replcae volaly cluserng, fa als of reurn dsrbuon, nonzero volume, auocorrelaons of volume, and posve, conemporary cross-correlaons beween he volume and he squared reurns. See, for example, LeBaron, Arhur, and Palmer (1999). Hommes (2006) and LeBaron (2006) survey he leraure on agen-based compuaonal fnance and explan s usefulness n generang fnancal marke phenomena. 2

4 sraeges. Sock marke nsably s explaned n an envronmen n whch agens swch he level of dependence on he sraeges over me. Sandard agen-based models, popularly exemplfed by a model creaed by Brock and Hommes (1998), assume ha agens combne fundamenal and echncal radng sraeges n her forecasng. Invesors usng he fundamenal sraegy expec ha fuure prces wll always hover around he fundamenal or nrnsc value of he asse, whch s ofen measured by a frm s earnngs or dvdends. The echncal radng sraegy s developed usng pas prce nformaon, and suggess ha expecaons are posvely correlaed o recen prce movemens f agens are momenum raders and ha hey are conrarans when he relaon s negave. The models demonsrae ha when mos agens selec he echncal sraegy, he sock marke ends o be unsable, whch explans he phenomena of he larger devaons from he fundamenal prce such as bubbles and crashes. Conversely, when mos agens adop he fundamenal sraegy, he marke wll be sablzed, movng he marke prce back o he fundamenal prce and leadng he marke o be nformaonally effcen. Sandard agen-based heorecal models demonsrae ha nvesors nerchangeably ulze he wo sraeges over me, and hs sraegy swchng s a major facor n explanng unsable prce movemens of fnancal asses. 3 Our paper provdes emprcal evdence on sraegy swchng n Japanese sock markes, and we furher demonsrae ha he sraegy swchng explans perssen prce devaons from economc fundamenals well. We explore hem by sorng forecasers no buy-sde and sell-sde professonals. 4 Buysde professonals are hose who work for nvesmen nsuons, such as muual funds, penson funds, and nsurance frms, whch purchase secures on her own accoun. Sell-sde professonals work for companes ha sell nvesmen servces o asse managemen frms, or buy-sde professonals, and provde research ncludng her recommendaons o her clens. 5 We emprcally denfy he sraegy swchng of buy-sde and sell-sde professonals, and we demonsrae ha her sraegy swchng explans perssen prce devaons from economc 3 Krman (1991), Lux and Marches (1999; 2000), and Gaunersdorfer, Hommes, and Wagner (2008) also explan he sraegc neracons and volaly. In addon, Charella, Ior, and Perelló (2009) and Farmer and Josh (2002) show ha rend-followng sraeges amplfy nose and cause sylzed phenomena n fnancal markes such as excess and clusered volaly. 4 Pfajfar and Sanoro (2010) sor forecasers expecaons n each perod n ascendng order wh respec o value, and hey consruc me seres of percenles from he emprcal dsrbuon. They adop he approach of nvesgang he effec of sraegy swchng on nflaon expecaons. 5 For more nformaon on he dfferen acves n whch buy-sde and sell-sde professonals engage, see Groysberg, Healy, and Chapman (2008) and Busse, Green, and Jegadeesh (forhcomng). 3

5 fundamenals. Prevous sudes on expecaon formaons focus on measurng he characerscs of he cenral endency of he forecass. 6 However, he dsrbuon of he forecass may no be symmercal, and he dsrbuon may vary over me. Thus, f we use he measure of he cenral endency of he forecas seres, we canno characerze he expecaon formaon of professonals forecasng dfferenly from he average, and wll no be able o denfy he ypes of professonals who are acually desablzng he marke. Mos sgnfcanly, hs paper conans he followng fve conrbuons. Frs, hs paper valdaes he sraegy swchng and demonsraes he sgnfcan relaon beween he sraegy swchng and sock marke nsably, whch s an mporan conrbuon of several agen-based models o he leraure. Some laboraory expermens wh human subjecs suppor hs mporan observaon n heorecal agen-based sock markes. 7 In addon, some survey sudes n fnancal markes provde evdence of sraegy swchng among he marke professonals. 8 Alhough we have seen heorecal and laboraory work, drec evdence s sll requred o emprcally suppor sraegy swchng and s conrbuon n generang he emprcal feaures of sock markes. Second, we emprcally denfy he ypes of professonals who acually swch he sraeges and desablze he marke. Prevous research on agen-based models concludes such nvesors behavor o be key n explanng several emprcal feaures n sock markes. Noneheless, hose papers denfy neher he ype of fnancal nsuons o whch hose agens specfcally belong nor her respecve busness caegores. Thrd, we emprcally analyze he sraegy swchng by boh buy-sde and sell-sde professonals. Several papers, such as Clemen (1999) and Hong and Kubk (2003), nvesgae he behavor of sell-sde nvesors from a cross-seconal vewpon, bu hey exclusvely focus on he sell-sde professonals. Accordngly o Groysberg, Healy, and Chapman (2008), hs s due o a lack of daa on buy-sde professonals. Whn he relavely lmed amoun of research conduced on buy-sde professonals, Cowen, Groysberg, and Healy (2006) and Groysberg, 6 For example, see Branch (2004), Brown and Clff (2004; 2005), Lux (2009; 2010), and Verma, Baklac, and Soydemr (2008). 7 See, for example, Hommes, Sonnemans, Tunsra, and van de Velden (2008) and Heemejer, Hommes, Sonnemans, and Tunsra (2009). 8 In he leraure on foregn exchange markes, Frankel and Froo (1990), Weserhoff and Rez (2003), and Gll and Wnker (2003) emprcally show sraegy swchng, whle Boswjk, Hommes, and Manzan (2007) nvesgae n he US sock marke. In he leraure on nflaon expecaons, Branch (2004) and Pfajfar and Sanoro (2010) provde emprcal evdence ha agens swch predcon regmes usng a survey on nflaon expecaons. 4

6 Healy, and Chapman (2008) examne he forecass made by boh buy-sde and sell-sde professonals bu do no characerze he sraegy swchng by buy-sde and sell-sde professonals. In addon, by analyzng he expecaon formaons by ypes, we can characerze he forecas behavor of professonals expecng dfferen from he cross-seconal average of he forecass. Fourh, we valdae he sraegy swchng n he Japanese sock marke a a monhly frequency. Boswjk, Hommes, and Manzan (2007) fnd sraegy-swchng behavor a a yearly frequency. Bu sll remans unknown a wha frequency sock nvesors acually change her sraeges. Ffh, we demonsrae ha he professonals n he Japanese sock marke have sysemac predcon bases and anchorng n some observable prors, conradcng he predcon of he effcen marke hypohess. Our resuls ndcae ha professonal forecasers combne echncal and fundamenal sraeges, meanng ha hey refer o pas prce nformaon n predcng fuure prces. The effcen marke hypohess suggess ha a marke s nformaonally effcen when he marke prce, or curren prce, already reflecs all known nformaon a any pon n me. The belefs of all nvesors regardng fuure prces are fully ncorporaed no he curren prce. Thus, he marke prce s an unbased esmae of he rue asse value n he sense ha pas prce nformaon canno be furher used o predc fuure prces. Whle Shller (1999) argues ha pas prce nformaon helps o explan curren prces n sock markes, several sudes ha examne hs hypohess by usng survey daa for professonal forecasers have ndcaed sysemac predcon bases. 9 Our emprcal resuls are conssen wh he fndngs of laboraory sudes conduced by Kahneman and Tversky (1973). Thus, our resuls help o mprove he robusness of he fndngs of hese sudes by usng survey daa for Japanese sock markes. Boswjk, Hommes, and Manzan (2007) provde evdence of sraegy swchng n sock markes. They esmae Brock and Hommes s (1998) ype of agen-based model n whch agens 9 For example, Nordhaus (1987) fnds a sgnfcan posve auocorrelaon of forecas revsons on GDP growh. When new nformaon arrves, forecasers do no ncorporae no her new expecaons mmedaely bu raher gradually adjus her vew n accordance wh he new nformaon. Campbell and Sharpe (2009) also nvesgae Money Marke Servces (MMS) consensus forecass and fnd ha expecaons are sysemacally based and anchored on recen pas values. A survey sudy of fnancal marke professonals and unversy sudens conduced by Kausa, Alho, and Puonen (2008) provdes evdence ha professonals and unversy sudens anchor her long-erm sock reurn expecaons o an nal value. In survey sudes on foregn exchange markes, for example, Frankel and Froo (1990), Lu and Mole (1998), and Menkoff and Taylor (2007), professonals ofen combne echncal radng sraeges wh he fundamenal sraegy n her forecasng. 5

7 swch her sraeges beween fundamenal and rend-followng regmes based on recen pas performance. They use he yearly S&P 500 and he correspondng earnng daa from and show ha rend-followng behavor explans he perssence of he devaon of sock prces from her fundamenal value, whch s esmaed based on he Gordon growh model usng earnngs daa, whle he fundamenal sraegy ends o rever he prces back o her hsorcal mean. Our paper dffers from ha of Boswjk, Hommes, and Manzan (2007) as follows. Frs, we characerze expecaon formaons of he buy-sde and sell-sde professonals. Thus, we demonsrae he mechansms of he sraegy swchng by dfferen ypes of professonals. Second, Boswjk, Hommes, and Manzan (2007) assume an agen-based model n esmang sraegy swchng such ha he marke s n equlbrum, on average. As we see n he followng secon, we follow he approach of Boswjk, Hommes, and Manzan (2007) o derve a fundamenal prce and consruc a fundamenal sraegy. However, our esmaon equaon s no an equlbrum prcng equaon; raher, uses forecas survey daa for sock marke professonals o nvesgae sraegy swchng. Thus, compared o Boswjk, Hommes, and Manzan (2007), we mpose fewer assumpons n valdang sraegy swchng. The res of he paper s srucured as follows: Secon 2 nroduces our daase of professonals forecass on he TOPIX and dsaggregaes he forecass no hose of buy-sde and sell-sde professonals. Secon 3 presens our emprcal models. Secon 4 provdes emprcal evdence on sraegy swchng, and Secon 5 dscusses he relaon beween sraegy swchng and prce flucuaons n he Japanese sock marke. The fnal secon presens a concluson o hs paper. 2. Daa We ulze a monhly panel daase gahered n surveys conduced by QUICK Corporaon, whch covers a perod of 117 monhs (from June 2000 hrough February 2010) and ncludes he onemonh-ahead expecaons for he TOPIX, provded by a oal of 1,132 professonals. The average number of respondens each monh s 182.0, and each forecaser repled an average of 20.5 mes. The survey s usually conduced a he begnnng of each monh over he course of hree consecuve days, wh he las of hese days akng place on he frs Thursday of he monh and he survey repor released on he followng Monday. The publshed repor solely ncludes 6

8 summarzed survey resuls, such as he mean, sandard devaon, medan, mnmum and maxmum of he forecass, and so forh. Alhough no all of he professonals repled o he survey for he full me perod of he sudy, our daase conans he survey resuls of each responden as well as nformaon such as he ndvdual code and company code of each responden, enablng us o rack he forecas record of ndvduals over me Buy-sde and sell-sde professonals We caegorze he respondens no buy- and sell-sde professonals, usng he nformaon for each responden presened n wo columns of he daase, whch are labeled assgned work and busness caegory. 10 Wh respec o he assgned work column, a responden s caegorzed as a buy-sde professonal f he or she s n charge of managng (1) hs or her company s own funds, (2) penson funds, (3) funds placed n rus (excludng penson purposes), (4) funds placed n rus (ncludng penson purposes), (5) nvesmen rus, or (6) propreary radng. (These subcaegores are denoed B1, B2, B3, B4, B5, and B6, respecvely). A responden s defned as a sell-sde professonal f he or she s nvolved n (7) brokerage of agency rades or (8) brokerage of prncpal radng and agency rades (denoed as S7 and S8, respecvely). If a forecaser works for (9) research and nformaon, (10) plannng for nvesmen managemen, or (11) oher, we look a a column labeled busness caegory. If he professonal works a a domesc secury company or foregn secury company, hen he or she s caegorzed as a sell-sde professonal (denoed as S1 or S2, respecvely). Oherwse, for example, f he or she works a an nvesmen rus, commercal bank, rus bank, n lfe nsurance, posal lfe nsurance, penson fund, or oher, or f he professonal s an nvesmen advsor, hen he or she s caegorzed as a buy-sde forecaser (B9, B10, and B11, respecvely). Our daase ncludes 826 buy-sde and 306 sell-sde professonals. The average number of respondens each monh s buy-sde and 52.0 sell-sde professonals. Each buy-sde professonal repled an average of 19.8 mes, and each sell-sde professonal repled an average of 21.5 mes hroughou he samplng perod. There are 9 ypes of buy-sde professonals (B1 B6 and B9 B11) and 4 ypes of sell-sde professonals (S1 2 and S7 8). Throughou our sample perods, he average fracons of hese ypes n percenage are as follows: 18.6 percen for B1, Ths caegorzaon s prmarly based on he prevous papers, such as ha of Groysberg, Healy, and Chapman (2008). In addon, we asked varous Japanese marke professonals abou our caegorzaons no buy-sde and sellsde professonals. In parcular, we hank Hdeosh Ohash (Morgan Sanley) for hs helpful suggesons. 7

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 S Forecas seres We ulze one-monh ahead forecas seres from QUICK Corporaon and denoe ha F 1 + s he average one-monh-ahead forecas made by ype a where = buy-sde professonals or sellsde professonals. We defne P as a monhly sock prce and he sock prce precedng he predcon dae. 11 As he survey s released a he begnnng of each monh, we assume ha P s he prce nformaon avalable before he release of he survey, meanng he prce nformaon F ha s avalable a he end of he precedng monh. Thus, ln + 1 represens uncondonal and P expeced percenage prce changes from he mos recen sock prce. 12 Fgure 1 plos he dfference of he cross-seconal averages of one-monh-ahead forecass from TOPIX. Fgure 1 ndcaes ha professonals usually have upward bases n her predcons, and he bases are F perssenly observed n our sample, suggesng ha he forecas varable a me, ln + 1, s P auo-correlaed; hus, he esmaon model should nclude s auoregressve componens. Alhough ceran prevous sudes on expecaon formaons n sock markes analyze he behavor of he cenral endency of he forecass, hs paper sors forecass n each perod no buy-sde and sell-sde professonals and nvesgaes he sraegy swchng n each ype. 13 If he 11 We use hs ndex from Daasream. 12 We avod usng he begnnng-of-monh prce for hs. The survey s usually conduced for hree days n he frs week of each monh, endng wh he frs Thursday, bu he survey perod shfs back and forh for a few days f any of he frs hree days overlaps a Japanese holday. Thus, f we use a ceran prce a he begnnng of he monh, he forecass F + 1 could be made before forecasers oban he nformaon on he sock prce. Ths conradcs he defnon of P, whch uses he mos recen prce before he predcon dae. We assume ha P s he pas prce nformaon ha all professonals observe and refer o n makng her forecass. 13 Snce professonals somemes move from buy-sde busness o sell-sde busness, or vce-versa, ceran professonals may be caegorzed as buy-sde professonals n some perods and as sell-sde professonals n oher perods, or hey may move from one ype o anoher whn he buy-sde professonal sde. However, as shown n emprcal sudes conduced by Curn (2005) and Pfajfar and Sanoro (2008), agens n he same caegory behave smlarly, leavng he nrnsc characerscs of he caegory unchanged. As Pfajfar and Sanoro (2010) explan, analyses ha nclude me seres of ypes are n lne wh he concepual srucure of overlappng generaon models. 8

10 June 00 June 02 July 04 Aug 06 Sep 08 Feb 10 perods Fgure 1: Dfferences of he forecass from TOPIX Sd. of Forecass/3 Skewness Kuross June 00 June 02 July 04 Aug 06 Sep 08 Feb 10 perods Fgure 2: Cross-seconal sandard devaon, skewness, and kuross of forecass 9

11 forecass n each perod are asymmercally dsrbued and he dsrbuon vares over me, analyses usng only he cenral endency canno characerze professonals expecaons forecasng as dfferen from he average. Fgure 2 confrms asymmercal and me-varyng dsrbuon of he forecass. The sar shows he cross-seconal sandard devaon of he forecass, measurng he expecaon heerogeney. We observe ha he expecaons are heerogeneous and vary over me. The hck lne and dos n he fgure show he skewness and kuross of he forecass, respecvely. The skewness and kuross are also me-varyng and do no ypcally exhb normal dsrbuon. The dsrbuon almos always has faer als han a sandard normal dsrbuon, ndcang ha ceran ypes of professonals end o have more opmsc or conservave vews on he fuure han he ohers. The skewness s usually no zero, ndcang asymmery n he forecas dsrbuon. Forecass are skewed o he lef when he skewness s negave, meanng ha ceran professonals are more conservave n forecas. Posve skewness ndcaes a small poron of professonals predcng more opmsc vews han ohers. The frs momen and he hgher momens of he forecas dsrbuon clearly confrm he asymmercal and me-varyng feaures of he forecas dsrbuon. To furher undersand he characerscs of he forecas dsrbuon, Fgure 3 plos he dfferences of buy-sde and sell-sde forecass from cross-seconal mean of he forecass. Three pons should be emphaszed here. Frs, he forecass of buy-sde professonals are ofen lower han he mean, whle he forecass of sell-sde professonals are ofen hgher han he mean. Second, he sell-sde professonals forecass are more volale han hose of buy-sde professonals. The frs and second pons are conssen wh general observaons of he forecas behavor of sell-sde and buy-sde professonals. Sell-sde professonals usually make opmsc forecass o sell her nvesmen servces, whle buy-sde professonals are generally consdered relavely conservave n her forecass. In addon, as descrbed by Cheng, Lu, and Qan (2006), sell-sde professonals have an ncenve o dfferenae her nvesmen servces from hose of oher sell-sde professonals, hopng o esablsh a repuaon on he marke by makng unque forecass. Ths ncenve generaes forecass ha are more volale han hose of buy-sde professonals. Thrd, he dfferences beween buy-sde and sell-sde forecass explan he dsrbuon of he enre forecass n Fgure 2. The sandard devaons of he enre forecass n Fgure 2 end o be greaer, as sell-sde professonals make hgher forecass and buy-sde professonals make lower 10

12 forecass han he mean. The skewness and kuross of he enre forecass n Fgure 2 vary sgnfcanly when sell-sde professonals change her forecass n greaer magnude. To summarze, Fgure 3 confrms ha he forecass by buy-sde and sell-sde professonals are dfferen from he average, and her forecass characerze he non-normal dsrbuon of he forecass n our sample. Ths suggess ha he nvesgaon of he forecass by buy-sde and sellsde professonals may, n some way, explan he forecasng behavor ha s no characerzed n an analyss usng he averaged forecas seres Buy-sde forecass - Mean forecass Sell-sde forecass - Mean forecass -30 June 00 June 02 July 04 Aug 06 Sep 08 Feb 10 perods Fgure 3: Dfferences beween he mean forecass and he buy-sde professonals forecass and sell-sde professonals forecass 11

13 3. Emprcal model We esmae he followng model for each ype o valdae he sraegy swchng. P ( 1 n ) β ln + n β ln C ε N F n F n ln α α n + Tc F Tc Tc + P = + n P 0 ln, P, (1) = 1 The lef-hand sde s he forecased varable. The frs and second erms n he rgh-hand sde are a consan erm and he lagged observaons wh order N, respecvely. We add auoregressve componens because he forecass are lkely o have perssenly upward bases, as observed n Fgure 1. We focus on he one-monh-ahead forecas o avod he overlappng forecas problem, n spe of he fac ha he QUICK daase conans one-monh-, hree-monh-, and sx-monhahead forecass. The hrd erm on he rgh-hand sde represens he fundamenal sraegy, whle P he fourh erm on he rgh-hand sde represens he echncal sraegy. ln s a fundamenal P ndcaor measurng he devaon of he precedng prce from he fundamenal or nrnsc value P, whle ln C s a echncal ndcaor measurng he recen prce rend, boh of whch wll be defned n more deal n he followng subsecons. β F and β Tc are coeffcens of he fundamenal and echncal radng sraeges, respecvely, for ype. When β F s posve, forecass based on he fundamenal sraegy are made around he fundamenal value. For example, f professonals use he fundamenal sraegy and he mos recen prce s below he fundamenal prce, hey expec ha he fuure prce wll move back oward he fundamenal prce, so hey predc upward prce movemen, and vce versa. When β Tc s posve, nvesors exrapolae he fuure pah of he sock prce n accordance wh he pas rend. They are conrarans when β Tc s negave, predcng a urnng pon n he prce rend. We assume ha professonals ulze boh fundamenal and echncal radng sraeges o reflec nvesors realsc behavor, as found n some sudes on surveys of fnancal marke parcpans, such as Lu and Mole (1998) and Menkoff and Taylor (2007). ( ) 1 and n Tc, n, are he fracons of professonals n ype, who ulzes he Tc fundamenal and echncal radng sraeges n forecasng, respecvely, rangng from 0 o 1. The sraegy swchng suggess ha hs varable n, changes over me. In he followng subsecons, we defne he deals regardng (1) he fundamenal prce Tc P ; (2) echncal ndcaor 12

14 ln ; (3) he fracons of he fundamenal and echncal radng sraeges ( ) C 1 and n Tc, n, ; and Tc (4) order N n he auoregressve componens, n order Fundamenal prce P We defne a fundamenal prce by closely followng he approach of Boswjk, Hommes, and Manzan (2007), whch s he presen value model wh raonal expecaons of fuure real dvdends dscouned by a consan real dscouned rae, whch s he so-called sac Gordon growh model (Gordon, 1962). The marke has wo radable asses: a rsky sock and a rsk-free bond. The rsk-free bond pays a consan neres rae of r f. The rsky asse s n zero ne supply and pays an unceran cash flow of Y n each perod. We defne P as he prce of he rsky asse a. Agens selec a predcon rule from he fundamenal and echncal radng sraeges. The expecaon of sraegy h a me s denoed as E h, where h = F (fundamenal sraegy) or Tc (echncal radng sraegy). Assumng a consan absolue rsk averson (CARA) uly and a Gaussan dsrbuon for cash flow and sock prces, agens selecng predcor h se her demand a me accordng o: S h, 2 ˆ h, E = h, ( P Y ˆ h, γσ ) (1 + r f ) P σ refers o he condonal varance esmae of predcon rule h a, and γ s a consan absolue rsk averson coeffcen. We assume ha all agens have homogeneous expecaons on 2 2 he condonal varance; hus, ˆ σ = σ. Denong he fracon of agens usng predcor h a me as h, ˆ n h, and assumng a zero ne supply of he rsky asse, he marke clearng condon s gven (2) by: H h= 1 n E ( P + Y ) (1 + r ) P h, f h, = 2 γσ The equlbrum prce s gven by: 1 P = 1+ r H f h= 1 n h, Eh, ( P Y+ 1) 0 (3) (4) As n he work of Boswjk, Hommes, and Manzan (2007), cash flow s assumed o be nonsaonary wh a consan growh rae as follows: 13

15 ln Y = + lny + ν 1 2 μ, ν.. d. N ( 0 σ ) + 1 ~, ν (5) Boswjk, Hommes, and Manzan (2007) show ha hs mples: Y Y μ + ν + 1 μ + (1/ 2) σν ν + 1 (1/ 2) σ = e = e e ν = 1 + (6) μ (1/2) where = + σν g e 1 and ε 2 ( g) ε ν 1 (1/ 2) σ ν + 1 = e +. Ths mples ha E ( ε ) 1 and ( ε ) = e σ ν V 1 +1 = Assumng ha all predcon rules have correc belefs on he cash flow, we have: [ Y + 1] = E [ Y + 1] = ( 1 + g) Y E [ + 1] = ( 1 g) Y E h, ε + (7) When all agens have raonal expecaons, he equlbrum prcng equaon (4) can be smplfed as: 1 P = E ( P Y+1) (8) 1+ r f In he case of a consan growh rae n cash flow of g, equaon (8) s expressed n erms of he raonal expecaons fundamenal prce 1 = rf P + g Y for g P as: r f > g (9) We refer o P as he fundamenal prce. We measure he devaon of he prce from he fundamenal prce as: P 1+ g Y ln = ln (10) P rf g P In our emprcal analyses, we ulze a monhly dvdend seres of TOPIX, whch s dsrbued by he Tokyo Sock Exchange, for he cash flow Y. Fgure 4 plos TOPIX and s fundamenal prce, defned by equaon (9). We follow he pracce of Shller (1981) and Boswjk, Hommes, and Manzan (2007) n usng he CPI o deflae he nomnal varables. Snce dvdends are usually pad n May or June n Japan, seasonal cycles are generaed n seres, so we smooh hem ou usng an exponenal movng average as follows: EMA ( α ) Y 1 = EMA α (11) 14

16 TOPIX Fundamenal prce June 00 June 02 July 04 Aug 06 Sep 08 Feb 10 perods Fgure 4: TOPIX and fundamenal prce where we se a consan smoohng parameer a Fgure 4 suggess ha he sock prce ofen devaes from he fundamenal prce bu shows a endency o rever o he fundamenal value. The sock prce has co-movemen wh he fundamenal value whn our sample perods, bu does no perfecly explan he sock prce dynamcs ha are a conssen feaure observed n U.S. daa, popularly n he work of Shller (1981) Techncal ndcaor ln C ln C refers o a echncal ndcaor n equaon (1), whch measures he pas prce rend. We examne wheher professonals look a he pas prce rend n formng her expecaons. Techncal ndcaors used n several agen-based models are based on a one-perod prce change, 15 whle real sock nvesors may use more complcaed and sophscaed rules. Thus, we selec a varey of smple bu slghly sophscaed rules for he rend ndcaor and emprcally 14 Shller (1981) derends he sock prce and fundamenal prce by dvdng by a long-run exponenal growh facor, whle Campbell and Shller (2005) smooh hem usng 10-year movng averages. 15 See, for example, Anufrev and Panchenko (2009). 15

17 deermne whch rule beer fs he echncal ndcaor for each ype of professonal by esmang: F + 1 ln = δ + φ C + ε P ln (12) Here, we consder he followng hree ypes of echncal ndcaors represenng he pas rend: P ln C = ln (13) P m where P s he end-of-monh prce and m represens how many days (m) each ype consders pas prce nformaon n formng he rend ndcaor, and m = 1, 2,, 19, and one monh. 16 We also consder wo rend ndcaors ha reflec prce devaons from he monhly mean prce. The frs one s he devaon of he end-of-monh prce from he mean prce, whch s: P ln C = ln (14) monhly mean prce where P s he end-of-monh prce and he monhly mean prce s calculaed by averagng all daly prces observed for he monh. The oher one ulzes he prce a he begnnng of he monh, whch s gven by: monhly mean prce ln C = ln (15) P where P s he begnnng-of-monh prce 17 and he monhly mean prce s calculaed as n equaon (14). Anoher ype of rend ndcaor measures he monhly prce change by usng he monhly average prce, whch s: P ln C = ln (16) P 1 where P s he monhly mean prce, calculaed by averagng all daly prces observed a. Noe ha our echncal ndcaors a are consruced usng daly daa whn a monh, or hey are 16 On average, 21.7 prces are recorded each monh; he mnmum and maxmum are 20 and 23, respecvely. Thus, f we se m o be equal o or more han 20, ln C may be serally correlaed wh ha of he precedng or followng monh. 17 For equaon (15), we use he prce a he begnnng of he precedng monh from he forecas dae. 16

18 monhly prce changes, o avod he overlappng sample problem. Ths means ha our echncal ndcaors a are ndependen from hose a he mos recen precedng and followng monhs. We have 23 rend varables, and we esmae equaon (12) for each ype of professonal usng hese 23 varables. For each ype, we conduc a unvarae regresson for each of he 23 varables, meanng ha we run a oal of 46 regressons. We use he Newey-Wes conssen sandard error (Newey and Wes, 1987; 1994) o evaluae he sgnfcance of φˆ because, que possbly, he expecaons can also be explaned by oher varables, suggesng ha we observe ceran serally correlaed paerns n he resduals. We selec a rend varable for each ype when he Newey-Wes correced p-value s less han If mulple rend varables are chosen wh hs creron, we randomly selec one of hem. If none of hem fulflls hs creron, we choose he one ha generaes he lowes p-value. Table 1 shows he resul. We hghlgh wo resuls here. Frs, he esmaes of φˆ are all posve, meanng ha buy-sde and sell-sde professonals are rend-followers. Second, boh ypes of professonals look over he pas 1 monh when forecasng he fuure prce. Our resuls sugges ha professonals end o ulze pas prce nformaon n makng her forecass. Ths conradcs he mplcaon Table 1: Parameer esmaes for rend ndcaors Type of professonals Rules seleced φˆ Buy-sde professonals Sell-sde professonals Trend over a monh (mean prce) Trend over a monh (mean prce) p-value < Table 2: Parameer esmaes AIC Buy-sde Professonals Sell-sde professonals Fundamenals 0.42*** 0.39*** Techncal radng 0.25*** 0.20** Inensy of swch BIC Buy-sde Professonals Sell-sde professonals Fundamenals 0.42*** 0.39*** Techncal radng 0.25*** 0.20** Inensy of swch Noe: *, **, and *** denoe sgnfcance a he 10%, 5% and 1% levels, respecvely. 17

19 of he effcen marke hypohess ha we canno accuraely predc fuure prces usng pas prce nformaon because he curren prce already conans all avalable nformaon n he marke. If ha were he case, nobody would use pas prce nformaon o predc fuure prces. Our resuls on predcon beng anchored n some observable prors are conssen wh ceran survey sudes on macroeconomc forecass. For example, Campbell and Sharpe (2009) show ha forecass of macroeconomc varables, such as CPI and ndusral producon, are anchored no only n he prevous monh s release bu also n he average of he hree prevous monhs releases and n Tc, 3.3. Fracons of he fundamenal and echncal radng sraegy ( ) n Tc, The fracon of he fundamenal sraegy ( ) n Tc, 1 s smulaneously deermned wh n,. Thus, he followng llusraes only n,. A he end of each perod, nvesors compare he forecas Tc performances from her fundamenal and echncal radng sraeges, and hey swch her sraeges o he one ha produced he smaller squared forecas error durng he prevous perod. As assumed n several agen-based models, such as Brock and Hommes (1998), we assume ha ype chooses sraeges accordng o: max where ( C +ω fness + ϖ ) fness F, 1 F,, Tc, 1, (17) C F, assumed o be posve, s he cos ha ype pays for acqurng he nformaon on he fundamenal value. ω, and ϖ, are random varables ha are ndependen and exremevalue dsrbued. Then, ype chooses he echncal radng sraegy by he log model probably as follows: n Tc, = exp exp( β fnesstc, ) ( β ( fness C ) + exp( β fness ) F, F Tc, Tc (18) 18 Among several arcles, hose of Aggarwal, Mohany, and Song (1995), Nordhaus (1987), and Schrm (2003) are oher examples. In addon, professonal forecasers of macroeconomc varables refer o older nformaon such as he pas hree monhs n he work of Campbell and Sharpe (2009) han he forecasers n our sample do. Forecased values of macroeconomc varables are ypcally released every monh, whle sock prces are dsclosed o he publc much more frequenly. Thus, professonal forecasers of macroeconomc varables may updae her nformaon se much more slowly han he forecasers of sock prces, and hey may rean he old nformaon n her nformaon se for a whle, suggesng ha macroeconomc forecasers may look a older nformaon for her forecass. 18

20 Parameer β 0 s called he nensy of choce and measures he sensvy of he swch beween fundamenal and echncal radng sraeges. The hgher he nensy of choce, he more rapdly professonals swch her sraeges o he one ha produced beer performance n he prevous perod. The lower β ndcaes ha ype changes hs sraegy only when here s a large dfference n he performance n he wo sraeges. The nensy of choce s nversely relaed o he varance of he nose erms ω, and ϖ,. We measure he fness from boh sraeges n erms of he squared forecas error by: fness fness F, Tc, ( ε ) 2 = (19) F, ( ε ) 2 = (20) Tc, The forecas errors a from he fundamenal sraegy ε F, and echncal radng sraegy ε Tc, for ype are gven by: P P 1 ε = F, ln βf ln (21) P 1 P 1 P P 1 ε = Tc, ln βtc ln (22) P 1 P 2 I s assumed ha professonals evaluae pas performance every monh, meanng ha hey updae he sraeges every perod, hopng o oban beer performances n he fuure Order N n an auoregressve componen Snce our emprcal model ncludes he auoregressve componens of forecased varable ln F + k P, we need o deermne he approprae order N o be esmaed. We ulze he Akake Informaon Creron (AIC) and he Bayesan Informaon Creron (BIC) o deermne he approprae lag lengh. Snce we canno decde whch creron s beer, we leave he model selecon ssue undecded and proceed wh our subsequen analyses usng canddae models seleced by boh crera. Snce we have wo ypes and use wo crera, four canddae models are chosen by he AIC and BIC. The lag lengh seleced by he AIC s lkely o be larger han ha seleced by he BIC, bu he lags seleced by he AIC and BIC are one monh for boh buy-sde and sell-sde professonals. 19

21 4. Evdence of sraegy swchng Ths secon provdes evdence of sraegy swchng n he Japanese sock marke. We frs esmae our emprcal model,.e., equaon (1), by nonlnear leas squares (NLLS). Afer confrmng he sgnfcance of he fundamenal and rend-followng parameers, we plo he fed value of n, (.e., fracon of professonals n ype ulzng he echncal radng sraegy). We Tc valdae he sraegy swchng by examnng wheher he fed value of n, vares by me. Tc We esmae he parameers n equaons (1), such as hose of fundamenal and echncal radng sraeges and nensy of choce, wh approprae lag lenghs seleced ndependenly by AIC and BIC for buy-sde and sell-sde professonals. Thus, we conduc NLLS four mes. Table 2 summarzes he resuls. As seen n Table 2, he parameers of he fundamenal and echncal radng sraeges are sgnfcanly posve for buy-sde and sell-sde professonals n boh of he AIC and BIC models. 19 The resuls ndcae ha, on he one hand, he forecass based on he fundamenal sraegy end o rever o he fundamenal value. On he oher hand, echncal raders are all rend followers. They forecas ha he prce change wll be proporonal o he laes observed change. In cases where he prce has ncreased n he pas, hey expec ha he fuure prce wll go up, and ha wll go down when he prce has decreased. 20 The parameers of he nensy of choce are all posve bu usually no sgnfcan. 21 As explaned by Boswjk, Hommes, and Manzan (2007), he parameer n he ranson funcon s hardly sgnfcan, because large varaons n he nensy of choce cause only small changes n he fracon n,. As emphaszed by Boswjk, Hommes, and Manzan (2007) and Teräsvra Tc 19 In addon, we have conduced wo sascal ess on he resduals: he Jarque-Bera es, where he null hypohess s ha he resduals follow a normal dsrbuon, and he Ljung-Box es, where he null s ha he resduals are no auocorrelaed. For boh ess n he AIC and BIC models, he es sascs are usually n a range of nsgnfcance a he 95% confdence level. The null hypohess n he Jarque-Bera es s no rejeced for buy-sde and sell-sde professonals, whle, n he Ljung-Box es, he resdual auocorrelaons do no exs for boh ypes. Thus, we conclude ha he resduals are normally dsrbued whou auocorrelaons. 20 We seleced echncal ndcaors from only 23 choces n Secon 3.2. In realy, professonals may refer o oher and more complcaed echncal ndcaors. Thus, f we adop oher (and possbly more complcaed) rules for selecng approprae echncal ndcaors, we may selec dfferen rules. However, our resuls ndcae ha hese rules ha we may possbly selec wll also be n a form smlar o ha of rend-followng ndcaors, because he rules we seleced here are sascally sgnfcan. 21 In boh of he AIC and BIC models, he p-values for he esmaes of he nensy of choce are 0.13 for buy-sde professonals and 0.13 for sell-sde professonals. 20

22 (1994), he sgnfcan heerogeney n he esmaed sraeges s more mporan han he nsgnfcance of he esmae of he nensy of choce. Our esmae of he nensy of choce for buy-sde professonals s very large (170.8) compared o ha of nflaon expecaons, as ndcaed n Branch (2004), possbly because buysde professonals generally employ passve nvesmen sraeges, whch are very common n he Japanese sock marke. The passve managemen adoped by he majory of he Japanese nsuonal nvesors, such as nvesors n penson funds and lfe nsurance, s he fund managemen mehod ha ulzes major sock ndces, such as TOPIX, as a benchmark and seeks an nvesmen performance smlar o he reurns from he benchmark. 22,23 Our resuls sugges ha many of he buy-sde professonals employ passve nvesmen sraeges and, hus, adjus her sraeges o he TOPIX prce movemens. Ther nensy of choce s srong, ndcang ha hey quckly and frequenly adjused her sraeges o he very volale movemens of he TOPIX durng our sample perods. In addon, he sell-sde professonals also ndcae srong nensy of choce (185.2), possbly because, as Yamamoo and Hraa (2011) demonsrae, he sell-sde professonals end o ulze buy-sde professonals deas abou fuure prces o ngraae hemselves o her clens, ha s o say, buy-sde professonals. Now we valdae he sraegy swchng by plong he fed value of n, n he upper fgure n Fgure 5. We plo he weghed average of he fracon ha weghs he fracons of buysde and sell-sde professonals by he number of respondens n each ype, denoed as Tc n Tc, Ths. clearly shows a me-varyng feaure. Snce he parameer esmaes of he fundamenal and rend-followng sraeges are sgnfcan for boh ypes, we conclude ha buy-sde and sell-sde professonals adjus her sraeges beween he fundamenal and rend-followng sraeges over me. Whle professonals on average pu more wegh on he rend-followng componen, snce he wegh s larger han 0.5, hey swch her sraeges and pu more wegh on he fundamenal sraegy when has generaed a beer forecas performance n he pas. Fgure 5 suggess ha he sock prce would be relaed o he sraegy swchng by buysde and sell-sde professonals. Snce our parameer esmaes of he echncal radng sraeges 22 As opposed o passve managemen, acve nvesmen looks for beer radng performances han he reurns from he benchmark. 23 See, for example, Ohba (2001), who documens he passve managemen sysems n he Japanese sock marke. 21

23 are posve, s clear ha more professonals end o choose rend-followng sraeges when he prce follows he rend, and herefore, he prce rends are furher nensfed. However, he posve parameer esmae of he fundamenal sraegy ndcaes ha professonals end o predc ha he prce wll rever o he fundamenal value when he devaon of he prce from he fundamenal prce becomes larger. As more professonals choose he fundamenal sraegy, he prce ends o move back o he fundamenal prce. We observe ha he fundamenals sraegy suddenly gans more wegh durng ceran perods. Such swchng behavor would be also relaed o ceran bg marke evens n Japanese markes and n global markes. 24 The examples nclude (1) he Resona shock n May 2003; (2) he UFJ 1 Tme seres of mean weghs June00 June01 June02 June03 June04 June05 June06 June07 June08 June TOPIX Fundamenal prce June00 June01 June02 June03 June04 June05 June06 June07 June08 June09 Fgure 5: Tme seres of mean fracon, TOPIX, and he fundamenal prce 24 The swchng behavor s also observed when he markes go bullsh (for example, summer 2003, summer 2004, and sprng 2005). Shbaa (2011) denfes he mngs of bull and bear markes n he case of Tokyo sock exchange by usng DDMS-ARCH model. 22

24 shock n Augus 2004, whch refers o he merger of he Msubsh Tokyo Fnancal Group and UFJ Holdngs, rggered by he problems of huge, nonperformng loans; (3) he lgaon of he nsder radng charges of he Japanese famous hedge fund, he Murakam fund, n June 2006; (4) he unexpeced sudden resgnaon of Prme Mnser Abe n Sepember 2007; and (5) ceran ssues ha occurred monhs pror o he Lehman shock n Sepember 2008, such as he Bear Searns shock n March 2008 and he Subprme shock n summer The boom fgure n Fgure 5 plos he TOPIX and s fundamenal value and ndcaes he perod of he abovemenoned evens. The Resona shock and he UFJ shock h he Japanese economy n a way ha moved he prce back o he fundamenal prce. Durng hose perods, professonals swched her sraeges o ha of fundamenals. Ths suggess ha hose evens calmed down he Japanese sock marke and made nvesors realze ha he marke would go back o fundamenal prces. When hey swched her sraeges, he prce seemed o rever o he fundamenal value. The oher evens, such as he lgaon of he Murakam fund, he seppng down of Prme Mnser Abe, he Bear Searns shock, and he Subprme shock also appeared o be correlaed o he movemen of n Tc,. As he Japanese economy experenced hose shocks, he nvesors ended o hnk ha he prce would no furher devae from he fundamenal prce. Thus, hey would swch o he fundamenal sraeges, and he prce would move back o he fundamenal prce. 25 Those observaons sugges ha he flucuaons of he fracon of professonals n he marke, ulzng rend-followng sraeges, would be relaed o he devaon of he sock prce from he fundamenal prce, as prevous agen-based models sugges. In he nex secon, we sascally nvesgae hs relaon n he Japanese sock marke. We wll demonsrae ha he sraegy swchng employed by buy-sde and sell-sde professonals acually drves he flucuaons of he Japanese sock marke. 5. Sraegy swchng and marke flucuaons from 2000 o 2010 Sandard agen-based models, such as ha of Brock and Hommes (1998), predc ha he rendfollowng sraegy can be a key facor generang unsable phases n he economy, whle he fundamenal sraegy conrbues o sablzng prce flucuaons. As more agens adop rend- 25 When he Lehman shock h he marke, professonals hough ha he prce would devae from he fundamenal prce. Thus, more professonals ulzed he rend-followng sraeges. Ths ndcaes ha he Lehman shock confused he marke, nensfyng he decrease n he sock prce. 23

25 followng sraeges, he prce moves away from he fundamenal prce and he prce devaons perss. Durng he perod of perssen prce movemens, he rend-followng sraeges produce beer forecas performances, whch resuls n more nvesors choosng he rend-followng sraeges. Thus, he rend-followng sraeges renforce he devaons. When he prce devaes much from he fundamenal prce, agens end o predc he prce reverng o he fundamenal prce. As more agens choose he fundamenal sraegy, he prce goes back o he fundamenal prce. Ths mples ha here s a posve correlaon beween he fracon of professonals n he marke ulzng he rend-followng sraegy, ha s o say, n Tc,, and he prce devaon from he fundamenal prce. 26 The followng nvesgaes he dynamc relaon beween he fracon and he prce devaon from he fundamenal prce. We measure he prce devaon from he fundamenal prce as he absolue value of he log dfference of TOPIX from he fundamenal P prce: abs ln. P We have conduced Augmened Dckey-Fuller ess and found ha he fracon and prce devaons are nonsaonary, wh dfferen lenghs of lags. 27 However, he frs dfferences n P n Tc, and abs ln appear o be saonary. Thus, we esmae a bvarae VAR model o P nvesgae he dynamc relaon by ulzng he frs dfferences of he wo varables. 28 We model our VAR model as follows: n Tc, Y + = V + AY Λ + ApY p U (23) Before esmang he parameers, we deermned he approprae order of he VAR models usng he AIC and BIC, and we found ha he lags seleced by he AIC and BIC are egh 26 Recall ha Tc n, s he weghed average of he fracon usng he numbers of respondens n buy-sde and sellsde professonals as weghs. 27 For example, he ADF es sascs are and for he fracon when he lags are 1 and 2, respecvely. The ADF es sascs for he absolue prce devaon are and when he lags are 1 and 2, respecvely. 28 The ADF es sascs are and for he frs dfference of he fracon when he lags are 1 and 2, respecvely. The ADF es sascs for he frs dfference of he absolue prce devaon are and when he lags are 1 and 2, respecvely. 24

26 and hree monhs, respecvely. where Y Δn, ln ', P Δabs. V s a 2 x 1 vecor of he P = Tc nercep erms, he column number, h s he lag order, and A k are 2 x 2 coeffcen marces wh enres a,, where n s he row, j s he h n j U s a vecor of dsurbances. Table 3: Causaly ess Panel A: Fracon Devaon from he fundamenal prce AIC BIC F-value p-value <0.01 <0.01 Panel B: Devaon from he fundamenal prce Fracon AIC BIC F-value p-value <0.01 <0.01 Table 4: Coeffcen esmaes of VAR models Panel A: Dependen varable: Devaon from he fundamenal prce a Independen varable: Fracon lags AIC 0.161** * BIC 0.164*** Panel B: Dependen varable: Devaon from he fundamenal prce a Independen varable: Devaon from he fundamenal prce a prevous perod lags AIC 0.261** * 0.321** * ** BIC 0.329*** ** 0.256** Noe: *, **, and *** denoe sgnfcance a he 10%, 5% and 1% levels, respecvely. 25

27 We esmae he VAR model wh he approprae lags and conduc Granger causaly ess o nvesgae he mpled causal srucures of he fracon n Tc, and he prce devaons from P he fundamenal prce, abs ln. We frs explan he resuls of he Granger causaly es n P Panels A and B n Table 3. Conssen wh earler research on agen-based heores, we fnd a sgnfcan nfluence of he fracon on he prce devaon from he fundamenal value for boh he AIC and BIC models. In addon, Panel B n Table 3 demonsraes sgnfcan causaly from he prce devaons o he fracon for boh he AIC and BIC models as well. The parameer esmaes are summarzed n Panels A and B n Table 4. In Panel A n Table 4, we fnd ha n boh of he AIC and BIC models, swchng sraeges o rend-followng sraeges by buy-sde and sell-sde professonals causes he prce o devae from he fundamenal prce (.e., posve coeffcen esmaes a a lag of one monh). When more professonals selec rend-followng sraeges, he prce ends o devae from he fundamenal value. The prce revers o he fundamenal value when more professonals choose he fundamenal sraeges. Panel B n Table 4 shows ha he coeffcen esmaes of he prce devaon are usually sgnfcan a a one-monh lag, meanng ha he prce devaons from he fundamenals are perssen over a monh, whch fs our observaon on he real daa. 6. Concluson Ths paper has demonsraed ha he buy-sde and sell-sde professonals n he Japanese sock marke ulze boh fundamenal and rend-followng sraeges n her forecasng and ha hey swch sraeges over me. We have demonsraed ha sraegy swchng by buy-sde and sellsde professonals has a sgnfcan mpac on he TOPIX prce devaons from he fundamenal value. Our fndngs help o valdae sraegy swchng as well as s nfluences on he perssen devaons of he prce from he fundamenals, whch are mporan resuls n sandard agenbased models, such as ha of Brock and Hommes (1998). Fnally, we conclude our dscusson wh ceran possble exensons of our research. We have relaed he sock prce forecas seres o he sock prce dynamcs. Therefore, our resuls sugges ha he sock prce forecas seres can possbly be ulzed o denfy he shape of he reurn dsrbuon. Snce praconers calculae he probably of large and small prce movemens from he al of he reurn dsrbuon, he hckness of he al ndcaes mporan 26

28 nformaon for beer rsk managemen. Therefore, he forecas seres can serve o provde a beer undersandng of he sources of rsk n sock markes. One possble exenson of our work nvolves relang he forecass o he probably of he large sock prce movemens, and s he subjec of our fuure work. Acknowledgemens The auhors are graeful o Hdeaka Kawaka, Naok Kshmoo, Blake LeBaron, Hdeosh Ohash, Carol Osler, and Sefan Rez, who provded useful suggesons and feedback on an earler draf. Ths paper has also benefed from commens by parcpans a Brandes Unversy, Chuo Unversy, Hose Unversy, 5h annual meeng of Assocaon of Behavoral Economcs and Fnance (Hyogo, Japan), and he 17h Inernaonal conference on compung n economcs and fnance (CEF 2011) (San Francsco). Ryuch Yamamoo graefully acknowledges fnancal suppors from he Naonal Scence Councl (Gran No. NSC H MY2). Hdeak Hraa graefully acknowledges fnancal suppors from Japan Cener for Economc Research and he Insue of Comparave Economc Sudes, Hose Unversy. References Aggarwal, R., S. Mohany, and F. Song Are Survey Forecass of Macroeconomc Varables Raonal? Journal of Busness, 68, Anufrev, M., V. Panchenko Asse prces, raders behavor and marke desgn. Journal of Economc Dynamcs and Conrol 33, Boswjk, H.P., Hommes, C.H. and Manzan, S., Behavoral heerogeney n sock prces, Journal of Economc Dynamcs and Conrol 31, Branch, W.A The heory of raonally heerogeneous expecaons: evdence from survey daa on nflaon expecaons, Economc Journal 114, Brock, W.A., Hommes, C.H., Heerogeneous belefs and roues o chaos n a smple asse prcng model. Journal of Economc Dynamcs and Conrol 22, Brown, G., M. Clff, Invesor senmen and he near-erm sock marke. Journal of Emprcal Fnance 11, Brown, G., Clff, M Invesor senmen and asse valuaon. Journal of Busness 78,

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